DATA 607 Project 3
Puja Roy, Chhiring Lama, William Berritt
2024-03-18
Introduction
For this project, the aim is to obtain data to answer the question, “Which are the most valued data science skills?”
We obtained the Data Science Job Salaries dataset from Kaggle: https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries?resource=download which contains information about salaries of jobs in the Data Science domain. The dataset includes work year, company size, job title, salary in USD, employee residence and company location.
We decided to focus on job titles that included salary in the USD and worked with variables including the work year, company size and company location.
Load the data
We stored the data in MySQL workbench and azure database, joined the tables that contained data of company and job and then queried it using R.
## [1] "chess_tournament" "clean_company_ids" "clean_job_dets"
## [4] "company" "job" "movie_ratings"# Retrieve data from a specific table
df1 <- dbGetQuery(mydb,'select * from company')
# Print the retrieved data
print(df1)## company_id employee_residence remote_ratio company_location company_size
## 1 1 DE 0 DE L
## 2 2 JP 0 JP S
## 3 3 GB 50 GB M
## 4 4 HN 0 HN S
## 5 5 US 50 US L
## 6 6 US 100 US L
## 7 7 US 100 US S
## 8 8 HU 50 HU L
## 9 9 NZ 50 NZ S
## 10 10 FR 0 FR S
## 11 11 IN 0 IN L
## 12 12 FR 0 FR M
## 13 13 PK 50 PK L
## 14 14 JP 100 JP S
## 15 15 PL 100 GB S
## 16 16 IN 50 IN M
## 17 17 PT 100 US M
## 18 18 CN 0 CN M
## 19 19 IN 100 IN L
## 20 20 GR 50 GR L
## 21 21 US 0 US M
## 22 22 AE 0 AE L
## 23 23 NL 50 NL L
## 24 24 MX 0 MX S
## 25 25 CA 50 CA L
## 26 26 DE 100 DE S
## 27 27 GR 100 US L
## 28 28 FR 50 FR L
## 29 29 AT 50 AT L
## 30 30 NG 100 NG S
## 31 31 PH 100 US S
## 32 32 ES 50 ES L
## 33 33 PT 0 PT M
## 34 34 GB 50 GB L
## 35 35 GB 100 GB S
## 36 36 IN 0 IN S
## 37 37 DK 50 DK S
## 38 38 PK 100 DE L
## 39 39 RU 100 US S
## 40 40 DE 100 DE M
## 41 41 ES 100 ES M
## 42 42 US 100 US M
## 43 43 ES 100 US M
## 44 44 IT 50 IT S
## 45 45 HR 100 HR S
## 46 46 DE 50 DE S
## 47 47 DE 0 DE S
## 48 48 AT 0 AT S
## 49 49 FR 50 LU S
## 50 50 FR 50 FR S
## 51 51 IN 100 US L
## 52 52 DE 100 DE L
## 53 53 GB 50 CA L
## 54 54 ES 100 ES S
## 55 55 IT 100 PL L
## 56 56 PL 100 PL L
## 57 57 FR 50 FR M
## 58 58 BG 100 US S
## 59 59 GR 100 DK L
## 60 60 US 0 US L
## 61 61 IN 50 IN L
## 62 62 SG 50 SG L
## 63 63 BR 100 US S
## 64 64 DE 100 US S
## 65 65 HU 50 US L
## 66 66 CA 100 CA L
## 67 67 DE 50 DE L
## 68 68 GB 100 GB M
## 69 69 PK 100 US M
## 70 70 NL 100 NL L
## 71 71 NG 100 NG L
## 72 72 GR 100 GR M
## 73 73 ES 50 RO M
## 74 74 GB 0 GB L
## 75 75 ES 50 ES M
## 76 76 US 50 US S
## 77 77 IN 100 IN S
## 78 78 IQ 50 IQ S
## 79 79 VN 100 US M
## 80 80 BR 100 BR M
## 81 81 US 0 US S
## 82 82 JP 50 JP S
## 83 83 BE 50 BE M
## 84 84 UA 100 UA L
## 85 85 SG 100 IL M
## 86 86 RU 50 RU L
## 87 87 RU 0 RU M
## 88 88 MT 50 MT L
## 89 89 DE 50 DE M
## 90 90 GB 100 GB L
## 91 91 PT 50 PT L
## 92 92 CL 100 CL L
## 93 93 IN 100 US S
## 94 94 RO 0 US L
## 95 95 PK 50 PK M
## 96 96 IR 100 IR M
## 97 97 VN 100 GB M
## 98 98 FR 100 ES S
## 99 99 RO 50 GB S
## 100 100 US 100 FR L
## 101 101 CO 50 CO M
## 102 102 MD 0 MD S
## 103 103 KE 100 KE S
## 104 104 IN 50 US L
## 105 105 DE 50 AT M
## 106 106 ES 100 ES L
## 107 107 FR 100 US S
## 108 108 BR 0 BR S
## 109 109 IT 0 US L
## 110 110 SI 50 SI L
## 111 111 HK 50 GB S
## 112 112 IN 0 CH L
## 113 113 BE 100 BE M
## 114 114 IN 0 IN M
## 115 115 US 100 CA L
## 116 116 CA 100 CA S
## 117 117 CA 100 CA M
## 118 118 VN 0 VN M
## 119 119 IN 100 AS S
## 120 120 TR 0 TR M
## 121 121 IN 100 IN M
## 122 122 CA 50 CA M
## 123 123 RS 100 DE S
## 124 124 PR 50 US S
## 125 125 TR 100 TR M
## 126 126 BR 0 BR M
## 127 127 NL 100 DE S
## 128 128 TR 50 TR L
## 129 129 LU 100 LU L
## 130 130 JE 0 CN L
## 131 131 CZ 50 CZ L
## 132 132 SI 100 SI L
## 133 133 IT 50 IT L
## 134 134 GB 0 GB M
## 135 135 GR 0 GR M
## 136 136 FR 100 DE M
## 137 137 AR 100 MX L
## 138 138 PT 100 PT L
## 139 139 DE 0 DE M
## 140 140 AE 100 AE S
## 141 141 DZ 50 DZ M
## 142 142 CA 100 US M
## 143 143 TN 100 CZ M
## 144 144 MY 100 US M
## 145 145 BR 100 US M
## 146 146 US 50 US M
## 147 147 PK 100 DE M
## 148 148 EE 100 EE S
## 149 149 JP 100 MY L
## 150 150 AU 100 AU L
## 151 151 AU 50 AU M
## 152 152 BO 100 US L
## 153 153 AT 0 AT L
## 154 154 AU 100 AU S
## 155 155 IE 100 IE S
## 156 156 PK 0 PK M
## 157 157 FR 100 FR M
## 158 158 CH 0 CH L
## 159 159 PT 100 LU M
## 160 160 GR 100 GR S
## 161 161 CA 0 CA M
## cid
## 1 DE_0_DE_L
## 2 JP_0_JP_S
## 3 GB_50_GB_M
## 4 HN_0_HN_S
## 5 US_50_US_L
## 6 US_100_US_L
## 7 US_100_US_S
## 8 HU_50_HU_L
## 9 NZ_50_NZ_S
## 10 FR_0_FR_S
## 11 IN_0_IN_L
## 12 FR_0_FR_M
## 13 PK_50_PK_L
## 14 JP_100_JP_S
## 15 PL_100_GB_S
## 16 IN_50_IN_M
## 17 PT_100_US_M
## 18 CN_0_CN_M
## 19 IN_100_IN_L
## 20 GR_50_GR_L
## 21 US_0_US_M
## 22 AE_0_AE_L
## 23 NL_50_NL_L
## 24 MX_0_MX_S
## 25 CA_50_CA_L
## 26 DE_100_DE_S
## 27 GR_100_US_L
## 28 FR_50_FR_L
## 29 AT_50_AT_L
## 30 NG_100_NG_S
## 31 PH_100_US_S
## 32 ES_50_ES_L
## 33 PT_0_PT_M
## 34 GB_50_GB_L
## 35 GB_100_GB_S
## 36 IN_0_IN_S
## 37 DK_50_DK_S
## 38 PK_100_DE_L
## 39 RU_100_US_S
## 40 DE_100_DE_M
## 41 ES_100_ES_M
## 42 US_100_US_M
## 43 ES_100_US_M
## 44 IT_50_IT_S
## 45 HR_100_HR_S
## 46 DE_50_DE_S
## 47 DE_0_DE_S
## 48 AT_0_AT_S
## 49 FR_50_LU_S
## 50 FR_50_FR_S
## 51 IN_100_US_L
## 52 DE_100_DE_L
## 53 GB_50_CA_L
## 54 ES_100_ES_S
## 55 IT_100_PL_L
## 56 PL_100_PL_L
## 57 FR_50_FR_M
## 58 BG_100_US_S
## 59 GR_100_DK_L
## 60 US_0_US_L
## 61 IN_50_IN_L
## 62 SG_50_SG_L
## 63 BR_100_US_S
## 64 DE_100_US_S
## 65 HU_50_US_L
## 66 CA_100_CA_L
## 67 DE_50_DE_L
## 68 GB_100_GB_M
## 69 PK_100_US_M
## 70 NL_100_NL_L
## 71 NG_100_NG_L
## 72 GR_100_GR_M
## 73 ES_50_RO_M
## 74 GB_0_GB_L
## 75 ES_50_ES_M
## 76 US_50_US_S
## 77 IN_100_IN_S
## 78 IQ_50_IQ_S
## 79 VN_100_US_M
## 80 BR_100_BR_M
## 81 US_0_US_S
## 82 JP_50_JP_S
## 83 BE_50_BE_M
## 84 UA_100_UA_L
## 85 SG_100_IL_M
## 86 RU_50_RU_L
## 87 RU_0_RU_M
## 88 MT_50_MT_L
## 89 DE_50_DE_M
## 90 GB_100_GB_L
## 91 PT_50_PT_L
## 92 CL_100_CL_L
## 93 IN_100_US_S
## 94 RO_0_US_L
## 95 PK_50_PK_M
## 96 IR_100_IR_M
## 97 VN_100_GB_M
## 98 FR_100_ES_S
## 99 RO_50_GB_S
## 100 US_100_FR_L
## 101 CO_50_CO_M
## 102 MD_0_MD_S
## 103 KE_100_KE_S
## 104 IN_50_US_L
## 105 DE_50_AT_M
## 106 ES_100_ES_L
## 107 FR_100_US_S
## 108 BR_0_BR_S
## 109 IT_0_US_L
## 110 SI_50_SI_L
## 111 HK_50_GB_S
## 112 IN_0_CH_L
## 113 BE_100_BE_M
## 114 IN_0_IN_M
## 115 US_100_CA_L
## 116 CA_100_CA_S
## 117 CA_100_CA_M
## 118 VN_0_VN_M
## 119 IN_100_AS_S
## 120 TR_0_TR_M
## 121 IN_100_IN_M
## 122 CA_50_CA_M
## 123 RS_100_DE_S
## 124 PR_50_US_S
## 125 TR_100_TR_M
## 126 BR_0_BR_M
## 127 NL_100_DE_S
## 128 TR_50_TR_L
## 129 LU_100_LU_L
## 130 JE_0_CN_L
## 131 CZ_50_CZ_L
## 132 SI_100_SI_L
## 133 IT_50_IT_L
## 134 GB_0_GB_M
## 135 GR_0_GR_M
## 136 FR_100_DE_M
## 137 AR_100_MX_L
## 138 PT_100_PT_L
## 139 DE_0_DE_M
## 140 AE_100_AE_S
## 141 DZ_50_DZ_M
## 142 CA_100_US_M
## 143 TN_100_CZ_M
## 144 MY_100_US_M
## 145 BR_100_US_M
## 146 US_50_US_M
## 147 PK_100_DE_M
## 148 EE_100_EE_S
## 149 JP_100_MY_L
## 150 AU_100_AU_L
## 151 AU_50_AU_M
## 152 BO_100_US_L
## 153 AT_0_AT_L
## 154 AU_100_AU_S
## 155 IE_100_IE_S
## 156 PK_0_PK_M
## 157 FR_100_FR_M
## 158 CH_0_CH_L
## 159 PT_100_LU_M
## 160 GR_100_GR_S
## 161 CA_0_CA_MJoin the Tables to obtain the full data
job <- dbGetQuery(mydb,'select * from job')
company <- dbGetQuery(mydb,'select * from company')
total_df <- left_join(company, job, by='cid')
print(total_df)## company_id employee_residence remote_ratio company_location company_size
## 1 1 DE 0 DE L
## 2 1 DE 0 DE L
## 3 1 DE 0 DE L
## 4 2 JP 0 JP S
## 5 2 JP 0 JP S
## 6 3 GB 50 GB M
## 7 4 HN 0 HN S
## 8 5 US 50 US L
## 9 5 US 50 US L
## 10 5 US 50 US L
## 11 5 US 50 US L
## 12 5 US 50 US L
## 13 5 US 50 US L
## 14 5 US 50 US L
## 15 5 US 50 US L
## 16 5 US 50 US L
## 17 5 US 50 US L
## 18 5 US 50 US L
## 19 5 US 50 US L
## 20 5 US 50 US L
## 21 6 US 100 US L
## 22 6 US 100 US L
## 23 6 US 100 US L
## 24 6 US 100 US L
## 25 6 US 100 US L
## 26 6 US 100 US L
## 27 6 US 100 US L
## 28 6 US 100 US L
## 29 6 US 100 US L
## 30 6 US 100 US L
## 31 6 US 100 US L
## 32 6 US 100 US L
## 33 6 US 100 US L
## 34 6 US 100 US L
## 35 6 US 100 US L
## 36 6 US 100 US L
## 37 6 US 100 US L
## 38 6 US 100 US L
## 39 6 US 100 US L
## 40 6 US 100 US L
## 41 6 US 100 US L
## 42 6 US 100 US L
## 43 6 US 100 US L
## 44 6 US 100 US L
## 45 6 US 100 US L
## 46 6 US 100 US L
## 47 6 US 100 US L
## 48 6 US 100 US L
## 49 6 US 100 US L
## 50 6 US 100 US L
## 51 6 US 100 US L
## 52 6 US 100 US L
## 53 6 US 100 US L
## 54 6 US 100 US L
## 55 6 US 100 US L
## 56 6 US 100 US L
## 57 6 US 100 US L
## 58 6 US 100 US L
## 59 6 US 100 US L
## 60 6 US 100 US L
## 61 6 US 100 US L
## 62 6 US 100 US L
## 63 6 US 100 US L
## 64 6 US 100 US L
## 65 6 US 100 US L
## 66 6 US 100 US L
## 67 6 US 100 US L
## 68 6 US 100 US L
## 69 6 US 100 US L
## 70 6 US 100 US L
## 71 6 US 100 US L
## 72 6 US 100 US L
## 73 6 US 100 US L
## 74 6 US 100 US L
## 75 6 US 100 US L
## 76 6 US 100 US L
## 77 6 US 100 US L
## 78 6 US 100 US L
## 79 6 US 100 US L
## 80 6 US 100 US L
## 81 6 US 100 US L
## 82 6 US 100 US L
## 83 6 US 100 US L
## 84 6 US 100 US L
## 85 7 US 100 US S
## 86 7 US 100 US S
## 87 7 US 100 US S
## 88 7 US 100 US S
## 89 7 US 100 US S
## 90 7 US 100 US S
## 91 7 US 100 US S
## 92 7 US 100 US S
## 93 7 US 100 US S
## 94 7 US 100 US S
## 95 7 US 100 US S
## 96 7 US 100 US S
## 97 7 US 100 US S
## 98 7 US 100 US S
## 99 8 HU 50 HU L
## 100 9 NZ 50 NZ S
## 101 10 FR 0 FR S
## 102 11 IN 0 IN L
## 103 11 IN 0 IN L
## 104 11 IN 0 IN L
## 105 12 FR 0 FR M
## 106 13 PK 50 PK L
## 107 14 JP 100 JP S
## 108 15 PL 100 GB S
## 109 16 IN 50 IN M
## 110 16 IN 50 IN M
## 111 16 IN 50 IN M
## 112 17 PT 100 US M
## 113 18 CN 0 CN M
## 114 19 IN 100 IN L
## 115 19 IN 100 IN L
## 116 19 IN 100 IN L
## 117 19 IN 100 IN L
## 118 19 IN 100 IN L
## 119 19 IN 100 IN L
## 120 20 GR 50 GR L
## 121 21 US 0 US M
## 122 21 US 0 US M
## 123 21 US 0 US M
## 124 21 US 0 US M
## 125 21 US 0 US M
## 126 21 US 0 US M
## 127 21 US 0 US M
## 128 21 US 0 US M
## 129 21 US 0 US M
## 130 21 US 0 US M
## 131 21 US 0 US M
## 132 21 US 0 US M
## 133 21 US 0 US M
## 134 21 US 0 US M
## 135 21 US 0 US M
## 136 21 US 0 US M
## 137 21 US 0 US M
## 138 21 US 0 US M
## 139 21 US 0 US M
## 140 21 US 0 US M
## 141 21 US 0 US M
## 142 21 US 0 US M
## 143 21 US 0 US M
## 144 21 US 0 US M
## 145 21 US 0 US M
## 146 21 US 0 US M
## 147 21 US 0 US M
## 148 21 US 0 US M
## 149 21 US 0 US M
## 150 21 US 0 US M
## 151 21 US 0 US M
## 152 21 US 0 US M
## 153 21 US 0 US M
## 154 21 US 0 US M
## 155 21 US 0 US M
## 156 21 US 0 US M
## 157 21 US 0 US M
## 158 21 US 0 US M
## 159 21 US 0 US M
## 160 21 US 0 US M
## 161 21 US 0 US M
## 162 21 US 0 US M
## 163 22 AE 0 AE L
## 164 23 NL 50 NL L
## 165 24 MX 0 MX S
## 166 24 MX 0 MX S
## 167 25 CA 50 CA L
## 168 25 CA 50 CA L
## 169 25 CA 50 CA L
## 170 25 CA 50 CA L
## 171 25 CA 50 CA L
## 172 26 DE 100 DE S
## 173 26 DE 100 DE S
## 174 26 DE 100 DE S
## 175 27 GR 100 US L
## 176 28 FR 50 FR L
## 177 28 FR 50 FR L
## 178 28 FR 50 FR L
## 179 28 FR 50 FR L
## 180 28 FR 50 FR L
## 181 29 AT 50 AT L
## 182 30 NG 100 NG S
## 183 31 PH 100 US S
## 184 32 ES 50 ES L
## 185 33 PT 0 PT M
## 186 34 GB 50 GB L
## 187 34 GB 50 GB L
## 188 34 GB 50 GB L
## 189 34 GB 50 GB L
## 190 34 GB 50 GB L
## 191 34 GB 50 GB L
## 192 34 GB 50 GB L
## 193 34 GB 50 GB L
## 194 35 GB 100 GB S
## 195 35 GB 100 GB S
## 196 36 IN 0 IN S
## 197 36 IN 0 IN S
## 198 37 DK 50 DK S
## 199 37 DK 50 DK S
## 200 38 PK 100 DE L
## 201 39 RU 100 US S
## 202 39 RU 100 US S
## 203 40 DE 100 DE M
## 204 40 DE 100 DE M
## 205 40 DE 100 DE M
## 206 41 ES 100 ES M
## 207 41 ES 100 ES M
## 208 41 ES 100 ES M
## 209 41 ES 100 ES M
## 210 41 ES 100 ES M
## 211 41 ES 100 ES M
## 212 41 ES 100 ES M
## 213 41 ES 100 ES M
## 214 42 US 100 US M
## 215 42 US 100 US M
## 216 42 US 100 US M
## 217 42 US 100 US M
## 218 42 US 100 US M
## 219 42 US 100 US M
## 220 42 US 100 US M
## 221 42 US 100 US M
## 222 42 US 100 US M
## 223 42 US 100 US M
## 224 42 US 100 US M
## 225 42 US 100 US M
## 226 42 US 100 US M
## 227 42 US 100 US M
## 228 42 US 100 US M
## 229 42 US 100 US M
## 230 42 US 100 US M
## 231 42 US 100 US M
## 232 42 US 100 US M
## 233 42 US 100 US M
## 234 42 US 100 US M
## 235 42 US 100 US M
## 236 42 US 100 US M
## 237 42 US 100 US M
## 238 42 US 100 US M
## 239 42 US 100 US M
## 240 42 US 100 US M
## 241 42 US 100 US M
## 242 42 US 100 US M
## 243 42 US 100 US M
## 244 42 US 100 US M
## 245 42 US 100 US M
## 246 42 US 100 US M
## 247 42 US 100 US M
## 248 42 US 100 US M
## 249 42 US 100 US M
## 250 42 US 100 US M
## 251 42 US 100 US M
## 252 42 US 100 US M
## 253 42 US 100 US M
## 254 42 US 100 US M
## 255 42 US 100 US M
## 256 42 US 100 US M
## 257 42 US 100 US M
## 258 42 US 100 US M
## 259 42 US 100 US M
## 260 42 US 100 US M
## 261 42 US 100 US M
## 262 42 US 100 US M
## 263 42 US 100 US M
## 264 42 US 100 US M
## 265 42 US 100 US M
## 266 42 US 100 US M
## 267 42 US 100 US M
## 268 42 US 100 US M
## 269 42 US 100 US M
## 270 42 US 100 US M
## 271 42 US 100 US M
## 272 42 US 100 US M
## 273 42 US 100 US M
## 274 42 US 100 US M
## 275 42 US 100 US M
## 276 42 US 100 US M
## 277 42 US 100 US M
## 278 42 US 100 US M
## 279 42 US 100 US M
## 280 42 US 100 US M
## 281 42 US 100 US M
## 282 42 US 100 US M
## 283 42 US 100 US M
## 284 42 US 100 US M
## 285 42 US 100 US M
## 286 42 US 100 US M
## 287 42 US 100 US M
## 288 42 US 100 US M
## 289 42 US 100 US M
## 290 42 US 100 US M
## 291 42 US 100 US M
## 292 42 US 100 US M
## 293 42 US 100 US M
## 294 42 US 100 US M
## 295 42 US 100 US M
## 296 42 US 100 US M
## 297 42 US 100 US M
## 298 42 US 100 US M
## 299 42 US 100 US M
## 300 42 US 100 US M
## 301 42 US 100 US M
## 302 42 US 100 US M
## 303 42 US 100 US M
## 304 42 US 100 US M
## 305 42 US 100 US M
## 306 42 US 100 US M
## 307 42 US 100 US M
## 308 42 US 100 US M
## 309 42 US 100 US M
## 310 42 US 100 US M
## 311 42 US 100 US M
## 312 42 US 100 US M
## 313 42 US 100 US M
## 314 42 US 100 US M
## 315 42 US 100 US M
## 316 42 US 100 US M
## 317 42 US 100 US M
## 318 42 US 100 US M
## 319 42 US 100 US M
## 320 42 US 100 US M
## 321 42 US 100 US M
## 322 42 US 100 US M
## 323 42 US 100 US M
## 324 42 US 100 US M
## 325 42 US 100 US M
## 326 42 US 100 US M
## 327 42 US 100 US M
## 328 42 US 100 US M
## 329 42 US 100 US M
## 330 42 US 100 US M
## 331 42 US 100 US M
## 332 42 US 100 US M
## 333 42 US 100 US M
## 334 42 US 100 US M
## 335 42 US 100 US M
## 336 42 US 100 US M
## 337 42 US 100 US M
## 338 42 US 100 US M
## 339 42 US 100 US M
## 340 42 US 100 US M
## 341 42 US 100 US M
## 342 42 US 100 US M
## 343 42 US 100 US M
## 344 42 US 100 US M
## 345 42 US 100 US M
## 346 42 US 100 US M
## 347 42 US 100 US M
## 348 42 US 100 US M
## 349 42 US 100 US M
## 350 43 ES 100 US M
## 351 44 IT 50 IT S
## 352 45 HR 100 HR S
## 353 46 DE 50 DE S
## 354 47 DE 0 DE S
## 355 48 AT 0 AT S
## 356 49 FR 50 LU S
## 357 50 FR 50 FR S
## 358 50 FR 50 FR S
## 359 51 IN 100 US L
## 360 51 IN 100 US L
## 361 52 DE 100 DE L
## 362 53 GB 50 CA L
## 363 54 ES 100 ES S
## 364 55 IT 100 PL L
## 365 56 PL 100 PL L
## 366 56 PL 100 PL L
## 367 56 PL 100 PL L
## 368 57 FR 50 FR M
## 369 57 FR 50 FR M
## 370 57 FR 50 FR M
## 371 57 FR 50 FR M
## 372 58 BG 100 US S
## 373 59 GR 100 DK L
## 374 60 US 0 US L
## 375 60 US 0 US L
## 376 60 US 0 US L
## 377 60 US 0 US L
## 378 60 US 0 US L
## 379 60 US 0 US L
## 380 60 US 0 US L
## 381 60 US 0 US L
## 382 60 US 0 US L
## 383 60 US 0 US L
## 384 60 US 0 US L
## 385 60 US 0 US L
## 386 60 US 0 US L
## 387 60 US 0 US L
## 388 60 US 0 US L
## 389 60 US 0 US L
## 390 60 US 0 US L
## 391 61 IN 50 IN L
## 392 61 IN 50 IN L
## 393 61 IN 50 IN L
## 394 61 IN 50 IN L
## 395 62 SG 50 SG L
## 396 63 BR 100 US S
## 397 63 BR 100 US S
## 398 64 DE 100 US S
## 399 65 HU 50 US L
## 400 66 CA 100 CA L
## 401 66 CA 100 CA L
## 402 66 CA 100 CA L
## 403 66 CA 100 CA L
## 404 67 DE 50 DE L
## 405 67 DE 50 DE L
## 406 67 DE 50 DE L
## 407 67 DE 50 DE L
## 408 67 DE 50 DE L
## 409 67 DE 50 DE L
## 410 68 GB 100 GB M
## 411 68 GB 100 GB M
## 412 68 GB 100 GB M
## 413 68 GB 100 GB M
## 414 68 GB 100 GB M
## 415 68 GB 100 GB M
## 416 68 GB 100 GB M
## 417 68 GB 100 GB M
## 418 68 GB 100 GB M
## 419 68 GB 100 GB M
## 420 69 PK 100 US M
## 421 70 NL 100 NL L
## 422 70 NL 100 NL L
## 423 70 NL 100 NL L
## 424 71 NG 100 NG L
## 425 72 GR 100 GR M
## 426 72 GR 100 GR M
## 427 72 GR 100 GR M
## 428 72 GR 100 GR M
## 429 72 GR 100 GR M
## 430 72 GR 100 GR M
## 431 72 GR 100 GR M
## 432 73 ES 50 RO M
## 433 74 GB 0 GB L
## 434 75 ES 50 ES M
## 435 76 US 50 US S
## 436 76 US 50 US S
## 437 76 US 50 US S
## 438 77 IN 100 IN S
## 439 77 IN 100 IN S
## 440 78 IQ 50 IQ S
## 441 79 VN 100 US M
## 442 80 BR 100 BR M
## 443 81 US 0 US S
## 444 81 US 0 US S
## 445 81 US 0 US S
## 446 82 JP 50 JP S
## 447 82 JP 50 JP S
## 448 82 JP 50 JP S
## 449 83 BE 50 BE M
## 450 84 UA 100 UA L
## 451 85 SG 100 IL M
## 452 86 RU 50 RU L
## 453 87 RU 0 RU M
## 454 88 MT 50 MT L
## 455 89 DE 50 DE M
## 456 89 DE 50 DE M
## 457 89 DE 50 DE M
## 458 90 GB 100 GB L
## 459 90 GB 100 GB L
## 460 91 PT 50 PT L
## 461 91 PT 50 PT L
## 462 92 CL 100 CL L
## 463 93 IN 100 US S
## 464 94 RO 0 US L
## 465 95 PK 50 PK M
## 466 96 IR 100 IR M
## 467 97 VN 100 GB M
## 468 98 FR 100 ES S
## 469 99 RO 50 GB S
## 470 100 US 100 FR L
## 471 101 CO 50 CO M
## 472 102 MD 0 MD S
## 473 103 KE 100 KE S
## 474 104 IN 50 US L
## 475 105 DE 50 AT M
## 476 106 ES 100 ES L
## 477 106 ES 100 ES L
## 478 107 FR 100 US S
## 479 108 BR 0 BR S
## 480 109 IT 0 US L
## 481 110 SI 50 SI L
## 482 111 HK 50 GB S
## 483 112 IN 0 CH L
## 484 113 BE 100 BE M
## 485 114 IN 0 IN M
## 486 115 US 100 CA L
## 487 116 CA 100 CA S
## 488 116 CA 100 CA S
## 489 116 CA 100 CA S
## 490 117 CA 100 CA M
## 491 117 CA 100 CA M
## 492 117 CA 100 CA M
## 493 117 CA 100 CA M
## 494 117 CA 100 CA M
## 495 117 CA 100 CA M
## 496 117 CA 100 CA M
## 497 117 CA 100 CA M
## 498 117 CA 100 CA M
## 499 118 VN 0 VN M
## 500 119 IN 100 AS S
## 501 120 TR 0 TR M
## 502 121 IN 100 IN M
## 503 121 IN 100 IN M
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## cid job_title_id work_year experience_level employment_type
## 1 DE_0_DE_L 1 2020 MI FT
## 2 DE_0_DE_L 258 2021 EX FT
## 3 DE_0_DE_L 271 2021 EN FT
## 4 JP_0_JP_S 2 2020 SE FT
## 5 JP_0_JP_S 151 2021 SE FT
## 6 GB_50_GB_M 3 2020 SE FT
## 7 HN_0_HN_S 4 2020 MI FT
## 8 US_50_US_L 5 2020 SE FT
## 9 US_50_US_L 38 2020 EN FT
## 10 US_50_US_L 59 2020 SE FT
## 11 US_50_US_L 158 2021 MI FT
## 12 US_50_US_L 196 2021 MI FT
## 13 US_50_US_L 250 2021 MI FT
## 14 US_50_US_L 257 2021 SE FT
## 15 US_50_US_L 259 2021 MI FT
## 16 US_50_US_L 275 2021 EN FT
## 17 US_50_US_L 286 2021 MI FT
## 18 US_50_US_L 436 2022 MI FT
## 19 US_50_US_L 442 2022 EN FT
## 20 US_50_US_L 447 2022 SE FT
## 21 US_100_US_L 6 2020 EN FT
## 22 US_100_US_L 9 2020 MI FT
## 23 US_100_US_L 14 2020 MI FT
## 24 US_100_US_L 15 2020 MI FT
## 25 US_100_US_L 26 2020 EX FT
## 26 US_100_US_L 29 2020 EN CT
## 27 US_100_US_L 32 2020 EN FT
## 28 US_100_US_L 37 2020 MI FT
## 29 US_100_US_L 44 2020 MI FT
## 30 US_100_US_L 48 2020 SE FT
## 31 US_100_US_L 49 2020 MI FT
## 32 US_100_US_L 52 2020 EN FT
## 33 US_100_US_L 61 2020 MI FT
## 34 US_100_US_L 64 2020 SE FT
## 35 US_100_US_L 75 2021 EX FT
## 36 US_100_US_L 79 2021 MI CT
## 37 US_100_US_L 82 2021 MI FT
## 38 US_100_US_L 88 2021 MI FT
## 39 US_100_US_L 89 2021 SE FT
## 40 US_100_US_L 98 2021 MI FT
## 41 US_100_US_L 102 2021 SE FT
## 42 US_100_US_L 116 2021 EN FT
## 43 US_100_US_L 120 2021 MI FT
## 44 US_100_US_L 126 2021 MI FT
## 45 US_100_US_L 141 2021 MI FT
## 46 US_100_US_L 145 2021 MI FT
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## 48 US_100_US_L 149 2021 SE FT
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## 51 US_100_US_L 167 2021 EN FT
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## 68 US_100_US_L 254 2021 MI FT
## 69 US_100_US_L 269 2021 EN FT
## 70 US_100_US_L 279 2021 MI FT
## 71 US_100_US_L 385 2022 SE FT
## 72 US_100_US_L 386 2022 SE FT
## 73 US_100_US_L 387 2022 SE FT
## 74 US_100_US_L 388 2022 SE FT
## 75 US_100_US_L 393 2022 SE FT
## 76 US_100_US_L 405 2022 MI FT
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## 78 US_100_US_L 412 2022 SE FT
## 79 US_100_US_L 488 2022 EN FT
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## 82 US_100_US_L 503 2022 SE FT
## 83 US_100_US_L 504 2022 MI FT
## 84 US_100_US_L 505 2022 SE FT
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## 86 US_100_US_S 40 2020 EN FT
## 87 US_100_US_S 69 2020 EN FT
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## 89 US_100_US_S 119 2021 EN FT
## 90 US_100_US_S 144 2021 MI FT
## 91 US_100_US_S 160 2021 EN FT
## 92 US_100_US_S 173 2021 EN FT
## 93 US_100_US_S 200 2021 EN FT
## 94 US_100_US_S 225 2021 EX CT
## 95 US_100_US_S 231 2021 SE FT
## 96 US_100_US_S 251 2021 EN FT
## 97 US_100_US_S 434 2022 MI FT
## 98 US_100_US_S 492 2022 EN FT
## 99 HU_50_HU_L 8 2020 MI FT
## 100 NZ_50_NZ_S 10 2020 SE FT
## 101 FR_0_FR_S 11 2020 EN FT
## 102 IN_0_IN_L 12 2020 MI FT
## 103 IN_0_IN_L 181 2021 MI FT
## 104 IN_0_IN_L 262 2021 SE FT
## 105 FR_0_FR_M 13 2020 EN FT
## 106 PK_50_PK_L 16 2020 MI FT
## 107 JP_100_JP_S 17 2020 EN FT
## 108 PL_100_GB_S 18 2020 SE FT
## 109 IN_50_IN_M 19 2020 EN FT
## 110 IN_50_IN_M 78 2021 MI PT
## 111 IN_50_IN_M 239 2021 EN FT
## 112 PT_100_US_M 20 2020 MI FT
## 113 CN_0_CN_M 21 2020 MI FT
## 114 IN_100_IN_L 22 2020 MI FT
## 115 IN_100_IN_L 93 2021 MI FT
## 116 IN_100_IN_L 110 2021 EN FT
## 117 IN_100_IN_L 198 2021 SE FT
## 118 IN_100_IN_L 230 2021 EN FT
## 119 IN_100_IN_L 440 2022 MI FT
## 120 GR_50_GR_L 23 2020 SE FT
## 121 US_0_US_M 24 2020 MI FT
## 122 US_0_US_M 34 2020 MI FT
## 123 US_0_US_M 208 2021 SE FT
## 124 US_0_US_M 265 2021 MI FT
## 125 US_0_US_M 266 2021 MI FT
## 126 US_0_US_M 291 2022 MI FT
## 127 US_0_US_M 292 2022 MI FT
## 128 US_0_US_M 304 2022 SE FT
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## 131 US_0_US_M 307 2022 MI FT
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## 141 US_0_US_M 364 2022 SE FT
## 142 US_0_US_M 367 2022 SE FT
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## 144 US_0_US_M 380 2022 MI FT
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## 146 US_0_US_M 390 2022 SE FT
## 147 US_0_US_M 404 2022 MI FT
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## 150 US_0_US_M 411 2022 MI FT
## 151 US_0_US_M 435 2022 EN FT
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## 161 US_0_US_M 537 2022 SE FT
## 162 US_0_US_M 563 2022 SE FT
## 163 AE_0_AE_L 25 2020 MI FT
## 164 NL_50_NL_L 27 2020 EN FT
## 165 MX_0_MX_S 28 2020 SE FT
## 166 MX_0_MX_S 177 2021 MI FT
## 167 CA_50_CA_L 30 2020 SE FT
## 168 CA_50_CA_L 156 2021 SE FT
## 169 CA_50_CA_L 195 2021 SE FT
## 170 CA_50_CA_L 433 2022 EX FT
## 171 CA_50_CA_L 491 2022 MI FT
## 172 DE_100_DE_S 31 2020 MI FT
## 173 DE_100_DE_S 46 2020 EN PT
## 174 DE_100_DE_S 92 2021 EN FT
## 175 GR_100_US_L 33 2020 SE FT
## 176 FR_50_FR_L 35 2020 MI FT
## 177 FR_50_FR_L 50 2020 MI FT
## 178 FR_50_FR_L 76 2021 SE FT
## 179 FR_50_FR_L 246 2021 EN FT
## 180 FR_50_FR_L 478 2022 SE FT
## 181 AT_50_AT_L 36 2020 MI FT
## 182 NG_100_NG_S 39 2020 EN FT
## 183 PH_100_US_S 41 2020 MI FT
## 184 ES_50_ES_L 42 2020 EX FT
## 185 PT_0_PT_M 43 2020 MI FT
## 186 GB_50_GB_L 45 2020 MI FT
## 187 GB_50_GB_L 73 2021 EN FT
## 188 GB_50_GB_L 106 2021 MI FT
## 189 GB_50_GB_L 184 2021 SE FT
## 190 GB_50_GB_L 221 2021 MI FT
## 191 GB_50_GB_L 223 2021 MI FT
## 192 GB_50_GB_L 245 2021 MI FT
## 193 GB_50_GB_L 248 2021 SE FT
## 194 GB_100_GB_S 47 2020 MI FT
## 195 GB_100_GB_S 113 2021 SE FT
## 196 IN_0_IN_S 51 2020 EN FT
## 197 IN_0_IN_S 128 2021 MI FT
## 198 DK_50_DK_S 53 2020 EN FT
## 199 DK_50_DK_S 217 2021 EN PT
## 200 PK_100_DE_L 54 2020 EN FT
## 201 RU_100_US_S 55 2020 SE FL
## 202 RU_100_US_S 495 2022 MI FT
## 203 DE_100_DE_M 56 2020 SE FT
## 204 DE_100_DE_M 256 2021 SE FT
## 205 DE_100_DE_M 445 2022 SE FT
## 206 ES_100_ES_M 57 2020 MI FT
## 207 ES_100_ES_M 237 2021 MI FT
## 208 ES_100_ES_M 413 2022 MI FT
## 209 ES_100_ES_M 416 2022 MI FT
## 210 ES_100_ES_M 417 2022 MI FT
## 211 ES_100_ES_M 418 2022 MI FT
## 212 ES_100_ES_M 419 2022 MI FT
## 213 ES_100_ES_M 422 2022 MI FT
## 214 US_100_US_M 58 2020 MI FT
## 215 US_100_US_M 60 2020 MI FT
## 216 US_100_US_M 68 2020 SE FT
## 217 US_100_US_M 77 2021 MI FT
## 218 US_100_US_M 80 2021 EN FT
## 219 US_100_US_M 99 2021 EN FT
## 220 US_100_US_M 109 2021 SE FT
## 221 US_100_US_M 122 2021 SE FT
## 222 US_100_US_M 123 2021 EN FT
## 223 US_100_US_M 136 2021 MI FT
## 224 US_100_US_M 140 2021 EN FT
## 225 US_100_US_M 152 2021 MI FT
## 226 US_100_US_M 159 2021 SE FT
## 227 US_100_US_M 202 2021 SE FT
## 228 US_100_US_M 249 2021 SE FT
## 229 US_100_US_M 274 2021 EN FT
## 230 US_100_US_M 282 2021 SE CT
## 231 US_100_US_M 288 2022 SE FT
## 232 US_100_US_M 289 2022 SE FT
## 233 US_100_US_M 290 2022 SE FT
## 234 US_100_US_M 293 2022 MI FT
## 235 US_100_US_M 294 2022 MI FT
## 236 US_100_US_M 295 2022 SE FT
## 237 US_100_US_M 296 2022 SE FT
## 238 US_100_US_M 297 2022 SE FT
## 239 US_100_US_M 298 2022 SE FT
## 240 US_100_US_M 301 2022 SE FT
## 241 US_100_US_M 302 2022 SE FT
## 242 US_100_US_M 308 2022 EX FT
## 243 US_100_US_M 309 2022 EX FT
## 244 US_100_US_M 314 2022 SE FT
## 245 US_100_US_M 316 2022 SE FT
## 246 US_100_US_M 317 2022 SE FT
## 247 US_100_US_M 324 2022 SE FT
## 248 US_100_US_M 325 2022 EX FT
## 249 US_100_US_M 326 2022 EX FT
## 250 US_100_US_M 327 2022 SE FT
## 251 US_100_US_M 328 2022 MI FT
## 252 US_100_US_M 329 2022 SE FT
## 253 US_100_US_M 330 2022 SE FT
## 254 US_100_US_M 331 2022 SE FT
## 255 US_100_US_M 332 2022 MI FT
## 256 US_100_US_M 333 2022 SE FT
## 257 US_100_US_M 334 2022 SE FT
## 258 US_100_US_M 335 2022 SE FT
## 259 US_100_US_M 336 2022 SE FT
## 260 US_100_US_M 337 2022 SE FT
## 261 US_100_US_M 338 2022 EX FT
## 262 US_100_US_M 339 2022 EX FT
## 263 US_100_US_M 340 2022 EX FT
## 264 US_100_US_M 341 2022 SE FT
## 265 US_100_US_M 345 2022 SE FT
## 266 US_100_US_M 346 2022 SE FT
## 267 US_100_US_M 347 2022 SE FT
## 268 US_100_US_M 348 2022 SE FT
## 269 US_100_US_M 352 2022 SE FT
## 270 US_100_US_M 358 2022 SE FT
## 271 US_100_US_M 361 2022 EX FT
## 272 US_100_US_M 362 2022 SE FT
## 273 US_100_US_M 368 2022 SE FT
## 274 US_100_US_M 369 2022 SE FT
## 275 US_100_US_M 370 2022 SE FT
## 276 US_100_US_M 371 2022 SE FT
## 277 US_100_US_M 372 2022 SE FT
## 278 US_100_US_M 373 2022 SE FT
## 279 US_100_US_M 377 2022 SE FT
## 280 US_100_US_M 381 2022 SE FT
## 281 US_100_US_M 382 2022 SE FT
## 282 US_100_US_M 391 2022 SE FT
## 283 US_100_US_M 395 2022 SE FT
## 284 US_100_US_M 402 2022 SE FT
## 285 US_100_US_M 407 2022 MI FT
## 286 US_100_US_M 408 2022 MI FT
## 287 US_100_US_M 429 2022 SE FT
## 288 US_100_US_M 431 2022 SE FT
## 289 US_100_US_M 437 2022 SE FT
## 290 US_100_US_M 446 2022 EN FT
## 291 US_100_US_M 448 2022 SE FT
## 292 US_100_US_M 449 2022 SE FT
## 293 US_100_US_M 450 2022 SE FT
## 294 US_100_US_M 451 2022 MI FT
## 295 US_100_US_M 452 2022 MI FT
## 296 US_100_US_M 453 2022 SE FT
## 297 US_100_US_M 456 2022 SE FT
## 298 US_100_US_M 457 2022 SE FT
## 299 US_100_US_M 458 2022 MI FT
## 300 US_100_US_M 459 2022 MI FT
## 301 US_100_US_M 462 2022 EX FT
## 302 US_100_US_M 463 2022 EX FT
## 303 US_100_US_M 464 2022 SE FT
## 304 US_100_US_M 465 2022 SE FT
## 305 US_100_US_M 466 2022 SE FT
## 306 US_100_US_M 477 2022 SE FT
## 307 US_100_US_M 484 2022 SE FT
## 308 US_100_US_M 496 2022 SE FT
## 309 US_100_US_M 506 2022 MI FT
## 310 US_100_US_M 507 2022 SE FT
## 311 US_100_US_M 510 2022 SE FT
## 312 US_100_US_M 511 2022 SE FT
## 313 US_100_US_M 512 2022 SE FT
## 314 US_100_US_M 513 2022 SE FT
## 315 US_100_US_M 516 2022 SE FT
## 316 US_100_US_M 517 2022 SE FT
## 317 US_100_US_M 520 2022 SE FT
## 318 US_100_US_M 521 2022 SE FT
## 319 US_100_US_M 522 2022 SE FT
## 320 US_100_US_M 523 2022 SE FT
## 321 US_100_US_M 525 2022 SE FT
## 322 US_100_US_M 526 2022 SE FT
## 323 US_100_US_M 527 2022 SE FT
## 324 US_100_US_M 528 2022 SE FT
## 325 US_100_US_M 534 2022 SE FT
## 326 US_100_US_M 535 2022 SE FT
## 327 US_100_US_M 536 2022 SE FT
## 328 US_100_US_M 539 2022 SE FT
## 329 US_100_US_M 540 2022 SE FT
## 330 US_100_US_M 541 2022 SE FT
## 331 US_100_US_M 542 2022 SE FT
## 332 US_100_US_M 543 2022 SE FT
## 333 US_100_US_M 544 2022 SE FT
## 334 US_100_US_M 545 2022 SE FT
## 335 US_100_US_M 546 2022 SE FT
## 336 US_100_US_M 547 2022 SE FT
## 337 US_100_US_M 548 2022 SE FT
## 338 US_100_US_M 549 2022 SE FT
## 339 US_100_US_M 551 2022 SE FT
## 340 US_100_US_M 552 2022 SE FT
## 341 US_100_US_M 553 2022 SE FT
## 342 US_100_US_M 554 2022 SE FT
## 343 US_100_US_M 555 2022 SE FT
## 344 US_100_US_M 556 2022 SE FT
## 345 US_100_US_M 557 2022 MI FT
## 346 US_100_US_M 558 2022 MI FT
## 347 US_100_US_M 561 2022 SE FT
## 348 US_100_US_M 562 2022 SE FT
## 349 US_100_US_M 564 2022 SE FT
## 350 ES_100_US_M 62 2020 MI FT
## 351 IT_50_IT_S 63 2020 EN PT
## 352 HR_100_HR_S 65 2020 SE FT
## 353 DE_50_DE_S 66 2020 EN FT
## 354 DE_0_DE_S 67 2020 EN FT
## 355 AT_0_AT_S 70 2020 SE FT
## 356 FR_50_LU_S 71 2020 MI FT
## 357 FR_50_FR_S 72 2020 MI FT
## 358 FR_50_FR_S 212 2021 MI FT
## 359 IN_100_US_L 74 2021 EX FT
## 360 IN_100_US_L 565 2022 MI FT
## 361 DE_100_DE_L 81 2021 SE FT
## 362 GB_50_CA_L 83 2021 MI FT
## 363 ES_100_ES_S 84 2021 MI FT
## 364 IT_100_PL_L 85 2021 EX FT
## 365 PL_100_PL_L 86 2021 MI FT
## 366 PL_100_PL_L 220 2021 MI FT
## 367 PL_100_PL_L 472 2022 MI FT
## 368 FR_50_FR_M 87 2021 EN FT
## 369 FR_50_FR_M 132 2021 EN FT
## 370 FR_50_FR_M 147 2021 MI FT
## 371 FR_50_FR_M 273 2021 SE FT
## 372 BG_100_US_S 90 2021 SE FT
## 373 GR_100_DK_L 91 2021 SE FT
## 374 US_0_US_L 94 2021 SE FT
## 375 US_0_US_L 101 2021 MI FT
## 376 US_0_US_L 104 2021 MI FT
## 377 US_0_US_L 105 2021 MI FT
## 378 US_0_US_L 139 2021 SE FT
## 379 US_0_US_L 142 2021 SE FT
## 380 US_0_US_L 143 2021 SE FT
## 381 US_0_US_L 168 2021 EX FT
## 382 US_0_US_L 210 2021 SE FT
## 383 US_0_US_L 228 2021 SE FT
## 384 US_0_US_L 322 2022 SE FT
## 385 US_0_US_L 323 2022 SE FT
## 386 US_0_US_L 344 2022 SE FT
## 387 US_0_US_L 357 2022 SE FT
## 388 US_0_US_L 414 2022 SE FT
## 389 US_0_US_L 427 2022 SE FT
## 390 US_0_US_L 524 2022 SE FT
## 391 IN_50_IN_L 95 2021 EN FT
## 392 IN_50_IN_L 130 2021 SE FT
## 393 IN_50_IN_L 284 2021 SE FT
## 394 IN_50_IN_L 374 2022 EX FT
## 395 SG_50_SG_L 96 2021 MI FT
## 396 BR_100_US_S 97 2021 EN PT
## 397 BR_100_US_S 150 2021 SE FT
## 398 DE_100_US_S 100 2021 MI FT
## 399 HU_50_US_L 103 2021 MI FT
## 400 CA_100_CA_L 107 2021 MI FT
## 401 CA_100_CA_L 153 2021 MI FT
## 402 CA_100_CA_L 240 2021 SE FT
## 403 CA_100_CA_L 479 2022 EN FT
## 404 DE_50_DE_L 111 2021 SE FT
## 405 DE_50_DE_L 164 2021 EN FT
## 406 DE_50_DE_L 182 2021 MI FT
## 407 DE_50_DE_L 227 2021 MI FT
## 408 DE_50_DE_L 260 2021 SE FT
## 409 DE_50_DE_L 443 2022 MI PT
## 410 GB_100_GB_M 112 2021 SE FT
## 411 GB_100_GB_M 303 2022 EN FT
## 412 GB_100_GB_M 315 2022 EN FT
## 413 GB_100_GB_M 394 2022 MI FT
## 414 GB_100_GB_M 400 2022 MI FT
## 415 GB_100_GB_M 401 2022 MI FT
## 416 GB_100_GB_M 415 2022 MI FT
## 417 GB_100_GB_M 420 2022 MI FT
## 418 GB_100_GB_M 421 2022 MI FT
## 419 GB_100_GB_M 426 2022 MI FT
## 420 PK_100_US_M 114 2021 EN PT
## 421 NL_100_NL_L 115 2021 MI FT
## 422 NL_100_NL_L 281 2021 MI PT
## 423 NL_100_NL_L 480 2022 SE FT
## 424 NG_100_NG_L 117 2021 MI FT
## 425 GR_100_GR_M 118 2021 MI FT
## 426 GR_100_GR_M 398 2022 MI FT
## 427 GR_100_GR_M 399 2022 MI FT
## 428 GR_100_GR_M 423 2022 MI FT
## 429 GR_100_GR_M 424 2022 MI FT
## 430 GR_100_GR_M 425 2022 MI FT
## 431 GR_100_GR_M 428 2022 MI FT
## 432 ES_50_RO_M 121 2021 MI FT
## 433 GB_0_GB_L 124 2021 EN FT
## 434 ES_50_ES_M 125 2021 EN PT
## 435 US_50_US_S 127 2021 SE FT
## 436 US_50_US_S 169 2021 EN FT
## 437 US_50_US_S 179 2021 EN FT
## 438 IN_100_IN_S 129 2021 EN FT
## 439 IN_100_IN_S 261 2021 MI FT
## 440 IQ_50_IQ_S 131 2021 EN FT
## 441 VN_100_US_M 133 2021 MI FT
## 442 BR_100_BR_M 134 2021 SE FT
## 443 US_0_US_S 135 2021 EN FT
## 444 US_0_US_S 235 2021 MI FT
## 445 US_0_US_S 360 2022 MI FT
## 446 JP_50_JP_S 137 2021 MI FT
## 447 JP_50_JP_S 138 2021 MI FT
## 448 JP_50_JP_S 190 2021 MI FT
## 449 BE_50_BE_M 146 2021 SE FT
## 450 UA_100_UA_L 154 2021 EN FT
## 451 SG_100_IL_M 157 2021 MI FT
## 452 RU_50_RU_L 161 2021 EX FT
## 453 RU_0_RU_M 162 2021 EX FT
## 454 MT_50_MT_L 163 2021 MI FT
## 455 DE_50_DE_M 165 2021 EX FT
## 456 DE_50_DE_M 215 2021 EN FT
## 457 DE_50_DE_M 268 2021 EN FT
## 458 GB_100_GB_L 172 2021 MI FT
## 459 GB_100_GB_L 375 2022 EN FT
## 460 PT_50_PT_L 175 2021 SE FT
## 461 PT_50_PT_L 441 2022 MI FT
## 462 CL_100_CL_L 178 2021 MI FT
## 463 IN_100_US_S 180 2021 MI FT
## 464 RO_0_US_L 183 2021 MI FT
## 465 PK_50_PK_M 185 2021 MI FL
## 466 IR_100_IR_M 186 2021 MI FT
## 467 VN_100_GB_M 187 2021 SE FT
## 468 FR_100_ES_S 188 2021 EX FT
## 469 RO_50_GB_S 189 2021 SE FT
## 470 US_100_FR_L 191 2021 SE FT
## 471 CO_50_CO_M 192 2021 EN FT
## 472 MD_0_MD_S 193 2021 MI FT
## 473 KE_100_KE_S 197 2021 EN FT
## 474 IN_50_US_L 199 2021 SE FT
## 475 DE_50_AT_M 201 2021 MI FT
## 476 ES_100_ES_L 203 2021 MI FT
## 477 ES_100_ES_L 276 2021 SE FT
## 478 FR_100_US_S 204 2021 SE FT
## 479 BR_0_BR_S 206 2021 MI FT
## 480 IT_0_US_L 209 2021 MI FL
## 481 SI_50_SI_L 211 2021 MI FT
## 482 HK_50_GB_S 213 2021 MI FT
## 483 IN_0_CH_L 214 2021 EN FT
## 484 BE_100_BE_M 218 2021 MI FT
## 485 IN_0_IN_M 222 2021 MI FT
## 486 US_100_CA_L 224 2021 SE FT
## 487 CA_100_CA_S 226 2021 SE FT
## 488 CA_100_CA_S 366 2022 EX FT
## 489 CA_100_CA_S 471 2022 MI FT
## 490 CA_100_CA_M 229 2021 SE FT
## 491 CA_100_CA_M 236 2021 MI FT
## 492 CA_100_CA_M 353 2022 SE FT
## 493 CA_100_CA_M 354 2022 SE FT
## 494 CA_100_CA_M 355 2022 SE FT
## 495 CA_100_CA_M 356 2022 SE FT
## 496 CA_100_CA_M 432 2022 MI FT
## 497 CA_100_CA_M 473 2022 SE FT
## 498 CA_100_CA_M 500 2022 MI FT
## 499 VN_0_VN_M 238 2021 EN FT
## 500 IN_100_AS_S 244 2021 EN FT
## 501 TR_0_TR_M 247 2021 MI FT
## 502 IN_100_IN_M 253 2021 EN FT
## 503 IN_100_IN_M 439 2022 MI FT
## 504 IN_100_IN_M 444 2022 EN FT
## 505 CA_50_CA_M 255 2021 SE FT
## 506 RS_100_DE_S 263 2021 MI FT
## 507 PR_50_US_S 264 2021 SE FT
## 508 TR_100_TR_M 267 2021 MI FT
## 509 BR_0_BR_M 270 2021 SE FT
## 510 NL_100_DE_S 272 2021 EN FT
## 511 TR_50_TR_L 277 2021 SE FT
## 512 LU_100_LU_L 278 2021 EN FT
## 513 JE_0_CN_L 280 2021 EN FT
## 514 CZ_50_CZ_L 283 2021 MI FT
## 515 SI_100_SI_L 285 2021 SE FT
## 516 IT_50_IT_L 287 2021 MI FT
## 517 GB_0_GB_M 299 2022 SE FT
## 518 GB_0_GB_M 300 2022 SE FT
## 519 GB_0_GB_M 310 2022 MI FT
## 520 GB_0_GB_M 311 2022 MI FT
## 521 GB_0_GB_M 312 2022 MI FT
## 522 GB_0_GB_M 313 2022 MI FT
## 523 GB_0_GB_M 349 2022 SE FT
## 524 GB_0_GB_M 350 2022 SE FT
## 525 GB_0_GB_M 378 2022 MI FT
## 526 GB_0_GB_M 379 2022 MI FT
## 527 GB_0_GB_M 384 2022 MI FT
## 528 GB_0_GB_M 392 2022 MI FT
## 529 GB_0_GB_M 396 2022 MI FT
## 530 GB_0_GB_M 397 2022 MI FT
## 531 GB_0_GB_M 454 2022 MI FT
## 532 GB_0_GB_M 455 2022 MI FT
## 533 GB_0_GB_M 538 2022 MI FT
## 534 GB_0_GB_M 550 2022 MI FT
## 535 GR_0_GR_M 365 2022 MI FT
## 536 FR_100_DE_M 383 2022 MI FT
## 537 AR_100_MX_L 403 2022 SE FT
## 538 PT_100_PT_L 430 2022 EN FT
## 539 DE_0_DE_M 438 2022 SE FT
## 540 AE_100_AE_S 460 2022 SE FT
## 541 AE_100_AE_S 461 2022 SE FT
## 542 DZ_50_DZ_M 467 2022 EN PT
## 543 CA_100_US_M 468 2022 MI FL
## 544 TN_100_CZ_M 469 2022 EN CT
## 545 MY_100_US_M 470 2022 SE FT
## 546 BR_100_US_M 474 2022 SE FT
## 547 US_50_US_M 475 2022 MI FT
## 548 PK_100_DE_M 476 2022 EN FT
## 549 EE_100_EE_S 481 2022 MI FT
## 550 JP_100_MY_L 482 2022 EN FT
## 551 AU_100_AU_L 483 2022 MI FT
## 552 AU_50_AU_M 485 2022 EN FT
## 553 BO_100_US_L 486 2022 MI FT
## 554 AT_0_AT_L 487 2022 MI FT
## 555 AU_100_AU_S 490 2022 EN FT
## 556 IE_100_IE_S 493 2022 SE FT
## 557 PK_0_PK_M 494 2022 EN FT
## 558 FR_100_FR_M 497 2022 MI FT
## 559 CH_0_CH_L 498 2022 MI FT
## 560 PT_100_LU_M 501 2022 EN FT
## 561 GR_100_GR_S 502 2022 MI FT
## 562 CA_0_CA_M 508 2022 MI FT
## 563 CA_0_CA_M 509 2022 MI FT
## 564 CA_0_CA_M 559 2022 EN FT
## 565 CA_0_CA_M 560 2022 EN FT
## job_title salary salary_currency
## 1 Data Scientist 70000 EUR
## 2 Director of Data Science 120000 EUR
## 3 Data Science Consultant 65000 EUR
## 4 Machine Learning Scientist 260000 USD
## 5 Director of Data Science 168000 USD
## 6 Big Data Engineer 85000 GBP
## 7 Product Data Analyst 20000 USD
## 8 Machine Learning Engineer 150000 USD
## 9 Machine Learning Engineer 250000 USD
## 10 Data Scientist 120000 USD
## 11 Applied Machine Learning Scientist 423000 USD
## 12 Data Scientist 147000 USD
## 13 Data Scientist 115000 USD
## 14 Machine Learning Engineer 185000 USD
## 15 Data Scientist 130000 USD
## 16 Data Scientist 58000 USD
## 17 Data Scientist 109000 USD
## 18 NLP Engineer 240000 CNY
## 19 Financial Data Analyst 100000 USD
## 20 Research Scientist 144000 USD
## 21 Data Analyst 72000 USD
## 22 Business Data Analyst 135000 USD
## 23 Lead Data Analyst 87000 USD
## 24 Data Analyst 85000 USD
## 25 Director of Data Science 325000 USD
## 26 Business Data Analyst 100000 USD
## 27 Big Data Engineer 70000 USD
## 28 Data Science Consultant 103000 USD
## 29 Data Engineer 106000 USD
## 30 Data Engineer 188000 USD
## 31 Data Scientist 105000 USD
## 32 Data Analyst 91000 USD
## 33 Data Engineer 110000 USD
## 34 Data Scientist 412000 USD
## 35 Head of Data 235000 USD
## 36 ML Engineer 270000 USD
## 37 Data Engineer 140000 USD
## 38 Data Analytics Engineer 110000 USD
## 39 Lead Data Analyst 170000 USD
## 40 Financial Data Analyst 450000 USD
## 41 Data Engineer 150000 USD
## 42 Machine Learning Scientist 225000 USD
## 43 Data Engineer 200000 USD
## 44 Principal Data Scientist 151000 USD
## 45 Data Analyst 135000 USD
## 46 Data Engineer 100000 USD
## 47 Data Engineer 90000 USD
## 48 Data Engineering Manager 153000 USD
## 49 Data Science Manager 144000 USD
## 50 Data Specialist 165000 USD
## 51 Data Engineer 80000 USD
## 52 Data Architect 150000 USD
## 53 Data Architect 170000 USD
## 54 Principal Data Scientist 235000 USD
## 55 Data Engineering Manager 174000 USD
## 56 Data Science Manager 174000 USD
## 57 Data Scientist 160000 USD
## 58 Machine Learning Engineer 200000 USD
## 59 Principal Data Engineer 185000 USD
## 60 Data Analytics Manager 140000 USD
## 61 Director of Data Engineering 200000 USD
## 62 Data Analyst 200000 USD
## 63 Data Architect 180000 USD
## 64 Data Analyst 80000 USD
## 65 Data Engineer 110000 USD
## 66 Data Scientist 165000 USD
## 67 Principal Data Engineer 600000 USD
## 68 Data Analyst 93000 USD
## 69 Data Engineer 72500 USD
## 70 Data Engineer 112000 USD
## 71 Data Scientist 215300 USD
## 72 Data Scientist 158200 USD
## 73 Data Engineer 209100 USD
## 74 Data Engineer 154600 USD
## 75 Data Engineer 183600 USD
## 76 Machine Learning Scientist 160000 USD
## 77 Machine Learning Scientist 112300 USD
## 78 Data Engineer 100800 USD
## 79 Research Scientist 120000 USD
## 80 Applied Data Scientist 157000 USD
## 81 Applied Data Scientist 380000 USD
## 82 Data Analytics Lead 405000 USD
## 83 Data Scientist 135000 USD
## 84 Applied Data Scientist 177000 USD
## 85 Lead Data Scientist 190000 USD
## 86 Machine Learning Engineer 138000 USD
## 87 Data Scientist 105000 USD
## 88 Data Engineer 115000 USD
## 89 Data Analyst 90000 USD
## 90 Data Scientist 82500 USD
## 91 Machine Learning Engineer 125000 USD
## 92 Data Analyst 60000 USD
## 93 Data Science Consultant 90000 USD
## 94 Principal Data Scientist 416000 USD
## 95 ML Engineer 256000 USD
## 96 Data Scientist 90000 USD
## 97 Machine Learning Engineer 120000 USD
## 98 Data Engineer 65000 USD
## 99 Data Scientist 11000000 HUF
## 100 Lead Data Engineer 125000 USD
## 101 Data Scientist 45000 EUR
## 102 Data Scientist 3000000 INR
## 103 Big Data Engineer 1672000 INR
## 104 Machine Learning Engineer 4900000 INR
## 105 Data Scientist 35000 EUR
## 106 Data Analyst 8000 USD
## 107 Data Engineer 4450000 JPY
## 108 Big Data Engineer 100000 EUR
## 109 Data Science Consultant 423000 INR
## 110 3D Computer Vision Researcher 400000 INR
## 111 Data Engineer 1600000 INR
## 112 Lead Data Engineer 56000 USD
## 113 Machine Learning Engineer 299000 CNY
## 114 Product Data Analyst 450000 INR
## 115 Lead Data Analyst 1450000 INR
## 116 Data Engineer 2250000 INR
## 117 Machine Learning Engineer 1799997 INR
## 118 Big Data Engineer 1200000 INR
## 119 Data Scientist 2400000 INR
## 120 Data Engineer 42000 EUR
## 121 BI Data Analyst 98000 USD
## 122 Research Scientist 450000 USD
## 123 Data Engineer 165000 USD
## 124 Data Engineer 93150 USD
## 125 Data Engineer 111775 USD
## 126 Data Scientist 130000 USD
## 127 Data Scientist 90000 USD
## 128 Data Analyst 99000 USD
## 129 Data Analyst 116000 USD
## 130 Data Analyst 106260 USD
## 131 Data Analyst 126500 USD
## 132 Data Engineer 181940 USD
## 133 Data Engineer 132320 USD
## 134 Data Engineer 220110 USD
## 135 Data Engineer 160080 USD
## 136 Data Engineer 108800 USD
## 137 Data Scientist 95550 USD
## 138 Data Scientist 150000 USD
## 139 Data Engineer 136000 USD
## 140 Machine Learning Engineer 189650 USD
## 141 Machine Learning Engineer 164996 USD
## 142 Data Analyst 132000 USD
## 143 Data Analyst 164000 USD
## 144 AI Scientist 120000 USD
## 145 Data Analyst 115934 USD
## 146 Data Analyst 81666 USD
## 147 Data Engineer 63900 USD
## 148 Data Scientist 180000 USD
## 149 Data Scientist 80000 USD
## 150 Data Engineer 82900 USD
## 151 Computer Vision Engineer 125000 USD
## 152 Data Scientist 141300 USD
## 153 Data Scientist 102100 USD
## 154 Data Engineer 206699 USD
## 155 Data Engineer 99100 USD
## 156 Data Engineer 70500 USD
## 157 Data Scientist 205300 USD
## 158 Data Scientist 140400 USD
## 159 Analytics Engineer 205300 USD
## 160 Analytics Engineer 184700 USD
## 161 Data Engineer 54000 USD
## 162 Data Analyst 129000 USD
## 163 Lead Data Scientist 115000 USD
## 164 Research Scientist 42000 USD
## 165 Data Engineer 720000 MXN
## 166 Data Scientist 58000 MXN
## 167 Machine Learning Manager 157000 CAD
## 168 Data Science Engineer 159500 CAD
## 169 Research Scientist 120500 CAD
## 170 Director of Data Science 250000 CAD
## 171 Business Data Analyst 90000 CAD
## 172 Data Engineering Manager 51999 EUR
## 173 ML Engineer 14000 EUR
## 174 Data Science Consultant 65000 EUR
## 175 Data Scientist 60000 EUR
## 176 Data Analyst 41000 EUR
## 177 Data Engineer 61500 EUR
## 178 Data Scientist 45000 EUR
## 179 Data Scientist 31000 EUR
## 180 Research Scientist 85000 EUR
## 181 Data Engineer 65000 EUR
## 182 Data Analyst 10000 USD
## 183 Data Scientist 45760 USD
## 184 Data Engineering Manager 70000 EUR
## 185 Machine Learning Infrastructure Engineer 44000 EUR
## 186 Data Engineer 88000 GBP
## 187 Research Scientist 60000 GBP
## 188 Data Analyst 37456 GBP
## 189 Finance Data Analyst 45000 GBP
## 190 Data Scientist 85000 GBP
## 191 Data Scientist 40900 GBP
## 192 Data Engineer 52500 GBP
## 193 Data Engineer 70000 GBP
## 194 Data Scientist 60000 GBP
## 195 Lead Data Engineer 75000 GBP
## 196 Data Analyst 450000 INR
## 197 Data Scientist 700000 INR
## 198 AI Scientist 300000 DKK
## 199 Computer Vision Engineer 180000 DKK
## 200 Data Engineer 48000 EUR
## 201 Computer Vision Engineer 60000 USD
## 202 Data Scientist 48000 USD
## 203 Principal Data Scientist 130000 EUR
## 204 Principal Data Scientist 147000 EUR
## 205 Principal Data Scientist 148000 EUR
## 206 Data Scientist 34000 EUR
## 207 Data Scientist 39600 EUR
## 208 Data Engineer 45000 EUR
## 209 Data Analyst 40000 EUR
## 210 Data Analyst 30000 EUR
## 211 Data Engineer 80000 EUR
## 212 Data Engineer 70000 EUR
## 213 Data Engineer 60000 EUR
## 214 Data Scientist 118000 USD
## 215 Data Scientist 138350 USD
## 216 Data Science Manager 190200 USD
## 217 BI Data Analyst 100000 USD
## 218 Data Analyst 80000 USD
## 219 Computer Vision Software Engineer 70000 USD
## 220 Data Engineer 150000 USD
## 221 Principal Data Engineer 200000 USD
## 222 Data Analyst 50000 USD
## 223 Data Analyst 90000 USD
## 224 Data Scientist 80000 USD
## 225 Data Scientist 150000 USD
## 226 Data Analytics Manager 120000 USD
## 227 Machine Learning Infrastructure Engineer 195000 USD
## 228 Principal Data Analyst 170000 USD
## 229 Data Scientist 100000 USD
## 230 Staff Data Scientist 105000 USD
## 231 Data Engineer 135000 USD
## 232 Data Analyst 155000 USD
## 233 Data Analyst 120600 USD
## 234 Data Engineer 170000 USD
## 235 Data Engineer 150000 USD
## 236 Data Analyst 102100 USD
## 237 Data Analyst 84900 USD
## 238 Data Scientist 136620 USD
## 239 Data Scientist 99360 USD
## 240 Data Scientist 146000 USD
## 241 Data Scientist 123000 USD
## 242 Data Engineer 242000 USD
## 243 Data Engineer 200000 USD
## 244 Data Scientist 165220 USD
## 245 Data Scientist 120160 USD
## 246 Data Analyst 90320 USD
## 247 Data Analyst 124190 USD
## 248 Data Analyst 130000 USD
## 249 Data Analyst 110000 USD
## 250 Data Analyst 170000 USD
## 251 Data Analyst 115500 USD
## 252 Data Analyst 112900 USD
## 253 Data Engineer 165400 USD
## 254 Data Engineer 132320 USD
## 255 Data Analyst 167000 USD
## 256 Data Engineer 243900 USD
## 257 Data Analyst 136600 USD
## 258 Data Analyst 109280 USD
## 259 Data Engineer 128875 USD
## 260 Data Engineer 93700 USD
## 261 Head of Data Science 224000 USD
## 262 Head of Data Science 167875 USD
## 263 Analytics Engineer 175000 USD
## 264 Data Engineer 156600 USD
## 265 Data Analyst 135000 USD
## 266 Data Science Manager 161342 USD
## 267 Data Science Manager 137141 USD
## 268 Data Scientist 167000 USD
## 269 Data Scientist 211500 USD
## 270 Data Scientist 138600 USD
## 271 Analytics Engineer 135000 USD
## 272 Data Scientist 170000 USD
## 273 Data Architect 208775 USD
## 274 Data Architect 147800 USD
## 275 Data Engineer 136994 USD
## 276 Data Engineer 101570 USD
## 277 Data Analyst 128875 USD
## 278 Data Analyst 93700 USD
## 279 Data Engineer 155000 USD
## 280 Data Analytics Manager 145000 USD
## 281 Data Analytics Manager 105400 USD
## 282 Data Engineer 175000 USD
## 283 Data Scientist 180000 USD
## 284 Data Scientist 260000 USD
## 285 Data Science Manager 241000 USD
## 286 Data Science Manager 159000 USD
## 287 Data Engineer 180000 USD
## 288 Data Engineer 80000 USD
## 289 Data Engineer 105000 USD
## 290 Data Engineer 120000 USD
## 291 Data Scientist 104890 USD
## 292 Data Engineer 100000 USD
## 293 Data Scientist 140000 USD
## 294 Data Analyst 135000 USD
## 295 Data Analyst 50000 USD
## 296 Data Scientist 220000 USD
## 297 Data Scientist 185100 USD
## 298 Machine Learning Engineer 220000 USD
## 299 Data Scientist 200000 USD
## 300 Data Scientist 120000 USD
## 301 Data Engineer 324000 USD
## 302 Data Engineer 216000 USD
## 303 Data Engineer 210000 USD
## 304 Machine Learning Engineer 120000 USD
## 305 Data Scientist 230000 USD
## 306 Data Scientist 165000 USD
## 307 Data Engineer 115000 USD
## 308 Data Science Manager 152500 USD
## 309 Data Scientist 78000 USD
## 310 Data Analyst 100000 USD
## 311 Machine Learning Engineer 214000 USD
## 312 Machine Learning Engineer 192600 USD
## 313 Data Architect 266400 USD
## 314 Data Architect 213120 USD
## 315 Data Analyst 115934 USD
## 316 Data Analyst 81666 USD
## 317 Data Engineer 130000 USD
## 318 Data Engineer 110500 USD
## 319 Data Analyst 99050 USD
## 320 Data Engineer 160000 USD
## 321 Data Scientist 176000 USD
## 322 Data Scientist 144000 USD
## 323 Data Engineer 200100 USD
## 324 Data Engineer 145000 USD
## 325 Data Engineer 175100 USD
## 326 Data Engineer 140250 USD
## 327 Data Analyst 116150 USD
## 328 Data Analyst 80000 USD
## 329 Data Scientist 210000 USD
## 330 Data Analyst 69000 USD
## 331 Data Analyst 150075 USD
## 332 Data Engineer 25000 USD
## 333 Data Analyst 126500 USD
## 334 Data Analyst 106260 USD
## 335 Data Engineer 220110 USD
## 336 Data Engineer 160080 USD
## 337 Data Analyst 105000 USD
## 338 Data Analyst 110925 USD
## 339 Data Analyst 60000 USD
## 340 Data Architect 192564 USD
## 341 Data Architect 144854 USD
## 342 Data Scientist 150000 USD
## 343 Data Analytics Manager 150260 USD
## 344 Data Analytics Manager 109280 USD
## 345 Data Scientist 160000 USD
## 346 Data Scientist 130000 USD
## 347 Data Engineer 154000 USD
## 348 Data Engineer 126000 USD
## 349 Data Analyst 150000 USD
## 350 Data Engineer 130800 USD
## 351 Data Scientist 19000 EUR
## 352 Machine Learning Engineer 40000 EUR
## 353 Data Scientist 55000 EUR
## 354 Data Scientist 43200 EUR
## 355 Data Scientist 80000 EUR
## 356 Data Scientist 55000 EUR
## 357 Data Scientist 37000 EUR
## 358 Research Scientist 48000 EUR
## 359 BI Data Analyst 150000 USD
## 360 AI Scientist 200000 USD
## 361 Data Analytics Engineer 67000 EUR
## 362 Applied Data Scientist 68000 CAD
## 363 Machine Learning Engineer 40000 EUR
## 364 Director of Data Science 130000 EUR
## 365 Data Engineer 110000 PLN
## 366 Machine Learning Engineer 180000 PLN
## 367 Data Scientist 150000 PLN
## 368 Data Analyst 50000 EUR
## 369 Data Scientist 42000 EUR
## 370 Research Scientist 53000 EUR
## 371 Data Scientist 65720 EUR
## 372 Data Analyst 80000 USD
## 373 Marketing Data Analyst 75000 EUR
## 374 Lead Data Engineer 276000 USD
## 375 Data Analyst 75000 USD
## 376 Data Analyst 62000 USD
## 377 Data Scientist 73000 USD
## 378 Principal Data Scientist 220000 USD
## 379 Data Science Manager 240000 USD
## 380 Data Engineering Manager 150000 USD
## 381 Director of Data Science 250000 USD
## 382 Data Analytics Manager 120000 USD
## 383 Data Scientist 135000 USD
## 384 Data Scientist 180000 USD
## 385 Data Scientist 120000 USD
## 386 Data Engineer 113000 USD
## 387 Data Engineer 160000 USD
## 388 Data Scientist 140400 USD
## 389 Data Scientist 215300 USD
## 390 Data Scientist 205300 USD
## 391 Data Scientist 2200000 INR
## 392 Lead Data Scientist 3000000 INR
## 393 Data Science Manager 7000000 INR
## 394 Head of Machine Learning 6000000 INR
## 395 Cloud Data Engineer 120000 SGD
## 396 AI Scientist 12000 USD
## 397 Cloud Data Engineer 160000 USD
## 398 Computer Vision Software Engineer 81000 EUR
## 399 BI Data Analyst 11000000 HUF
## 400 Research Scientist 235000 CAD
## 401 Data Scientist 95000 CAD
## 402 Data Scientist 130000 CAD
## 403 Data Scientist 66500 CAD
## 404 Machine Learning Engineer 80000 EUR
## 405 Data Science Consultant 54000 EUR
## 406 Data Scientist 76760 EUR
## 407 Data Scientist 75000 EUR
## 408 Data Analyst 54000 EUR
## 409 Data Engineer 50000 EUR
## 410 Director of Data Engineering 82500 GBP
## 411 Data Engineer 40000 GBP
## 412 Data Engineer 35000 GBP
## 413 Data Analyst 40000 GBP
## 414 Data Engineer 60000 GBP
## 415 Data Engineer 45000 GBP
## 416 Data Analyst 30000 GBP
## 417 Data Engineer 80000 GBP
## 418 Data Engineer 70000 GBP
## 419 Data Engineer 75000 GBP
## 420 AI Scientist 12000 USD
## 421 Data Engineer 38400 EUR
## 422 Data Engineer 59000 EUR
## 423 Machine Learning Engineer 57000 EUR
## 424 Data Scientist 50000 USD
## 425 Data Science Engineer 34000 EUR
## 426 Data Engineer 60000 EUR
## 427 Data Engineer 45000 EUR
## 428 Data Engineer 80000 EUR
## 429 Data Analyst 40000 EUR
## 430 Data Analyst 30000 EUR
## 431 Data Engineer 70000 EUR
## 432 Big Data Engineer 60000 USD
## 433 Applied Data Scientist 80000 GBP
## 434 Data Analyst 8760 EUR
## 435 Machine Learning Scientist 120000 USD
## 436 BI Data Analyst 55000 USD
## 437 Machine Learning Engineer 81000 USD
## 438 Machine Learning Engineer 20000 USD
## 439 Data Scientist 1250000 INR
## 440 Machine Learning Developer 100000 USD
## 441 Applied Machine Learning Scientist 38400 USD
## 442 Computer Vision Engineer 24000 USD
## 443 Data Scientist 100000 USD
## 444 Head of Data Science 110000 USD
## 445 Data Analyst 58000 USD
## 446 ML Engineer 7000000 JPY
## 447 ML Engineer 8500000 JPY
## 448 Machine Learning Engineer 74000 USD
## 449 Machine Learning Engineer 70000 EUR
## 450 Data Scientist 13400 USD
## 451 Data Scientist 160000 SGD
## 452 Head of Data 230000 USD
## 453 Head of Data Science 85000 USD
## 454 Data Engineer 24000 EUR
## 455 Director of Data Science 110000 EUR
## 456 Machine Learning Engineer 21000 EUR
## 457 Data Engineer 55000 EUR
## 458 Data Engineer 60000 GBP
## 459 Machine Learning Engineer 28500 GBP
## 460 Research Scientist 51400 EUR
## 461 Machine Learning Infrastructure Engineer 53000 EUR
## 462 Data Scientist 30400000 CLP
## 463 Data Scientist 420000 INR
## 464 Data Engineer 22000 EUR
## 465 Machine Learning Scientist 12000 USD
## 466 Data Engineer 4000 USD
## 467 Data Analytics Engineer 50000 USD
## 468 Data Science Consultant 59000 EUR
## 469 Data Engineer 65000 EUR
## 470 Data Science Manager 152000 USD
## 471 Machine Learning Engineer 21844 USD
## 472 Big Data Engineer 18000 USD
## 473 BI Data Analyst 9272 USD
## 474 Data Science Manager 4000000 INR
## 475 Data Scientist 52000 EUR
## 476 Data Scientist 32000 EUR
## 477 AI Scientist 55000 USD
## 478 Research Scientist 50000 USD
## 479 Data Scientist 69600 BRL
## 480 Data Engineer 20000 USD
## 481 Machine Learning Engineer 21000 EUR
## 482 Data Engineer 48000 GBP
## 483 Big Data Engineer 435000 INR
## 484 Machine Learning Engineer 75000 EUR
## 485 Data Scientist 2500000 INR
## 486 Machine Learning Scientist 225000 USD
## 487 Data Scientist 110000 CAD
## 488 Lead Data Engineer 150000 CAD
## 489 Principal Data Analyst 75000 USD
## 490 Data Analyst 90000 CAD
## 491 Research Scientist 80000 CAD
## 492 Data Architect 192400 USD
## 493 Data Architect 90700 USD
## 494 Data Analyst 130000 USD
## 495 Data Analyst 61300 USD
## 496 Machine Learning Developer 100000 CAD
## 497 Machine Learning Developer 100000 CAD
## 498 Data Scientist 88000 CAD
## 499 Data Scientist 4000 USD
## 500 AI Scientist 1335000 INR
## 501 Data Engineer 108000 TRY
## 502 Data Scientist 2100000 INR
## 503 Business Data Analyst 1400000 INR
## 504 Data Scientist 1400000 INR
## 505 Big Data Architect 125000 CAD
## 506 Data Scientist 21600 EUR
## 507 Lead Data Engineer 160000 USD
## 508 Data Engineer 250000 TRY
## 509 Computer Vision Engineer 102000 BRL
## 510 Machine Learning Engineer 85000 USD
## 511 Data Scientist 180000 TRY
## 512 Business Data Analyst 50000 EUR
## 513 Research Scientist 100000 USD
## 514 Research Scientist 69999 USD
## 515 Head of Data 87000 EUR
## 516 Machine Learning Engineer 43200 EUR
## 517 Data Scientist 90000 GBP
## 518 Data Scientist 80000 GBP
## 519 Data Scientist 50000 GBP
## 520 Data Scientist 30000 GBP
## 521 Data Engineer 60000 GBP
## 522 Data Engineer 40000 GBP
## 523 Data Engineer 60000 GBP
## 524 Data Engineer 50000 GBP
## 525 Machine Learning Engineer 95000 GBP
## 526 Machine Learning Engineer 75000 GBP
## 527 Data Engineer 90000 GBP
## 528 Data Engineer 75000 GBP
## 529 Data Scientist 55000 GBP
## 530 Data Scientist 35000 GBP
## 531 Data Scientist 140000 GBP
## 532 Data Scientist 70000 GBP
## 533 Data Analyst 50000 GBP
## 534 Data Analyst 35000 GBP
## 535 ETL Developer 50000 EUR
## 536 Machine Learning Engineer 80000 EUR
## 537 Data Science Engineer 60000 USD
## 538 ML Engineer 20000 EUR
## 539 Lead Machine Learning Engineer 80000 EUR
## 540 Machine Learning Engineer 120000 USD
## 541 Machine Learning Engineer 65000 USD
## 542 Data Scientist 100000 USD
## 543 Data Scientist 100000 USD
## 544 Applied Machine Learning Scientist 29000 EUR
## 545 Head of Data 200000 USD
## 546 Data Scientist 100000 USD
## 547 Machine Learning Scientist 153000 USD
## 548 Data Engineer 52800 EUR
## 549 Head of Data 30000 EUR
## 550 Data Scientist 40000 USD
## 551 Machine Learning Engineer 121000 AUD
## 552 Data Scientist 120000 AUD
## 553 Applied Machine Learning Scientist 75000 USD
## 554 Research Scientist 59000 EUR
## 555 Computer Vision Software Engineer 150000 USD
## 556 Machine Learning Engineer 65000 EUR
## 557 Data Analytics Engineer 20000 USD
## 558 Data Engineer 62000 EUR
## 559 Data Scientist 115000 CHF
## 560 Computer Vision Engineer 10000 USD
## 561 Data Analyst 20000 USD
## 562 Data Analyst 85000 USD
## 563 Data Analyst 75000 USD
## 564 Data Analyst 67000 USD
## 565 Data Analyst 52000 USD
## salary_in_usd
## 1 79833
## 2 141846
## 3 76833
## 4 260000
## 5 168000
## 6 109024
## 7 20000
## 8 150000
## 9 250000
## 10 120000
## 11 423000
## 12 147000
## 13 115000
## 14 185000
## 15 130000
## 16 58000
## 17 109000
## 18 37236
## 19 100000
## 20 144000
## 21 72000
## 22 135000
## 23 87000
## 24 85000
## 25 325000
## 26 100000
## 27 70000
## 28 103000
## 29 106000
## 30 188000
## 31 105000
## 32 91000
## 33 110000
## 34 412000
## 35 235000
## 36 270000
## 37 140000
## 38 110000
## 39 170000
## 40 450000
## 41 150000
## 42 225000
## 43 200000
## 44 151000
## 45 135000
## 46 100000
## 47 90000
## 48 153000
## 49 144000
## 50 165000
## 51 80000
## 52 150000
## 53 170000
## 54 235000
## 55 174000
## 56 174000
## 57 160000
## 58 200000
## 59 185000
## 60 140000
## 61 200000
## 62 200000
## 63 180000
## 64 80000
## 65 110000
## 66 165000
## 67 600000
## 68 93000
## 69 72500
## 70 112000
## 71 215300
## 72 158200
## 73 209100
## 74 154600
## 75 183600
## 76 160000
## 77 112300
## 78 100800
## 79 120000
## 80 157000
## 81 380000
## 82 405000
## 83 135000
## 84 177000
## 85 190000
## 86 138000
## 87 105000
## 88 115000
## 89 90000
## 90 82500
## 91 125000
## 92 60000
## 93 90000
## 94 416000
## 95 256000
## 96 90000
## 97 120000
## 98 65000
## 99 35735
## 100 125000
## 101 51321
## 102 40481
## 103 22611
## 104 66265
## 105 39916
## 106 8000
## 107 41689
## 108 114047
## 109 5707
## 110 5409
## 111 21637
## 112 56000
## 113 43331
## 114 6072
## 115 19609
## 116 30428
## 117 24342
## 118 16228
## 119 31615
## 120 47899
## 121 98000
## 122 450000
## 123 165000
## 124 93150
## 125 111775
## 126 130000
## 127 90000
## 128 99000
## 129 116000
## 130 106260
## 131 126500
## 132 181940
## 133 132320
## 134 220110
## 135 160080
## 136 108800
## 137 95550
## 138 150000
## 139 136000
## 140 189650
## 141 164996
## 142 132000
## 143 164000
## 144 120000
## 145 115934
## 146 81666
## 147 63900
## 148 180000
## 149 80000
## 150 82900
## 151 125000
## 152 141300
## 153 102100
## 154 206699
## 155 99100
## 156 70500
## 157 205300
## 158 140400
## 159 205300
## 160 184700
## 161 54000
## 162 129000
## 163 115000
## 164 42000
## 165 33511
## 166 2859
## 167 117104
## 168 127221
## 169 96113
## 170 196979
## 171 70912
## 172 59303
## 173 15966
## 174 76833
## 175 68428
## 176 46759
## 177 70139
## 178 53192
## 179 36643
## 180 93427
## 181 74130
## 182 10000
## 183 45760
## 184 79833
## 185 50180
## 186 112872
## 187 82528
## 188 51519
## 189 61896
## 190 116914
## 191 56256
## 192 72212
## 193 96282
## 194 76958
## 195 103160
## 196 6072
## 197 9466
## 198 45896
## 199 28609
## 200 54742
## 201 60000
## 202 48000
## 203 148261
## 204 173762
## 205 162674
## 206 38776
## 207 46809
## 208 49461
## 209 43966
## 210 32974
## 211 87932
## 212 76940
## 213 65949
## 214 118000
## 215 138350
## 216 190200
## 217 100000
## 218 80000
## 219 70000
## 220 150000
## 221 200000
## 222 50000
## 223 90000
## 224 80000
## 225 150000
## 226 120000
## 227 195000
## 228 170000
## 229 100000
## 230 105000
## 231 135000
## 232 155000
## 233 120600
## 234 170000
## 235 150000
## 236 102100
## 237 84900
## 238 136620
## 239 99360
## 240 146000
## 241 123000
## 242 242000
## 243 200000
## 244 165220
## 245 120160
## 246 90320
## 247 124190
## 248 130000
## 249 110000
## 250 170000
## 251 115500
## 252 112900
## 253 165400
## 254 132320
## 255 167000
## 256 243900
## 257 136600
## 258 109280
## 259 128875
## 260 93700
## 261 224000
## 262 167875
## 263 175000
## 264 156600
## 265 135000
## 266 161342
## 267 137141
## 268 167000
## 269 211500
## 270 138600
## 271 135000
## 272 170000
## 273 208775
## 274 147800
## 275 136994
## 276 101570
## 277 128875
## 278 93700
## 279 155000
## 280 145000
## 281 105400
## 282 175000
## 283 180000
## 284 260000
## 285 241000
## 286 159000
## 287 180000
## 288 80000
## 289 105000
## 290 120000
## 291 104890
## 292 100000
## 293 140000
## 294 135000
## 295 50000
## 296 220000
## 297 185100
## 298 220000
## 299 200000
## 300 120000
## 301 324000
## 302 216000
## 303 210000
## 304 120000
## 305 230000
## 306 165000
## 307 115000
## 308 152500
## 309 78000
## 310 100000
## 311 214000
## 312 192600
## 313 266400
## 314 213120
## 315 115934
## 316 81666
## 317 130000
## 318 110500
## 319 99050
## 320 160000
## 321 176000
## 322 144000
## 323 200100
## 324 145000
## 325 175100
## 326 140250
## 327 116150
## 328 80000
## 329 210000
## 330 69000
## 331 150075
## 332 25000
## 333 126500
## 334 106260
## 335 220110
## 336 160080
## 337 105000
## 338 110925
## 339 60000
## 340 192564
## 341 144854
## 342 150000
## 343 150260
## 344 109280
## 345 160000
## 346 130000
## 347 154000
## 348 126000
## 349 150000
## 350 130800
## 351 21669
## 352 45618
## 353 62726
## 354 49268
## 355 91237
## 356 62726
## 357 42197
## 358 56738
## 359 150000
## 360 200000
## 361 79197
## 362 54238
## 363 47282
## 364 153667
## 365 28476
## 366 46597
## 367 35590
## 368 59102
## 369 49646
## 370 62649
## 371 77684
## 372 80000
## 373 88654
## 374 276000
## 375 75000
## 376 62000
## 377 73000
## 378 220000
## 379 240000
## 380 150000
## 381 250000
## 382 120000
## 383 135000
## 384 180000
## 385 120000
## 386 113000
## 387 160000
## 388 140400
## 389 215300
## 390 205300
## 391 29751
## 392 40570
## 393 94665
## 394 79039
## 395 89294
## 396 12000
## 397 160000
## 398 95746
## 399 36259
## 400 187442
## 401 75774
## 402 103691
## 403 52396
## 404 94564
## 405 63831
## 406 90734
## 407 88654
## 408 63831
## 409 54957
## 410 113476
## 411 52351
## 412 45807
## 413 52351
## 414 78526
## 415 58894
## 416 39263
## 417 104702
## 418 91614
## 419 98158
## 420 12000
## 421 45391
## 422 69741
## 423 62651
## 424 50000
## 425 40189
## 426 65949
## 427 49461
## 428 87932
## 429 43966
## 430 32974
## 431 76940
## 432 60000
## 433 110037
## 434 10354
## 435 120000
## 436 55000
## 437 81000
## 438 20000
## 439 16904
## 440 100000
## 441 38400
## 442 24000
## 443 100000
## 444 110000
## 445 58000
## 446 63711
## 447 77364
## 448 74000
## 449 82744
## 450 13400
## 451 119059
## 452 230000
## 453 85000
## 454 28369
## 455 130026
## 456 24823
## 457 65013
## 458 82528
## 459 37300
## 460 60757
## 461 58255
## 462 40038
## 463 5679
## 464 26005
## 465 12000
## 466 4000
## 467 50000
## 468 69741
## 469 76833
## 470 152000
## 471 21844
## 472 18000
## 473 9272
## 474 54094
## 475 61467
## 476 37825
## 477 55000
## 478 50000
## 479 12901
## 480 20000
## 481 24823
## 482 66022
## 483 5882
## 484 88654
## 485 33808
## 486 225000
## 487 87738
## 488 118187
## 489 75000
## 490 71786
## 491 63810
## 492 192400
## 493 90700
## 494 130000
## 495 61300
## 496 78791
## 497 78791
## 498 69336
## 499 4000
## 500 18053
## 501 12103
## 502 28399
## 503 18442
## 504 18442
## 505 99703
## 506 25532
## 507 160000
## 508 28016
## 509 18907
## 510 85000
## 511 20171
## 512 59102
## 513 100000
## 514 69999
## 515 102839
## 516 51064
## 517 117789
## 518 104702
## 519 65438
## 520 39263
## 521 78526
## 522 52351
## 523 78526
## 524 65438
## 525 124333
## 526 98158
## 527 117789
## 528 98158
## 529 71982
## 530 45807
## 531 183228
## 532 91614
## 533 65438
## 534 45807
## 535 54957
## 536 87932
## 537 60000
## 538 21983
## 539 87932
## 540 120000
## 541 65000
## 542 100000
## 543 100000
## 544 31875
## 545 200000
## 546 100000
## 547 153000
## 548 58035
## 549 32974
## 550 40000
## 551 87425
## 552 86703
## 553 75000
## 554 64849
## 555 150000
## 556 71444
## 557 20000
## 558 68147
## 559 122346
## 560 10000
## 561 20000
## 562 85000
## 563 75000
## 564 67000
## 565 52000Data Exploration and Analysis
We checked for missing and duplicate values. As shown below in the results, there were no missing or duplicate values.
## 'data.frame': 565 obs. of 14 variables:
## $ company_id : int 1 1 1 2 2 3 4 5 5 5 ...
## $ employee_residence: chr "DE" "DE" "DE" "JP" ...
## $ remote_ratio : int 0 0 0 0 0 50 0 50 50 50 ...
## $ company_location : chr "DE" "DE" "DE" "JP" ...
## $ company_size : chr "L" "L" "L" "S" ...
## $ cid : chr "DE_0_DE_L" "DE_0_DE_L" "DE_0_DE_L" "JP_0_JP_S" ...
## $ job_title_id : int 1 258 271 2 151 3 4 5 38 59 ...
## $ work_year : int 2020 2021 2021 2020 2021 2020 2020 2020 2020 2020 ...
## $ experience_level : chr "MI" "EX" "EN" "SE" ...
## $ employment_type : chr "FT" "FT" "FT" "FT" ...
## $ job_title : chr "Data Scientist" "Director of Data Science" "Data Science Consultant" "Machine Learning Scientist" ...
## $ salary : int 70000 120000 65000 260000 168000 85000 20000 150000 250000 120000 ...
## $ salary_currency : chr "EUR" "EUR" "EUR" "USD" ...
## $ salary_in_usd : int 79833 141846 76833 260000 168000 109024 20000 150000 250000 120000 ...## company_id employee_residence remote_ratio company_location
## Min. : 1.00 Length:565 Min. : 0.00 Length:565
## 1st Qu.: 21.00 Class :character 1st Qu.: 50.00 Class :character
## Median : 42.00 Mode :character Median :100.00 Mode :character
## Mean : 52.98 Mean : 69.91
## 3rd Qu.: 71.00 3rd Qu.:100.00
## Max. :161.00 Max. :100.00
## company_size cid job_title_id work_year
## Length:565 Length:565 Min. : 1 Min. :2020
## Class :character Class :character 1st Qu.:142 1st Qu.:2021
## Mode :character Mode :character Median :283 Median :2021
## Mean :283 Mean :2021
## 3rd Qu.:424 3rd Qu.:2022
## Max. :565 Max. :2022
## experience_level employment_type job_title salary
## Length:565 Length:565 Length:565 Min. : 4000
## Class :character Class :character Class :character 1st Qu.: 67000
## Mode :character Mode :character Mode :character Median : 110925
## Mean : 338116
## 3rd Qu.: 165000
## Max. :30400000
## salary_currency salary_in_usd
## Length:565 Min. : 2859
## Class :character 1st Qu.: 60757
## Mode :character Median :100000
## Mean :110610
## 3rd Qu.:150000
## Max. :600000## [1] 0## [1] "company_id" "employee_residence" "remote_ratio"
## [4] "company_location" "company_size" "cid"
## [7] "job_title_id" "work_year" "experience_level"
## [10] "employment_type" "job_title" "salary"
## [13] "salary_currency" "salary_in_usd"To verify duplicate values in the dataset, I used the duplicated() function. This creates a new dataframe displaying any duplication values. I also used the sum function.
num_duplicates <- sum(duplicated(total_df))
# Check for duplicates
duplicates <- total_df[duplicated(total_df), ]
print(duplicates)## [1] company_id employee_residence remote_ratio company_location
## [5] company_size cid job_title_id work_year
## [9] experience_level employment_type job_title salary
## [13] salary_currency salary_in_usd
## <0 rows> (or 0-length row.names)There are no duplicate values in the dataset.
Extract Relevant Skills
I extracted certain skills based on job titles including Data Scientist, Data Analyst and Machine Learning Engineer which would imply skills relevant to data science.
# Analysis
# Extract relevant skills (based on job titles)
data_science_roles <- c("Data Scientist", "Data Analyst", "Machine Learning Engineer")
data_science_data <- total_df[total_df$job_title %in% data_science_roles, ]
print(data_science_data)## company_id employee_residence remote_ratio company_location company_size
## 1 1 DE 0 DE L
## 8 5 US 50 US L
## 9 5 US 50 US L
## 10 5 US 50 US L
## 12 5 US 50 US L
## 13 5 US 50 US L
## 14 5 US 50 US L
## 15 5 US 50 US L
## 16 5 US 50 US L
## 17 5 US 50 US L
## 21 6 US 100 US L
## 24 6 US 100 US L
## 31 6 US 100 US L
## 32 6 US 100 US L
## 34 6 US 100 US L
## 45 6 US 100 US L
## 57 6 US 100 US L
## 58 6 US 100 US L
## 62 6 US 100 US L
## 64 6 US 100 US L
## 66 6 US 100 US L
## 68 6 US 100 US L
## 71 6 US 100 US L
## 72 6 US 100 US L
## 83 6 US 100 US L
## 86 7 US 100 US S
## 87 7 US 100 US S
## 89 7 US 100 US S
## 90 7 US 100 US S
## 91 7 US 100 US S
## 92 7 US 100 US S
## 96 7 US 100 US S
## 97 7 US 100 US S
## 99 8 HU 50 HU L
## 101 10 FR 0 FR S
## 102 11 IN 0 IN L
## 104 11 IN 0 IN L
## 105 12 FR 0 FR M
## 106 13 PK 50 PK L
## 113 18 CN 0 CN M
## 117 19 IN 100 IN L
## 119 19 IN 100 IN L
## 126 21 US 0 US M
## 127 21 US 0 US M
## 128 21 US 0 US M
## 129 21 US 0 US M
## 130 21 US 0 US M
## 131 21 US 0 US M
## 137 21 US 0 US M
## 138 21 US 0 US M
## 140 21 US 0 US M
## 141 21 US 0 US M
## 142 21 US 0 US M
## 143 21 US 0 US M
## 145 21 US 0 US M
## 146 21 US 0 US M
## 148 21 US 0 US M
## 149 21 US 0 US M
## 152 21 US 0 US M
## 153 21 US 0 US M
## 157 21 US 0 US M
## 158 21 US 0 US M
## 162 21 US 0 US M
## 166 24 MX 0 MX S
## 175 27 GR 100 US L
## 176 28 FR 50 FR L
## 178 28 FR 50 FR L
## 179 28 FR 50 FR L
## 182 30 NG 100 NG S
## 183 31 PH 100 US S
## 188 34 GB 50 GB L
## 190 34 GB 50 GB L
## 191 34 GB 50 GB L
## 194 35 GB 100 GB S
## 196 36 IN 0 IN S
## 197 36 IN 0 IN S
## 202 39 RU 100 US S
## 206 41 ES 100 ES M
## 207 41 ES 100 ES M
## 209 41 ES 100 ES M
## 210 41 ES 100 ES M
## 214 42 US 100 US M
## 215 42 US 100 US M
## 218 42 US 100 US M
## 222 42 US 100 US M
## 223 42 US 100 US M
## 224 42 US 100 US M
## 225 42 US 100 US M
## 229 42 US 100 US M
## 232 42 US 100 US M
## 233 42 US 100 US M
## 236 42 US 100 US M
## 237 42 US 100 US M
## 238 42 US 100 US M
## 239 42 US 100 US M
## 240 42 US 100 US M
## 241 42 US 100 US M
## 244 42 US 100 US M
## 245 42 US 100 US M
## 246 42 US 100 US M
## 247 42 US 100 US M
## 248 42 US 100 US M
## 249 42 US 100 US M
## 250 42 US 100 US M
## 251 42 US 100 US M
## 252 42 US 100 US M
## 255 42 US 100 US M
## 257 42 US 100 US M
## 258 42 US 100 US M
## 265 42 US 100 US M
## 268 42 US 100 US M
## 269 42 US 100 US M
## 270 42 US 100 US M
## 272 42 US 100 US M
## 277 42 US 100 US M
## 278 42 US 100 US M
## 283 42 US 100 US M
## 284 42 US 100 US M
## 291 42 US 100 US M
## 293 42 US 100 US M
## 294 42 US 100 US M
## 295 42 US 100 US M
## 296 42 US 100 US M
## 297 42 US 100 US M
## 298 42 US 100 US M
## 299 42 US 100 US M
## 300 42 US 100 US M
## 304 42 US 100 US M
## 305 42 US 100 US M
## 306 42 US 100 US M
## 309 42 US 100 US M
## 310 42 US 100 US M
## 311 42 US 100 US M
## 312 42 US 100 US M
## 315 42 US 100 US M
## 316 42 US 100 US M
## 319 42 US 100 US M
## 321 42 US 100 US M
## 322 42 US 100 US M
## 327 42 US 100 US M
## 328 42 US 100 US M
## 329 42 US 100 US M
## 330 42 US 100 US M
## 331 42 US 100 US M
## 333 42 US 100 US M
## 334 42 US 100 US M
## 337 42 US 100 US M
## 338 42 US 100 US M
## 339 42 US 100 US M
## 342 42 US 100 US M
## 345 42 US 100 US M
## 346 42 US 100 US M
## 349 42 US 100 US M
## 351 44 IT 50 IT S
## 352 45 HR 100 HR S
## 353 46 DE 50 DE S
## 354 47 DE 0 DE S
## 355 48 AT 0 AT S
## 356 49 FR 50 LU S
## 357 50 FR 50 FR S
## 363 54 ES 100 ES S
## 366 56 PL 100 PL L
## 367 56 PL 100 PL L
## 368 57 FR 50 FR M
## 369 57 FR 50 FR M
## 371 57 FR 50 FR M
## 372 58 BG 100 US S
## 375 60 US 0 US L
## 376 60 US 0 US L
## 377 60 US 0 US L
## 383 60 US 0 US L
## 384 60 US 0 US L
## 385 60 US 0 US L
## 388 60 US 0 US L
## 389 60 US 0 US L
## 390 60 US 0 US L
## 391 61 IN 50 IN L
## 401 66 CA 100 CA L
## 402 66 CA 100 CA L
## 403 66 CA 100 CA L
## 404 67 DE 50 DE L
## 406 67 DE 50 DE L
## 407 67 DE 50 DE L
## 408 67 DE 50 DE L
## 413 68 GB 100 GB M
## 416 68 GB 100 GB M
## 423 70 NL 100 NL L
## 424 71 NG 100 NG L
## 429 72 GR 100 GR M
## 430 72 GR 100 GR M
## 434 75 ES 50 ES M
## 437 76 US 50 US S
## 438 77 IN 100 IN S
## 439 77 IN 100 IN S
## 443 81 US 0 US S
## 445 81 US 0 US S
## 448 82 JP 50 JP S
## 449 83 BE 50 BE M
## 450 84 UA 100 UA L
## 451 85 SG 100 IL M
## 456 89 DE 50 DE M
## 459 90 GB 100 GB L
## 462 92 CL 100 CL L
## 463 93 IN 100 US S
## 471 101 CO 50 CO M
## 475 105 DE 50 AT M
## 476 106 ES 100 ES L
## 479 108 BR 0 BR S
## 481 110 SI 50 SI L
## 484 113 BE 100 BE M
## 485 114 IN 0 IN M
## 487 116 CA 100 CA S
## 490 117 CA 100 CA M
## 494 117 CA 100 CA M
## 495 117 CA 100 CA M
## 498 117 CA 100 CA M
## 499 118 VN 0 VN M
## 502 121 IN 100 IN M
## 504 121 IN 100 IN M
## 506 123 RS 100 DE S
## 510 127 NL 100 DE S
## 511 128 TR 50 TR L
## 516 133 IT 50 IT L
## 517 134 GB 0 GB M
## 518 134 GB 0 GB M
## 519 134 GB 0 GB M
## 520 134 GB 0 GB M
## 525 134 GB 0 GB M
## 526 134 GB 0 GB M
## 529 134 GB 0 GB M
## 530 134 GB 0 GB M
## 531 134 GB 0 GB M
## 532 134 GB 0 GB M
## 533 134 GB 0 GB M
## 534 134 GB 0 GB M
## 536 136 FR 100 DE M
## 540 140 AE 100 AE S
## 541 140 AE 100 AE S
## 542 141 DZ 50 DZ M
## 543 142 CA 100 US M
## 546 145 BR 100 US M
## 550 149 JP 100 MY L
## 551 150 AU 100 AU L
## 552 151 AU 50 AU M
## 556 155 IE 100 IE S
## 559 158 CH 0 CH L
## 561 160 GR 100 GR S
## 562 161 CA 0 CA M
## 563 161 CA 0 CA M
## 564 161 CA 0 CA M
## 565 161 CA 0 CA M
## cid job_title_id work_year experience_level employment_type
## 1 DE_0_DE_L 1 2020 MI FT
## 8 US_50_US_L 5 2020 SE FT
## 9 US_50_US_L 38 2020 EN FT
## 10 US_50_US_L 59 2020 SE FT
## 12 US_50_US_L 196 2021 MI FT
## 13 US_50_US_L 250 2021 MI FT
## 14 US_50_US_L 257 2021 SE FT
## 15 US_50_US_L 259 2021 MI FT
## 16 US_50_US_L 275 2021 EN FT
## 17 US_50_US_L 286 2021 MI FT
## 21 US_100_US_L 6 2020 EN FT
## 24 US_100_US_L 15 2020 MI FT
## 31 US_100_US_L 49 2020 MI FT
## 32 US_100_US_L 52 2020 EN FT
## 34 US_100_US_L 64 2020 SE FT
## 45 US_100_US_L 141 2021 MI FT
## 57 US_100_US_L 205 2021 MI FT
## 58 US_100_US_L 207 2021 SE FT
## 62 US_100_US_L 233 2021 SE FT
## 64 US_100_US_L 241 2021 MI FT
## 66 US_100_US_L 243 2021 SE FT
## 68 US_100_US_L 254 2021 MI FT
## 71 US_100_US_L 385 2022 SE FT
## 72 US_100_US_L 386 2022 SE FT
## 83 US_100_US_L 504 2022 MI FT
## 86 US_100_US_S 40 2020 EN FT
## 87 US_100_US_S 69 2020 EN FT
## 89 US_100_US_S 119 2021 EN FT
## 90 US_100_US_S 144 2021 MI FT
## 91 US_100_US_S 160 2021 EN FT
## 92 US_100_US_S 173 2021 EN FT
## 96 US_100_US_S 251 2021 EN FT
## 97 US_100_US_S 434 2022 MI FT
## 99 HU_50_HU_L 8 2020 MI FT
## 101 FR_0_FR_S 11 2020 EN FT
## 102 IN_0_IN_L 12 2020 MI FT
## 104 IN_0_IN_L 262 2021 SE FT
## 105 FR_0_FR_M 13 2020 EN FT
## 106 PK_50_PK_L 16 2020 MI FT
## 113 CN_0_CN_M 21 2020 MI FT
## 117 IN_100_IN_L 198 2021 SE FT
## 119 IN_100_IN_L 440 2022 MI FT
## 126 US_0_US_M 291 2022 MI FT
## 127 US_0_US_M 292 2022 MI FT
## 128 US_0_US_M 304 2022 SE FT
## 129 US_0_US_M 305 2022 SE FT
## 130 US_0_US_M 306 2022 MI FT
## 131 US_0_US_M 307 2022 MI FT
## 137 US_0_US_M 343 2022 SE FT
## 138 US_0_US_M 351 2022 SE FT
## 140 US_0_US_M 363 2022 SE FT
## 141 US_0_US_M 364 2022 SE FT
## 142 US_0_US_M 367 2022 SE FT
## 143 US_0_US_M 376 2022 SE FT
## 145 US_0_US_M 389 2022 SE FT
## 146 US_0_US_M 390 2022 SE FT
## 148 US_0_US_M 409 2022 SE FT
## 149 US_0_US_M 410 2022 SE FT
## 152 US_0_US_M 514 2022 MI FT
## 153 US_0_US_M 515 2022 MI FT
## 157 US_0_US_M 530 2022 SE FT
## 158 US_0_US_M 531 2022 SE FT
## 162 US_0_US_M 563 2022 SE FT
## 166 MX_0_MX_S 177 2021 MI FT
## 175 GR_100_US_L 33 2020 SE FT
## 176 FR_50_FR_L 35 2020 MI FT
## 178 FR_50_FR_L 76 2021 SE FT
## 179 FR_50_FR_L 246 2021 EN FT
## 182 NG_100_NG_S 39 2020 EN FT
## 183 PH_100_US_S 41 2020 MI FT
## 188 GB_50_GB_L 106 2021 MI FT
## 190 GB_50_GB_L 221 2021 MI FT
## 191 GB_50_GB_L 223 2021 MI FT
## 194 GB_100_GB_S 47 2020 MI FT
## 196 IN_0_IN_S 51 2020 EN FT
## 197 IN_0_IN_S 128 2021 MI FT
## 202 RU_100_US_S 495 2022 MI FT
## 206 ES_100_ES_M 57 2020 MI FT
## 207 ES_100_ES_M 237 2021 MI FT
## 209 ES_100_ES_M 416 2022 MI FT
## 210 ES_100_ES_M 417 2022 MI FT
## 214 US_100_US_M 58 2020 MI FT
## 215 US_100_US_M 60 2020 MI FT
## 218 US_100_US_M 80 2021 EN FT
## 222 US_100_US_M 123 2021 EN FT
## 223 US_100_US_M 136 2021 MI FT
## 224 US_100_US_M 140 2021 EN FT
## 225 US_100_US_M 152 2021 MI FT
## 229 US_100_US_M 274 2021 EN FT
## 232 US_100_US_M 289 2022 SE FT
## 233 US_100_US_M 290 2022 SE FT
## 236 US_100_US_M 295 2022 SE FT
## 237 US_100_US_M 296 2022 SE FT
## 238 US_100_US_M 297 2022 SE FT
## 239 US_100_US_M 298 2022 SE FT
## 240 US_100_US_M 301 2022 SE FT
## 241 US_100_US_M 302 2022 SE FT
## 244 US_100_US_M 314 2022 SE FT
## 245 US_100_US_M 316 2022 SE FT
## 246 US_100_US_M 317 2022 SE FT
## 247 US_100_US_M 324 2022 SE FT
## 248 US_100_US_M 325 2022 EX FT
## 249 US_100_US_M 326 2022 EX FT
## 250 US_100_US_M 327 2022 SE FT
## 251 US_100_US_M 328 2022 MI FT
## 252 US_100_US_M 329 2022 SE FT
## 255 US_100_US_M 332 2022 MI FT
## 257 US_100_US_M 334 2022 SE FT
## 258 US_100_US_M 335 2022 SE FT
## 265 US_100_US_M 345 2022 SE FT
## 268 US_100_US_M 348 2022 SE FT
## 269 US_100_US_M 352 2022 SE FT
## 270 US_100_US_M 358 2022 SE FT
## 272 US_100_US_M 362 2022 SE FT
## 277 US_100_US_M 372 2022 SE FT
## 278 US_100_US_M 373 2022 SE FT
## 283 US_100_US_M 395 2022 SE FT
## 284 US_100_US_M 402 2022 SE FT
## 291 US_100_US_M 448 2022 SE FT
## 293 US_100_US_M 450 2022 SE FT
## 294 US_100_US_M 451 2022 MI FT
## 295 US_100_US_M 452 2022 MI FT
## 296 US_100_US_M 453 2022 SE FT
## 297 US_100_US_M 456 2022 SE FT
## 298 US_100_US_M 457 2022 SE FT
## 299 US_100_US_M 458 2022 MI FT
## 300 US_100_US_M 459 2022 MI FT
## 304 US_100_US_M 465 2022 SE FT
## 305 US_100_US_M 466 2022 SE FT
## 306 US_100_US_M 477 2022 SE FT
## 309 US_100_US_M 506 2022 MI FT
## 310 US_100_US_M 507 2022 SE FT
## 311 US_100_US_M 510 2022 SE FT
## 312 US_100_US_M 511 2022 SE FT
## 315 US_100_US_M 516 2022 SE FT
## 316 US_100_US_M 517 2022 SE FT
## 319 US_100_US_M 522 2022 SE FT
## 321 US_100_US_M 525 2022 SE FT
## 322 US_100_US_M 526 2022 SE FT
## 327 US_100_US_M 536 2022 SE FT
## 328 US_100_US_M 539 2022 SE FT
## 329 US_100_US_M 540 2022 SE FT
## 330 US_100_US_M 541 2022 SE FT
## 331 US_100_US_M 542 2022 SE FT
## 333 US_100_US_M 544 2022 SE FT
## 334 US_100_US_M 545 2022 SE FT
## 337 US_100_US_M 548 2022 SE FT
## 338 US_100_US_M 549 2022 SE FT
## 339 US_100_US_M 551 2022 SE FT
## 342 US_100_US_M 554 2022 SE FT
## 345 US_100_US_M 557 2022 MI FT
## 346 US_100_US_M 558 2022 MI FT
## 349 US_100_US_M 564 2022 SE FT
## 351 IT_50_IT_S 63 2020 EN PT
## 352 HR_100_HR_S 65 2020 SE FT
## 353 DE_50_DE_S 66 2020 EN FT
## 354 DE_0_DE_S 67 2020 EN FT
## 355 AT_0_AT_S 70 2020 SE FT
## 356 FR_50_LU_S 71 2020 MI FT
## 357 FR_50_FR_S 72 2020 MI FT
## 363 ES_100_ES_S 84 2021 MI FT
## 366 PL_100_PL_L 220 2021 MI FT
## 367 PL_100_PL_L 472 2022 MI FT
## 368 FR_50_FR_M 87 2021 EN FT
## 369 FR_50_FR_M 132 2021 EN FT
## 371 FR_50_FR_M 273 2021 SE FT
## 372 BG_100_US_S 90 2021 SE FT
## 375 US_0_US_L 101 2021 MI FT
## 376 US_0_US_L 104 2021 MI FT
## 377 US_0_US_L 105 2021 MI FT
## 383 US_0_US_L 228 2021 SE FT
## 384 US_0_US_L 322 2022 SE FT
## 385 US_0_US_L 323 2022 SE FT
## 388 US_0_US_L 414 2022 SE FT
## 389 US_0_US_L 427 2022 SE FT
## 390 US_0_US_L 524 2022 SE FT
## 391 IN_50_IN_L 95 2021 EN FT
## 401 CA_100_CA_L 153 2021 MI FT
## 402 CA_100_CA_L 240 2021 SE FT
## 403 CA_100_CA_L 479 2022 EN FT
## 404 DE_50_DE_L 111 2021 SE FT
## 406 DE_50_DE_L 182 2021 MI FT
## 407 DE_50_DE_L 227 2021 MI FT
## 408 DE_50_DE_L 260 2021 SE FT
## 413 GB_100_GB_M 394 2022 MI FT
## 416 GB_100_GB_M 415 2022 MI FT
## 423 NL_100_NL_L 480 2022 SE FT
## 424 NG_100_NG_L 117 2021 MI FT
## 429 GR_100_GR_M 424 2022 MI FT
## 430 GR_100_GR_M 425 2022 MI FT
## 434 ES_50_ES_M 125 2021 EN PT
## 437 US_50_US_S 179 2021 EN FT
## 438 IN_100_IN_S 129 2021 EN FT
## 439 IN_100_IN_S 261 2021 MI FT
## 443 US_0_US_S 135 2021 EN FT
## 445 US_0_US_S 360 2022 MI FT
## 448 JP_50_JP_S 190 2021 MI FT
## 449 BE_50_BE_M 146 2021 SE FT
## 450 UA_100_UA_L 154 2021 EN FT
## 451 SG_100_IL_M 157 2021 MI FT
## 456 DE_50_DE_M 215 2021 EN FT
## 459 GB_100_GB_L 375 2022 EN FT
## 462 CL_100_CL_L 178 2021 MI FT
## 463 IN_100_US_S 180 2021 MI FT
## 471 CO_50_CO_M 192 2021 EN FT
## 475 DE_50_AT_M 201 2021 MI FT
## 476 ES_100_ES_L 203 2021 MI FT
## 479 BR_0_BR_S 206 2021 MI FT
## 481 SI_50_SI_L 211 2021 MI FT
## 484 BE_100_BE_M 218 2021 MI FT
## 485 IN_0_IN_M 222 2021 MI FT
## 487 CA_100_CA_S 226 2021 SE FT
## 490 CA_100_CA_M 229 2021 SE FT
## 494 CA_100_CA_M 355 2022 SE FT
## 495 CA_100_CA_M 356 2022 SE FT
## 498 CA_100_CA_M 500 2022 MI FT
## 499 VN_0_VN_M 238 2021 EN FT
## 502 IN_100_IN_M 253 2021 EN FT
## 504 IN_100_IN_M 444 2022 EN FT
## 506 RS_100_DE_S 263 2021 MI FT
## 510 NL_100_DE_S 272 2021 EN FT
## 511 TR_50_TR_L 277 2021 SE FT
## 516 IT_50_IT_L 287 2021 MI FT
## 517 GB_0_GB_M 299 2022 SE FT
## 518 GB_0_GB_M 300 2022 SE FT
## 519 GB_0_GB_M 310 2022 MI FT
## 520 GB_0_GB_M 311 2022 MI FT
## 525 GB_0_GB_M 378 2022 MI FT
## 526 GB_0_GB_M 379 2022 MI FT
## 529 GB_0_GB_M 396 2022 MI FT
## 530 GB_0_GB_M 397 2022 MI FT
## 531 GB_0_GB_M 454 2022 MI FT
## 532 GB_0_GB_M 455 2022 MI FT
## 533 GB_0_GB_M 538 2022 MI FT
## 534 GB_0_GB_M 550 2022 MI FT
## 536 FR_100_DE_M 383 2022 MI FT
## 540 AE_100_AE_S 460 2022 SE FT
## 541 AE_100_AE_S 461 2022 SE FT
## 542 DZ_50_DZ_M 467 2022 EN PT
## 543 CA_100_US_M 468 2022 MI FL
## 546 BR_100_US_M 474 2022 SE FT
## 550 JP_100_MY_L 482 2022 EN FT
## 551 AU_100_AU_L 483 2022 MI FT
## 552 AU_50_AU_M 485 2022 EN FT
## 556 IE_100_IE_S 493 2022 SE FT
## 559 CH_0_CH_L 498 2022 MI FT
## 561 GR_100_GR_S 502 2022 MI FT
## 562 CA_0_CA_M 508 2022 MI FT
## 563 CA_0_CA_M 509 2022 MI FT
## 564 CA_0_CA_M 559 2022 EN FT
## 565 CA_0_CA_M 560 2022 EN FT
## job_title salary salary_currency salary_in_usd
## 1 Data Scientist 70000 EUR 79833
## 8 Machine Learning Engineer 150000 USD 150000
## 9 Machine Learning Engineer 250000 USD 250000
## 10 Data Scientist 120000 USD 120000
## 12 Data Scientist 147000 USD 147000
## 13 Data Scientist 115000 USD 115000
## 14 Machine Learning Engineer 185000 USD 185000
## 15 Data Scientist 130000 USD 130000
## 16 Data Scientist 58000 USD 58000
## 17 Data Scientist 109000 USD 109000
## 21 Data Analyst 72000 USD 72000
## 24 Data Analyst 85000 USD 85000
## 31 Data Scientist 105000 USD 105000
## 32 Data Analyst 91000 USD 91000
## 34 Data Scientist 412000 USD 412000
## 45 Data Analyst 135000 USD 135000
## 57 Data Scientist 160000 USD 160000
## 58 Machine Learning Engineer 200000 USD 200000
## 62 Data Analyst 200000 USD 200000
## 64 Data Analyst 80000 USD 80000
## 66 Data Scientist 165000 USD 165000
## 68 Data Analyst 93000 USD 93000
## 71 Data Scientist 215300 USD 215300
## 72 Data Scientist 158200 USD 158200
## 83 Data Scientist 135000 USD 135000
## 86 Machine Learning Engineer 138000 USD 138000
## 87 Data Scientist 105000 USD 105000
## 89 Data Analyst 90000 USD 90000
## 90 Data Scientist 82500 USD 82500
## 91 Machine Learning Engineer 125000 USD 125000
## 92 Data Analyst 60000 USD 60000
## 96 Data Scientist 90000 USD 90000
## 97 Machine Learning Engineer 120000 USD 120000
## 99 Data Scientist 11000000 HUF 35735
## 101 Data Scientist 45000 EUR 51321
## 102 Data Scientist 3000000 INR 40481
## 104 Machine Learning Engineer 4900000 INR 66265
## 105 Data Scientist 35000 EUR 39916
## 106 Data Analyst 8000 USD 8000
## 113 Machine Learning Engineer 299000 CNY 43331
## 117 Machine Learning Engineer 1799997 INR 24342
## 119 Data Scientist 2400000 INR 31615
## 126 Data Scientist 130000 USD 130000
## 127 Data Scientist 90000 USD 90000
## 128 Data Analyst 99000 USD 99000
## 129 Data Analyst 116000 USD 116000
## 130 Data Analyst 106260 USD 106260
## 131 Data Analyst 126500 USD 126500
## 137 Data Scientist 95550 USD 95550
## 138 Data Scientist 150000 USD 150000
## 140 Machine Learning Engineer 189650 USD 189650
## 141 Machine Learning Engineer 164996 USD 164996
## 142 Data Analyst 132000 USD 132000
## 143 Data Analyst 164000 USD 164000
## 145 Data Analyst 115934 USD 115934
## 146 Data Analyst 81666 USD 81666
## 148 Data Scientist 180000 USD 180000
## 149 Data Scientist 80000 USD 80000
## 152 Data Scientist 141300 USD 141300
## 153 Data Scientist 102100 USD 102100
## 157 Data Scientist 205300 USD 205300
## 158 Data Scientist 140400 USD 140400
## 162 Data Analyst 129000 USD 129000
## 166 Data Scientist 58000 MXN 2859
## 175 Data Scientist 60000 EUR 68428
## 176 Data Analyst 41000 EUR 46759
## 178 Data Scientist 45000 EUR 53192
## 179 Data Scientist 31000 EUR 36643
## 182 Data Analyst 10000 USD 10000
## 183 Data Scientist 45760 USD 45760
## 188 Data Analyst 37456 GBP 51519
## 190 Data Scientist 85000 GBP 116914
## 191 Data Scientist 40900 GBP 56256
## 194 Data Scientist 60000 GBP 76958
## 196 Data Analyst 450000 INR 6072
## 197 Data Scientist 700000 INR 9466
## 202 Data Scientist 48000 USD 48000
## 206 Data Scientist 34000 EUR 38776
## 207 Data Scientist 39600 EUR 46809
## 209 Data Analyst 40000 EUR 43966
## 210 Data Analyst 30000 EUR 32974
## 214 Data Scientist 118000 USD 118000
## 215 Data Scientist 138350 USD 138350
## 218 Data Analyst 80000 USD 80000
## 222 Data Analyst 50000 USD 50000
## 223 Data Analyst 90000 USD 90000
## 224 Data Scientist 80000 USD 80000
## 225 Data Scientist 150000 USD 150000
## 229 Data Scientist 100000 USD 100000
## 232 Data Analyst 155000 USD 155000
## 233 Data Analyst 120600 USD 120600
## 236 Data Analyst 102100 USD 102100
## 237 Data Analyst 84900 USD 84900
## 238 Data Scientist 136620 USD 136620
## 239 Data Scientist 99360 USD 99360
## 240 Data Scientist 146000 USD 146000
## 241 Data Scientist 123000 USD 123000
## 244 Data Scientist 165220 USD 165220
## 245 Data Scientist 120160 USD 120160
## 246 Data Analyst 90320 USD 90320
## 247 Data Analyst 124190 USD 124190
## 248 Data Analyst 130000 USD 130000
## 249 Data Analyst 110000 USD 110000
## 250 Data Analyst 170000 USD 170000
## 251 Data Analyst 115500 USD 115500
## 252 Data Analyst 112900 USD 112900
## 255 Data Analyst 167000 USD 167000
## 257 Data Analyst 136600 USD 136600
## 258 Data Analyst 109280 USD 109280
## 265 Data Analyst 135000 USD 135000
## 268 Data Scientist 167000 USD 167000
## 269 Data Scientist 211500 USD 211500
## 270 Data Scientist 138600 USD 138600
## 272 Data Scientist 170000 USD 170000
## 277 Data Analyst 128875 USD 128875
## 278 Data Analyst 93700 USD 93700
## 283 Data Scientist 180000 USD 180000
## 284 Data Scientist 260000 USD 260000
## 291 Data Scientist 104890 USD 104890
## 293 Data Scientist 140000 USD 140000
## 294 Data Analyst 135000 USD 135000
## 295 Data Analyst 50000 USD 50000
## 296 Data Scientist 220000 USD 220000
## 297 Data Scientist 185100 USD 185100
## 298 Machine Learning Engineer 220000 USD 220000
## 299 Data Scientist 200000 USD 200000
## 300 Data Scientist 120000 USD 120000
## 304 Machine Learning Engineer 120000 USD 120000
## 305 Data Scientist 230000 USD 230000
## 306 Data Scientist 165000 USD 165000
## 309 Data Scientist 78000 USD 78000
## 310 Data Analyst 100000 USD 100000
## 311 Machine Learning Engineer 214000 USD 214000
## 312 Machine Learning Engineer 192600 USD 192600
## 315 Data Analyst 115934 USD 115934
## 316 Data Analyst 81666 USD 81666
## 319 Data Analyst 99050 USD 99050
## 321 Data Scientist 176000 USD 176000
## 322 Data Scientist 144000 USD 144000
## 327 Data Analyst 116150 USD 116150
## 328 Data Analyst 80000 USD 80000
## 329 Data Scientist 210000 USD 210000
## 330 Data Analyst 69000 USD 69000
## 331 Data Analyst 150075 USD 150075
## 333 Data Analyst 126500 USD 126500
## 334 Data Analyst 106260 USD 106260
## 337 Data Analyst 105000 USD 105000
## 338 Data Analyst 110925 USD 110925
## 339 Data Analyst 60000 USD 60000
## 342 Data Scientist 150000 USD 150000
## 345 Data Scientist 160000 USD 160000
## 346 Data Scientist 130000 USD 130000
## 349 Data Analyst 150000 USD 150000
## 351 Data Scientist 19000 EUR 21669
## 352 Machine Learning Engineer 40000 EUR 45618
## 353 Data Scientist 55000 EUR 62726
## 354 Data Scientist 43200 EUR 49268
## 355 Data Scientist 80000 EUR 91237
## 356 Data Scientist 55000 EUR 62726
## 357 Data Scientist 37000 EUR 42197
## 363 Machine Learning Engineer 40000 EUR 47282
## 366 Machine Learning Engineer 180000 PLN 46597
## 367 Data Scientist 150000 PLN 35590
## 368 Data Analyst 50000 EUR 59102
## 369 Data Scientist 42000 EUR 49646
## 371 Data Scientist 65720 EUR 77684
## 372 Data Analyst 80000 USD 80000
## 375 Data Analyst 75000 USD 75000
## 376 Data Analyst 62000 USD 62000
## 377 Data Scientist 73000 USD 73000
## 383 Data Scientist 135000 USD 135000
## 384 Data Scientist 180000 USD 180000
## 385 Data Scientist 120000 USD 120000
## 388 Data Scientist 140400 USD 140400
## 389 Data Scientist 215300 USD 215300
## 390 Data Scientist 205300 USD 205300
## 391 Data Scientist 2200000 INR 29751
## 401 Data Scientist 95000 CAD 75774
## 402 Data Scientist 130000 CAD 103691
## 403 Data Scientist 66500 CAD 52396
## 404 Machine Learning Engineer 80000 EUR 94564
## 406 Data Scientist 76760 EUR 90734
## 407 Data Scientist 75000 EUR 88654
## 408 Data Analyst 54000 EUR 63831
## 413 Data Analyst 40000 GBP 52351
## 416 Data Analyst 30000 GBP 39263
## 423 Machine Learning Engineer 57000 EUR 62651
## 424 Data Scientist 50000 USD 50000
## 429 Data Analyst 40000 EUR 43966
## 430 Data Analyst 30000 EUR 32974
## 434 Data Analyst 8760 EUR 10354
## 437 Machine Learning Engineer 81000 USD 81000
## 438 Machine Learning Engineer 20000 USD 20000
## 439 Data Scientist 1250000 INR 16904
## 443 Data Scientist 100000 USD 100000
## 445 Data Analyst 58000 USD 58000
## 448 Machine Learning Engineer 74000 USD 74000
## 449 Machine Learning Engineer 70000 EUR 82744
## 450 Data Scientist 13400 USD 13400
## 451 Data Scientist 160000 SGD 119059
## 456 Machine Learning Engineer 21000 EUR 24823
## 459 Machine Learning Engineer 28500 GBP 37300
## 462 Data Scientist 30400000 CLP 40038
## 463 Data Scientist 420000 INR 5679
## 471 Machine Learning Engineer 21844 USD 21844
## 475 Data Scientist 52000 EUR 61467
## 476 Data Scientist 32000 EUR 37825
## 479 Data Scientist 69600 BRL 12901
## 481 Machine Learning Engineer 21000 EUR 24823
## 484 Machine Learning Engineer 75000 EUR 88654
## 485 Data Scientist 2500000 INR 33808
## 487 Data Scientist 110000 CAD 87738
## 490 Data Analyst 90000 CAD 71786
## 494 Data Analyst 130000 USD 130000
## 495 Data Analyst 61300 USD 61300
## 498 Data Scientist 88000 CAD 69336
## 499 Data Scientist 4000 USD 4000
## 502 Data Scientist 2100000 INR 28399
## 504 Data Scientist 1400000 INR 18442
## 506 Data Scientist 21600 EUR 25532
## 510 Machine Learning Engineer 85000 USD 85000
## 511 Data Scientist 180000 TRY 20171
## 516 Machine Learning Engineer 43200 EUR 51064
## 517 Data Scientist 90000 GBP 117789
## 518 Data Scientist 80000 GBP 104702
## 519 Data Scientist 50000 GBP 65438
## 520 Data Scientist 30000 GBP 39263
## 525 Machine Learning Engineer 95000 GBP 124333
## 526 Machine Learning Engineer 75000 GBP 98158
## 529 Data Scientist 55000 GBP 71982
## 530 Data Scientist 35000 GBP 45807
## 531 Data Scientist 140000 GBP 183228
## 532 Data Scientist 70000 GBP 91614
## 533 Data Analyst 50000 GBP 65438
## 534 Data Analyst 35000 GBP 45807
## 536 Machine Learning Engineer 80000 EUR 87932
## 540 Machine Learning Engineer 120000 USD 120000
## 541 Machine Learning Engineer 65000 USD 65000
## 542 Data Scientist 100000 USD 100000
## 543 Data Scientist 100000 USD 100000
## 546 Data Scientist 100000 USD 100000
## 550 Data Scientist 40000 USD 40000
## 551 Machine Learning Engineer 121000 AUD 87425
## 552 Data Scientist 120000 AUD 86703
## 556 Machine Learning Engineer 65000 EUR 71444
## 559 Data Scientist 115000 CHF 122346
## 561 Data Analyst 20000 USD 20000
## 562 Data Analyst 85000 USD 85000
## 563 Data Analyst 75000 USD 75000
## 564 Data Analyst 67000 USD 67000
## 565 Data Analyst 52000 USD 52000I used the dplyr library to filter the data and compute the summary statistics including the median, mean, min and max salary by job title. The Data Scientist job title had the highest mean salary among the other job titles based on data science roles.
library(dplyr)
# Filter data for relevant job titles
data_science_roles <- c("Data Scientist", "Data Analyst", "Machine Learning Engineer")
data_science_data <- total_df[total_df$job_title %in% data_science_roles, ]
# Summary statistics of salary by job title
summary_stats <- data_science_data %>%
group_by(job_title) %>%
summarise(
median_salary = median(salary_in_usd),
mean_salary = mean(salary_in_usd),
min_salary = min(salary_in_usd),
max_salary = max(salary_in_usd)
)
print(summary_stats)## # A tibble: 3 × 5
## job_title median_salary mean_salary min_salary max_salary
## <chr> <dbl> <dbl> <int> <int>
## 1 Data Analyst 90000 90090. 6072 200000
## 2 Data Scientist 100000 103336. 2859 412000
## 3 Machine Learning Engineer 87425 101165. 20000 250000Salary Distributions based on Job Title, Work Experience & Location
We created visualizations to display the summary statistics of salary by job title using ggplot2. Below is a boxplot where each box represents the distribution of salaries for each job title. It provides a visual comparison of the median, quartiles, and potential outliers for each job title’s salary distribution.
# Boxplot visualization with color and removed scientific notation
boxplot <- ggplot(data_science_data, aes(x = job_title, y = salary_in_usd, fill = job_title)) +
geom_boxplot() +
scale_y_continuous(labels = scales::comma) + # Remove scientific notation
labs(title = "Salary Distribution by Job Title",
x = "Job Title",
y = "Salary (USD)") +
theme_minimal()
print(boxplot)
We directly searched for the highest salary across all job titles in the data_science_data data frame by using the which.max() function. The job title “Data Scientist” had the highest salary: $412,000 for the work year 2020 and experience level SE. The job title “Data Scientist” had the lowest salary: $2859 for the work year 2021 and experience level MI.
# Find the row index of the highest salary
highest_salary_index <- which.max(data_science_data$salary_in_usd)
# Get the corresponding row with the highest salary
highest_salary_row <- data_science_data[highest_salary_index, ]
print(highest_salary_row)## company_id employee_residence remote_ratio company_location company_size
## 34 6 US 100 US L
## cid job_title_id work_year experience_level employment_type
## 34 US_100_US_L 64 2020 SE FT
## job_title salary salary_currency salary_in_usd
## 34 Data Scientist 412000 USD 412000# Find the row index of the lowest salary
lowest_salary_index <- which.min(data_science_data$salary_in_usd)
# Get the corresponding row with the lowest salary
lowest_salary_row <- data_science_data[lowest_salary_index, ]
print(lowest_salary_row)## company_id employee_residence remote_ratio company_location company_size
## 166 24 MX 0 MX S
## cid job_title_id work_year experience_level employment_type
## 166 MX_0_MX_S 177 2021 MI FT
## job_title salary salary_currency salary_in_usd
## 166 Data Scientist 58000 MXN 2859The bar plot below displays the salary distribution by work experience and job title based on years 2020-2022. During the years 2020 and 2022, the job title Data Scientist received higher salaries compared to Data Analyst and Machine Learning Engineer roles. In 2021, Data Analyst and Machine Learning Engineer roles received the same salary distribution.
# Create a bar plot for each job title
ggplot(data_science_data, aes(x = factor(work_year), y = salary_in_usd, fill = job_title)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Salary Distribution by Work Experience and Job Title",
x = "Work Experience (Years)",
y = "Salary (USD)",
fill = "Job Title") +
scale_y_continuous(labels = scales::comma_format()) +
theme_minimal()
The bar plots below displays that there were a higher frequency of Data Scientist roles.
# Create a bar plot of job titles with count values
ggplot(data_science_data, aes(x = job_title)) +
geom_bar() +
geom_text(stat = 'count', aes(label=..count..), vjust = -0.1) + # Add count values on top of bars
labs(title = "Distribution of Data Science Job Titles",
x = "Job Title",
y = "Frequency") +
theme(axis.text.x = element_text())## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
More scatter plots in the US and fewer in other locations suggest differences in the distribution and representation of salary data across different geographic regions, potentially reflecting underlying socioeconomic and industrial factors.
# Filter data for USD locations
df_usd <- total_df[total_df$salary_currency == "USD", ]
# Scatter plot: Salary (USD) vs. Location (with reversed axes)
ggplot(df_usd, aes(x = salary_in_usd, y = company_location)) +
geom_point(alpha = 0.5) +
labs(title = "Salary Distribution by Location (USD)",
x = "Salary (USD)",
y = "Location") +
scale_x_continuous(labels = scales::comma) + # Remove scientific notation
theme(axis.text.y = element_text(hjust = 1)) # Adjust text alignment
Based on this scatter plot displaying the salary distribution by Job title, there are a crowd of scattered points for the data science roles: Data Analyst, Data Scientist and Data Engineer. This means that there is a higher representation of these job titles being prevalent in the dataset compared to other data science job titles. It also seems that these 3 roles are popular in the data science industry.
The spread of these points for each job title category reflects significant differences in compensation depending on factors such as experience, education, location, and specific industry.
The clustering of points around these job titles could also indicate higher demand or competition in the job market for roles like Data Analyst, Data Scientist, and Data Engineer. Companies may be offering a wide range of salaries to attract talent in these fields.
To add on, this might also signify ongoing trends or changes in the industry where roles related to data analysis and data science are in high demand. This could be due to advancements in technology, the increasing importance of data-driven decision-making, or emerging sectors such as artificial intelligence and machine learning.
# Filter data for USD locations
df_usd <- total_df[total_df$salary_currency == "USD", ]
# Scatter plot: Salary (USD) vs. Job Title (with reversed axes)
ggplot(df_usd, aes(x = salary_in_usd, y = reorder(job_title, desc(salary_in_usd)))) +
geom_point(alpha = 0.5) +
labs(title = "Salary Distribution by Job Title",
x = "Salary (USD)",
y = "Job Title") +
scale_x_continuous(labels = scales::comma_format()) # Remove scientific notation
We also calculated the average salary by the work year, experience level, job title and company size and visualized the results in a heat map to observe the color intensity between each variable.
Each cell in the heatmap represents the average salary for a combination of the work year, job title, experience level, and company size.
The color intensity of each cell represents the average salary, with higher intensities indicating higher average salaries.
Heat Maps based on Average Salary & Job Title by Work year, Experience Level, Employment Type & Company Size
library(ggplot2)
# Calculate average salary by work year and job title
average_salary <- aggregate(salary_in_usd ~ work_year + job_title, data = df_usd, FUN = mean)
print(average_salary)## work_year job_title salary_in_usd
## 1 2021 AI Scientist 26333.33
## 2 2022 AI Scientist 160000.00
## 3 2022 Analytics Engineer 175000.00
## 4 2022 Applied Data Scientist 238000.00
## 5 2021 Applied Machine Learning Scientist 230700.00
## 6 2022 Applied Machine Learning Scientist 75000.00
## 7 2020 BI Data Analyst 98000.00
## 8 2021 BI Data Analyst 78568.00
## 9 2020 Big Data Engineer 70000.00
## 10 2021 Big Data Engineer 39000.00
## 11 2020 Business Data Analyst 117500.00
## 12 2021 Cloud Data Engineer 160000.00
## 13 2020 Computer Vision Engineer 60000.00
## 14 2021 Computer Vision Engineer 24000.00
## 15 2022 Computer Vision Engineer 67500.00
## 16 2021 Computer Vision Software Engineer 70000.00
## 17 2022 Computer Vision Software Engineer 150000.00
## 18 2020 Data Analyst 53200.00
## 19 2021 Data Analyst 91250.00
## 20 2022 Data Analyst 107203.70
## 21 2021 Data Analytics Engineer 80000.00
## 22 2022 Data Analytics Engineer 20000.00
## 23 2022 Data Analytics Lead 405000.00
## 24 2021 Data Analytics Manager 126666.67
## 25 2022 Data Analytics Manager 127485.00
## 26 2021 Data Architect 166666.67
## 27 2022 Data Architect 182076.62
## 28 2020 Data Engineer 133700.00
## 29 2021 Data Engineer 107089.06
## 30 2022 Data Engineer 146999.05
## 31 2021 Data Engineering Manager 159000.00
## 32 2020 Data Science Consultant 103000.00
## 33 2021 Data Science Consultant 90000.00
## 34 2022 Data Science Engineer 60000.00
## 35 2020 Data Science Manager 190200.00
## 36 2021 Data Science Manager 177500.00
## 37 2022 Data Science Manager 170196.60
## 38 2020 Data Scientist 149158.57
## 39 2021 Data Scientist 97883.33
## 40 2022 Data Scientist 148052.00
## 41 2021 Data Specialist 165000.00
## 42 2021 Director of Data Engineering 200000.00
## 43 2020 Director of Data Science 325000.00
## 44 2021 Director of Data Science 209000.00
## 45 2021 Financial Data Analyst 450000.00
## 46 2022 Financial Data Analyst 100000.00
## 47 2021 Head of Data 232500.00
## 48 2022 Head of Data 200000.00
## 49 2021 Head of Data Science 97500.00
## 50 2022 Head of Data Science 195937.50
## 51 2020 Lead Data Analyst 87000.00
## 52 2021 Lead Data Analyst 170000.00
## 53 2020 Lead Data Engineer 90500.00
## 54 2021 Lead Data Engineer 218000.00
## 55 2020 Lead Data Scientist 152500.00
## 56 2021 Machine Learning Developer 100000.00
## 57 2020 Machine Learning Engineer 179333.33
## 58 2021 Machine Learning Engineer 98980.50
## 59 2022 Machine Learning Engineer 156249.56
## 60 2021 Machine Learning Infrastructure Engineer 195000.00
## 61 2020 Machine Learning Scientist 260000.00
## 62 2021 Machine Learning Scientist 145500.00
## 63 2022 Machine Learning Scientist 141766.67
## 64 2021 ML Engineer 263000.00
## 65 2021 Principal Data Analyst 170000.00
## 66 2022 Principal Data Analyst 75000.00
## 67 2021 Principal Data Engineer 328333.33
## 68 2021 Principal Data Scientist 255500.00
## 69 2020 Product Data Analyst 20000.00
## 70 2020 Research Scientist 246000.00
## 71 2021 Research Scientist 73333.00
## 72 2022 Research Scientist 132000.00
## 73 2021 Staff Data Scientist 105000.00# Create a heat map
ggplot(average_salary, aes(x = work_year, y = job_title, fill = salary_in_usd)) +
geom_tile() +
scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) + # Remove scientific notation
labs(title = "Average Salary by Work Year and Job Title",
x = "Work Year",
y = "Job Title",
fill = "Average Salary (USD)") +
theme(legend.position = "right") # Adjust legend position
The abbreviations “EN”, “EX”, “MI”, and “SE” likely represent different experience levels. Below is a typical interpretation of these abbreviations:
- EN: Entry-level
- EX: Experienced
- MI: Mid-level
- SE: Senior-level
These abbreviations are commonly used in job postings or HR contexts to describe the level of experience required or preferred for a particular role.
library(ggplot2)
# Calculate average salary by experience level, job title, and company size
average_salary <- aggregate(salary_in_usd ~ experience_level + job_title + company_size, data = df_usd, FUN = mean)
print(average_salary)## experience_level job_title company_size
## 1 MI AI Scientist L
## 2 SE AI Scientist L
## 3 MI Applied Data Scientist L
## 4 SE Applied Data Scientist L
## 5 MI Applied Machine Learning Scientist L
## 6 EX BI Data Analyst L
## 7 EN Big Data Engineer L
## 8 EN Business Data Analyst L
## 9 MI Business Data Analyst L
## 10 EN Data Analyst L
## 11 MI Data Analyst L
## 12 SE Data Analyst L
## 13 MI Data Analytics Engineer L
## 14 SE Data Analytics Lead L
## 15 SE Data Analytics Manager L
## 16 MI Data Architect L
## 17 EN Data Engineer L
## 18 MI Data Engineer L
## 19 SE Data Engineer L
## 20 SE Data Engineering Manager L
## 21 MI Data Science Consultant L
## 22 SE Data Science Engineer L
## 23 SE Data Science Manager L
## 24 EN Data Scientist L
## 25 MI Data Scientist L
## 26 SE Data Scientist L
## 27 SE Data Specialist L
## 28 SE Director of Data Engineering L
## 29 EX Director of Data Science L
## 30 EN Financial Data Analyst L
## 31 MI Financial Data Analyst L
## 32 EX Head of Data L
## 33 MI Lead Data Analyst L
## 34 SE Lead Data Analyst L
## 35 SE Lead Data Engineer L
## 36 MI Lead Data Scientist L
## 37 EN Machine Learning Engineer L
## 38 SE Machine Learning Engineer L
## 39 EN Machine Learning Scientist L
## 40 MI Machine Learning Scientist L
## 41 SE Machine Learning Scientist L
## 42 MI ML Engineer L
## 43 EX Principal Data Engineer L
## 44 SE Principal Data Engineer L
## 45 MI Principal Data Scientist L
## 46 SE Principal Data Scientist L
## 47 EN Research Scientist L
## 48 MI Research Scientist L
## 49 SE Research Scientist L
## 50 EN AI Scientist M
## 51 MI AI Scientist M
## 52 EX Analytics Engineer M
## 53 SE Analytics Engineer M
## 54 MI Applied Machine Learning Scientist M
## 55 MI BI Data Analyst M
## 56 MI Big Data Engineer M
## 57 EN Computer Vision Engineer M
## 58 SE Computer Vision Engineer M
## 59 EN Computer Vision Software Engineer M
## 60 EN Data Analyst M
## 61 EX Data Analyst M
## 62 MI Data Analyst M
## 63 SE Data Analyst M
## 64 EN Data Analytics Engineer M
## 65 SE Data Analytics Engineer M
## 66 SE Data Analytics Manager M
## 67 SE Data Architect M
## 68 EN Data Engineer M
## 69 EX Data Engineer M
## 70 MI Data Engineer M
## 71 SE Data Engineer M
## 72 MI Data Science Manager M
## 73 SE Data Science Manager M
## 74 EN Data Scientist M
## 75 MI Data Scientist M
## 76 SE Data Scientist M
## 77 SE Head of Data M
## 78 EX Head of Data Science M
## 79 MI Lead Data Engineer M
## 80 EN Machine Learning Engineer M
## 81 SE Machine Learning Engineer M
## 82 SE Machine Learning Infrastructure Engineer M
## 83 MI Machine Learning Scientist M
## 84 SE Principal Data Analyst M
## 85 SE Principal Data Engineer M
## 86 MI Research Scientist M
## 87 SE Staff Data Scientist M
## 88 EN AI Scientist S
## 89 EN BI Data Analyst S
## 90 MI Big Data Engineer S
## 91 SE Cloud Data Engineer S
## 92 SE Computer Vision Engineer S
## 93 EN Computer Vision Software Engineer S
## 94 EN Data Analyst S
## 95 MI Data Analyst S
## 96 SE Data Analyst S
## 97 EN Data Engineer S
## 98 SE Data Engineer S
## 99 EN Data Science Consultant S
## 100 EN Data Scientist S
## 101 MI Data Scientist S
## 102 SE Director of Data Science S
## 103 MI Head of Data Science S
## 104 SE Lead Data Engineer S
## 105 SE Lead Data Scientist S
## 106 EN Machine Learning Developer S
## 107 EN Machine Learning Engineer S
## 108 MI Machine Learning Engineer S
## 109 SE Machine Learning Engineer S
## 110 SE Machine Learning Scientist S
## 111 SE ML Engineer S
## 112 MI Principal Data Analyst S
## 113 EX Principal Data Scientist S
## 114 MI Product Data Analyst S
## 115 SE Research Scientist S
## salary_in_usd
## 1 200000.00
## 2 55000.00
## 3 157000.00
## 4 278500.00
## 5 249000.00
## 6 150000.00
## 7 70000.00
## 8 100000.00
## 9 135000.00
## 10 81500.00
## 11 76857.14
## 12 200000.00
## 13 110000.00
## 14 405000.00
## 15 130000.00
## 16 166666.67
## 17 76250.00
## 18 109777.78
## 19 157387.50
## 20 159000.00
## 21 103000.00
## 22 60000.00
## 23 177500.00
## 24 37133.33
## 25 113777.78
## 26 187863.64
## 27 165000.00
## 28 200000.00
## 29 287500.00
## 30 100000.00
## 31 450000.00
## 32 232500.00
## 33 87000.00
## 34 170000.00
## 35 276000.00
## 36 115000.00
## 37 250000.00
## 38 178333.33
## 39 225000.00
## 40 136150.00
## 41 225000.00
## 42 270000.00
## 43 600000.00
## 44 185000.00
## 45 151000.00
## 46 227500.00
## 47 87333.33
## 48 69999.00
## 49 144000.00
## 50 12000.00
## 51 120000.00
## 52 155000.00
## 53 195000.00
## 54 38400.00
## 55 99000.00
## 56 60000.00
## 57 67500.00
## 58 24000.00
## 59 70000.00
## 60 62250.00
## 61 120000.00
## 62 105584.44
## 63 112859.03
## 64 20000.00
## 65 50000.00
## 66 125988.00
## 67 182076.62
## 68 120000.00
## 69 245500.00
## 70 111232.40
## 71 142032.03
## 72 200000.00
## 73 160295.75
## 74 71000.00
## 75 127519.23
## 76 158403.45
## 77 200000.00
## 78 158958.33
## 79 56000.00
## 80 21844.00
## 81 183541.00
## 82 195000.00
## 83 82500.00
## 84 170000.00
## 85 200000.00
## 86 450000.00
## 87 105000.00
## 88 12000.00
## 89 32136.00
## 90 18000.00
## 91 160000.00
## 92 60000.00
## 93 150000.00
## 94 53333.33
## 95 39000.00
## 96 80000.00
## 97 65000.00
## 98 115000.00
## 99 90000.00
## 100 98333.33
## 101 58753.33
## 102 168000.00
## 103 110000.00
## 104 142500.00
## 105 190000.00
## 106 100000.00
## 107 89800.00
## 108 97000.00
## 109 92500.00
## 110 190000.00
## 111 256000.00
## 112 75000.00
## 113 416000.00
## 114 20000.00
## 115 50000.00# Create a heatmap
ggplot(average_salary, aes(x = experience_level, y = job_title, fill = salary_in_usd)) +
geom_tile() +
scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) + # Remove scientific notation
labs(title = "Average Salary by Job Title and Experience Level",
x = "Experience Level",
y = "Job Title",
fill = "Average Salary (USD)") +
theme(legend.position = "right") # Adjust legend position
The abbreviations “L”, “M”, “S” represent different company sizes. Below is a typical interpretation of these abbreviations:
- L: Large-Sized
- M: Mid-Sized
- S: small-sized
library(ggplot2)
# Calculate average salary by company size, job title, and experience level
average_salary <- aggregate(salary_in_usd ~ company_size + job_title + experience_level, data = df_usd, FUN = mean)
print(average_salary)## company_size job_title experience_level
## 1 M AI Scientist EN
## 2 S AI Scientist EN
## 3 S BI Data Analyst EN
## 4 L Big Data Engineer EN
## 5 L Business Data Analyst EN
## 6 M Computer Vision Engineer EN
## 7 M Computer Vision Software Engineer EN
## 8 S Computer Vision Software Engineer EN
## 9 L Data Analyst EN
## 10 M Data Analyst EN
## 11 S Data Analyst EN
## 12 M Data Analytics Engineer EN
## 13 L Data Engineer EN
## 14 M Data Engineer EN
## 15 S Data Engineer EN
## 16 S Data Science Consultant EN
## 17 L Data Scientist EN
## 18 M Data Scientist EN
## 19 S Data Scientist EN
## 20 L Financial Data Analyst EN
## 21 S Machine Learning Developer EN
## 22 L Machine Learning Engineer EN
## 23 M Machine Learning Engineer EN
## 24 S Machine Learning Engineer EN
## 25 L Machine Learning Scientist EN
## 26 L Research Scientist EN
## 27 M Analytics Engineer EX
## 28 L BI Data Analyst EX
## 29 M Data Analyst EX
## 30 M Data Engineer EX
## 31 L Director of Data Science EX
## 32 L Head of Data EX
## 33 M Head of Data Science EX
## 34 L Principal Data Engineer EX
## 35 S Principal Data Scientist EX
## 36 L AI Scientist MI
## 37 M AI Scientist MI
## 38 L Applied Data Scientist MI
## 39 L Applied Machine Learning Scientist MI
## 40 M Applied Machine Learning Scientist MI
## 41 M BI Data Analyst MI
## 42 M Big Data Engineer MI
## 43 S Big Data Engineer MI
## 44 L Business Data Analyst MI
## 45 L Data Analyst MI
## 46 M Data Analyst MI
## 47 S Data Analyst MI
## 48 L Data Analytics Engineer MI
## 49 L Data Architect MI
## 50 L Data Engineer MI
## 51 M Data Engineer MI
## 52 L Data Science Consultant MI
## 53 M Data Science Manager MI
## 54 L Data Scientist MI
## 55 M Data Scientist MI
## 56 S Data Scientist MI
## 57 L Financial Data Analyst MI
## 58 S Head of Data Science MI
## 59 L Lead Data Analyst MI
## 60 M Lead Data Engineer MI
## 61 L Lead Data Scientist MI
## 62 S Machine Learning Engineer MI
## 63 L Machine Learning Scientist MI
## 64 M Machine Learning Scientist MI
## 65 L ML Engineer MI
## 66 S Principal Data Analyst MI
## 67 L Principal Data Scientist MI
## 68 S Product Data Analyst MI
## 69 L Research Scientist MI
## 70 M Research Scientist MI
## 71 L AI Scientist SE
## 72 M Analytics Engineer SE
## 73 L Applied Data Scientist SE
## 74 S Cloud Data Engineer SE
## 75 M Computer Vision Engineer SE
## 76 S Computer Vision Engineer SE
## 77 L Data Analyst SE
## 78 M Data Analyst SE
## 79 S Data Analyst SE
## 80 M Data Analytics Engineer SE
## 81 L Data Analytics Lead SE
## 82 L Data Analytics Manager SE
## 83 M Data Analytics Manager SE
## 84 M Data Architect SE
## 85 L Data Engineer SE
## 86 M Data Engineer SE
## 87 S Data Engineer SE
## 88 L Data Engineering Manager SE
## 89 L Data Science Engineer SE
## 90 L Data Science Manager SE
## 91 M Data Science Manager SE
## 92 L Data Scientist SE
## 93 M Data Scientist SE
## 94 L Data Specialist SE
## 95 L Director of Data Engineering SE
## 96 S Director of Data Science SE
## 97 M Head of Data SE
## 98 L Lead Data Analyst SE
## 99 L Lead Data Engineer SE
## 100 S Lead Data Engineer SE
## 101 S Lead Data Scientist SE
## 102 L Machine Learning Engineer SE
## 103 M Machine Learning Engineer SE
## 104 S Machine Learning Engineer SE
## 105 M Machine Learning Infrastructure Engineer SE
## 106 L Machine Learning Scientist SE
## 107 S Machine Learning Scientist SE
## 108 S ML Engineer SE
## 109 M Principal Data Analyst SE
## 110 L Principal Data Engineer SE
## 111 M Principal Data Engineer SE
## 112 L Principal Data Scientist SE
## 113 L Research Scientist SE
## 114 S Research Scientist SE
## 115 M Staff Data Scientist SE
## salary_in_usd
## 1 12000.00
## 2 12000.00
## 3 32136.00
## 4 70000.00
## 5 100000.00
## 6 67500.00
## 7 70000.00
## 8 150000.00
## 9 81500.00
## 10 62250.00
## 11 53333.33
## 12 20000.00
## 13 76250.00
## 14 120000.00
## 15 65000.00
## 16 90000.00
## 17 37133.33
## 18 71000.00
## 19 98333.33
## 20 100000.00
## 21 100000.00
## 22 250000.00
## 23 21844.00
## 24 89800.00
## 25 225000.00
## 26 87333.33
## 27 155000.00
## 28 150000.00
## 29 120000.00
## 30 245500.00
## 31 287500.00
## 32 232500.00
## 33 158958.33
## 34 600000.00
## 35 416000.00
## 36 200000.00
## 37 120000.00
## 38 157000.00
## 39 249000.00
## 40 38400.00
## 41 99000.00
## 42 60000.00
## 43 18000.00
## 44 135000.00
## 45 76857.14
## 46 105584.44
## 47 39000.00
## 48 110000.00
## 49 166666.67
## 50 109777.78
## 51 111232.40
## 52 103000.00
## 53 200000.00
## 54 113777.78
## 55 127519.23
## 56 58753.33
## 57 450000.00
## 58 110000.00
## 59 87000.00
## 60 56000.00
## 61 115000.00
## 62 97000.00
## 63 136150.00
## 64 82500.00
## 65 270000.00
## 66 75000.00
## 67 151000.00
## 68 20000.00
## 69 69999.00
## 70 450000.00
## 71 55000.00
## 72 195000.00
## 73 278500.00
## 74 160000.00
## 75 24000.00
## 76 60000.00
## 77 200000.00
## 78 112859.03
## 79 80000.00
## 80 50000.00
## 81 405000.00
## 82 130000.00
## 83 125988.00
## 84 182076.62
## 85 157387.50
## 86 142032.03
## 87 115000.00
## 88 159000.00
## 89 60000.00
## 90 177500.00
## 91 160295.75
## 92 187863.64
## 93 158403.45
## 94 165000.00
## 95 200000.00
## 96 168000.00
## 97 200000.00
## 98 170000.00
## 99 276000.00
## 100 142500.00
## 101 190000.00
## 102 178333.33
## 103 183541.00
## 104 92500.00
## 105 195000.00
## 106 225000.00
## 107 190000.00
## 108 256000.00
## 109 170000.00
## 110 185000.00
## 111 200000.00
## 112 227500.00
## 113 144000.00
## 114 50000.00
## 115 105000.00# Create a heatmap
ggplot(average_salary, aes(x = company_size, y = job_title, fill = salary_in_usd)) +
geom_tile() +
scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) + # Remove scientific notation
labs(title = "Average Salary by Job Title and Company Size",
x = "Company Size",
y = "Job Title",
fill = "Average Salary (USD)") +
theme(legend.position = "right") # Adjust legend position
The abbreviations “CT”, “FL”, “FT”, and “PT” likely represent different types of employment. Here’s a typical interpretation of these abbreviations:
- CT: Contract or Contractor
- FL: Freelance or Freelancer
- FT: Full-time
- PT: Part-time
These abbreviations are commonly used in employment contexts to describe the nature of the work arrangement or employment status. Each abbreviation corresponds to a different type of employment arrangement, indicating whether the position is full-time, part-time, contract-based, or freelance.
library(ggplot2)
# Calculate average salary by employment type, job title, and experience level
average_salary <- aggregate(salary_in_usd ~ employment_type + job_title + experience_level, data = df_usd, FUN = mean)
print(average_salary)## employment_type job_title experience_level
## 1 PT AI Scientist EN
## 2 FT BI Data Analyst EN
## 3 FT Big Data Engineer EN
## 4 CT Business Data Analyst EN
## 5 FT Computer Vision Engineer EN
## 6 FT Computer Vision Software Engineer EN
## 7 FT Data Analyst EN
## 8 FT Data Analytics Engineer EN
## 9 FT Data Engineer EN
## 10 FT Data Science Consultant EN
## 11 FT Data Scientist EN
## 12 PT Data Scientist EN
## 13 FT Financial Data Analyst EN
## 14 FT Machine Learning Developer EN
## 15 FT Machine Learning Engineer EN
## 16 FT Machine Learning Scientist EN
## 17 FT Research Scientist EN
## 18 FT Analytics Engineer EX
## 19 FT BI Data Analyst EX
## 20 FT Data Analyst EX
## 21 FT Data Engineer EX
## 22 FT Director of Data Science EX
## 23 FT Head of Data EX
## 24 FT Head of Data Science EX
## 25 FT Principal Data Engineer EX
## 26 CT Principal Data Scientist EX
## 27 FT AI Scientist MI
## 28 FT Applied Data Scientist MI
## 29 FT Applied Machine Learning Scientist MI
## 30 FT BI Data Analyst MI
## 31 FT Big Data Engineer MI
## 32 FT Business Data Analyst MI
## 33 FT Data Analyst MI
## 34 FT Data Analytics Engineer MI
## 35 FT Data Architect MI
## 36 FL Data Engineer MI
## 37 FT Data Engineer MI
## 38 FT Data Science Consultant MI
## 39 FT Data Science Manager MI
## 40 FL Data Scientist MI
## 41 FT Data Scientist MI
## 42 FT Financial Data Analyst MI
## 43 FT Head of Data Science MI
## 44 FT Lead Data Analyst MI
## 45 FT Lead Data Engineer MI
## 46 FT Lead Data Scientist MI
## 47 FT Machine Learning Engineer MI
## 48 FL Machine Learning Scientist MI
## 49 FT Machine Learning Scientist MI
## 50 CT ML Engineer MI
## 51 FT Principal Data Analyst MI
## 52 FT Principal Data Scientist MI
## 53 FT Product Data Analyst MI
## 54 FT Research Scientist MI
## 55 FT AI Scientist SE
## 56 FT Analytics Engineer SE
## 57 FT Applied Data Scientist SE
## 58 FT Cloud Data Engineer SE
## 59 FL Computer Vision Engineer SE
## 60 FT Computer Vision Engineer SE
## 61 FT Data Analyst SE
## 62 FT Data Analytics Engineer SE
## 63 FT Data Analytics Lead SE
## 64 FT Data Analytics Manager SE
## 65 FT Data Architect SE
## 66 FT Data Engineer SE
## 67 FT Data Engineering Manager SE
## 68 FT Data Science Engineer SE
## 69 FT Data Science Manager SE
## 70 FT Data Scientist SE
## 71 FT Data Specialist SE
## 72 FT Director of Data Engineering SE
## 73 FT Director of Data Science SE
## 74 FT Head of Data SE
## 75 FT Lead Data Analyst SE
## 76 FT Lead Data Engineer SE
## 77 FT Lead Data Scientist SE
## 78 FT Machine Learning Engineer SE
## 79 FT Machine Learning Infrastructure Engineer SE
## 80 FT Machine Learning Scientist SE
## 81 FT ML Engineer SE
## 82 FT Principal Data Analyst SE
## 83 FT Principal Data Engineer SE
## 84 FT Principal Data Scientist SE
## 85 FT Research Scientist SE
## 86 CT Staff Data Scientist SE
## salary_in_usd
## 1 12000.00
## 2 32136.00
## 3 70000.00
## 4 100000.00
## 5 67500.00
## 6 110000.00
## 7 63555.56
## 8 20000.00
## 9 84375.00
## 10 90000.00
## 11 65600.00
## 12 100000.00
## 13 100000.00
## 14 100000.00
## 15 102977.71
## 16 225000.00
## 17 87333.33
## 18 155000.00
## 19 150000.00
## 20 120000.00
## 21 245500.00
## 22 287500.00
## 23 232500.00
## 24 158958.33
## 25 600000.00
## 26 416000.00
## 27 160000.00
## 28 157000.00
## 29 178800.00
## 30 99000.00
## 31 39000.00
## 32 135000.00
## 33 87014.44
## 34 110000.00
## 35 166666.67
## 36 20000.00
## 37 115573.56
## 38 103000.00
## 39 200000.00
## 40 100000.00
## 41 114917.08
## 42 450000.00
## 43 110000.00
## 44 87000.00
## 45 56000.00
## 46 115000.00
## 47 97000.00
## 48 12000.00
## 49 141766.67
## 50 270000.00
## 51 75000.00
## 52 151000.00
## 53 20000.00
## 54 259999.50
## 55 55000.00
## 56 195000.00
## 57 278500.00
## 58 160000.00
## 59 60000.00
## 60 24000.00
## 61 114287.50
## 62 50000.00
## 63 405000.00
## 64 127134.29
## 65 182076.62
## 66 144028.10
## 67 159000.00
## 68 60000.00
## 69 168897.88
## 70 166505.00
## 71 165000.00
## 72 200000.00
## 73 168000.00
## 74 200000.00
## 75 170000.00
## 76 187000.00
## 77 190000.00
## 78 165567.82
## 79 195000.00
## 80 201666.67
## 81 256000.00
## 82 170000.00
## 83 192500.00
## 84 227500.00
## 85 97000.00
## 86 105000.00# Create a heatmap
ggplot(average_salary, aes(x = employment_type, y = job_title, fill = salary_in_usd)) +
geom_tile() +
scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) + # Remove scientific notation
labs(title = "Average Salary by Job Title and Employment Type",
x = "Employment Type",
y = "Job Title",
fill = "Average Salary (USD)") +
theme(legend.position = "right") # Adjust legend position
Top 10 Data Science Job Titles by Average Salary
library(dplyr)
library(ggplot2)
# Calculate average salary by job title
average_salary <- df_usd %>%
group_by(job_title) %>%
summarise(average_salary = mean(salary_in_usd)) %>%
arrange(desc(average_salary)) %>%
slice(1:10) # Select top 10 job titles
# Create a bar plot
ggplot(average_salary, aes(x = reorder(job_title, -average_salary), y = average_salary)) +
geom_bar(stat = "identity", fill = "darkblue") +
geom_text(aes(label = sprintf("$%.2f", average_salary)), vjust = -0.1, size = 3) + # Add salary labels
labs(title = "Top 10 Data Science Job Titles by Average Salary",
x = "Job Title",
y = "Average Salary (USD)") +
scale_y_continuous(labels = scales::comma_format()) + # Remove scientific notation
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank()) # Remove x-axis label for better readability
Conclusion
Based on the data analysis and visualization conducted from this dataset we obtained from Kaggle, it is evident that data science roles involving Data Analyst, Data Scientist and Machine Learning Engineer are the most valued data science skills due to high representation, salary distribution, job market demand and industry trends. Based on the data visualizations above, in 2021 and 2022, the average salary for the data science job titles: Financial Data Analyst and Data Analytics Lead were the highest. The average salary by Experience Levels (Experienced (EX), Mid-level (MI) and Senior-level (SE) ) for the data science job titles: Principal Data Engineer, Research Scientist, Financial Data Analyst and Data Analytics Manager were the highest. To add on, the average salary for data science roles were the highest for mostly large sized companies. Based on the data visualizations, the average salary for the job titles: Financial Data Analyst and Data Analytics Lead were paid the highest in large size companies. On the other hand, Research Scientists were paid the highest in mid-sized companies and Principal Data Scientists were paid the highest in small sized companies. There is a higher color intensity for data science job titles that are full time which proves that the job titles: Financial Data Analyst and Data Analytics Lead receive the highest salary. In contrast, Principal Data Scientists receive a high salary as Contractors. Therefore, data science skills involved with analytics, science, financial, machine learning and research are valued the most.
---
title: "DATA 607 Project 3"
author: "Puja Roy, Chhiring Lama, William Berritt"
date: "`r Sys.Date()`"
output: openintro::lab_report
---

### Load the packages 
```{r load-packages, message=FALSE}
library(tidyverse)
library(openintro)

library('DBI')
library('RMySQL')
```

### Introduction

For this project, the aim is to obtain data to answer the question, “Which are the most valued data science skills?”

We obtained the Data Science Job Salaries dataset from Kaggle: https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries?resource=download
which contains information about salaries of jobs in the Data Science domain. The dataset includes work year, company size, job title, salary in USD, employee residence and company location.

We decided to focus on job titles that included salary in the USD and worked with variables including the work year, company size and company location.

### Load the data
We stored the data in MySQL workbench and azure database, joined the tables that contained data of company and job and then queried it using R.
```{r echo=FALSE}
# Assign the password to a variable
db_password <- "lama65"

# Connect to the database
mydb <- dbConnect(MySQL(), user='chhiring.lama65', password=db_password, dbname='chhiring.lama65', host='cunydata607sql.mysql.database.azure.com')
```

```{r}
# List tables in the database (not shown in the published document)
dbListTables(mydb)

# Retrieve data from a specific table
df1 <- dbGetQuery(mydb,'select * from company')

# Print the retrieved data
print(df1)

```

### Join the Tables to obtain the full data
```{r}
job <- dbGetQuery(mydb,'select * from job')

company <- dbGetQuery(mydb,'select * from company')

total_df <- left_join(company, job, by='cid')
print(total_df)
```

### Data Exploration and Analysis
We checked for missing and duplicate values. As shown below in the results, there were no missing or duplicate values.
```{r}
# Data Exploration
str(total_df)
summary(total_df)
# Check for missing values
sum(is.na(total_df))
```

```{r}
# Print out the column names
print(colnames(total_df))
```
To verify duplicate values in the dataset, I used the duplicated() function. This creates a new dataframe displaying any duplication values. I also used the sum function.
```{r}
num_duplicates <- sum(duplicated(total_df))
# Check for duplicates
duplicates <- total_df[duplicated(total_df), ]
print(duplicates)
```
There are no duplicate values in the dataset.

### Extract Relevant Skills

I extracted certain skills based on job titles including Data Scientist, Data Analyst and Machine Learning Engineer which would imply skills relevant to data science.
```{r}
# Analysis
# Extract relevant skills (based on job titles)
data_science_roles <- c("Data Scientist", "Data Analyst", "Machine Learning Engineer")
data_science_data <- total_df[total_df$job_title %in% data_science_roles, ]
print(data_science_data)
```
I used the dplyr library to filter the data and compute the summary statistics including the median, mean, min and max salary by job title. The Data Scientist job title had the highest mean salary among the other job titles based on data science roles.
```{r}
library(dplyr)

# Filter data for relevant job titles
data_science_roles <- c("Data Scientist", "Data Analyst", "Machine Learning Engineer")
data_science_data <- total_df[total_df$job_title %in% data_science_roles, ]

# Summary statistics of salary by job title
summary_stats <- data_science_data %>%
  group_by(job_title) %>%
  summarise(
    median_salary = median(salary_in_usd),
    mean_salary = mean(salary_in_usd),
    min_salary = min(salary_in_usd),
    max_salary = max(salary_in_usd)
  )

print(summary_stats)
```

### Salary Distributions based on Job Title, Work Experience & Location

We created visualizations to display the summary statistics of salary by job title using ggplot2. Below is a boxplot where each box represents the distribution of salaries for each job title. It provides a visual comparison of the median, quartiles, and potential outliers for each job title's salary distribution. 

```{r}
# Boxplot visualization with color and removed scientific notation
boxplot <- ggplot(data_science_data, aes(x = job_title, y = salary_in_usd, fill = job_title)) +
  geom_boxplot() +
  scale_y_continuous(labels = scales::comma) +  # Remove scientific notation
  labs(title = "Salary Distribution by Job Title",
       x = "Job Title",
       y = "Salary (USD)") +
  theme_minimal()

print(boxplot)

```

We directly searched for the highest salary across all job titles in the data_science_data data frame by using the which.max() function. The job title "Data Scientist" had the highest salary: $412,000 for the work year 2020 and experience level SE. The job title "Data Scientist" had the lowest salary: $2859 for the work year 2021 and experience level MI.

```{r}
# Find the row index of the highest salary
highest_salary_index <- which.max(data_science_data$salary_in_usd)

# Get the corresponding row with the highest salary
highest_salary_row <- data_science_data[highest_salary_index, ]

print(highest_salary_row)

```


```{r}
# Find the row index of the lowest salary
lowest_salary_index <- which.min(data_science_data$salary_in_usd)

# Get the corresponding row with the lowest salary
lowest_salary_row <- data_science_data[lowest_salary_index, ]

print(lowest_salary_row)

```
The bar plot below displays the salary distribution by work experience and job title based on years 2020-2022. During the years 2020 and 2022, the job title Data Scientist received higher salaries compared to Data Analyst and Machine Learning Engineer roles. In 2021, Data Analyst and Machine Learning Engineer roles received the same salary distribution.

```{r}
# Create a bar plot for each job title
ggplot(data_science_data, aes(x = factor(work_year), y = salary_in_usd, fill = job_title)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Salary Distribution by Work Experience and Job Title",
       x = "Work Experience (Years)",
       y = "Salary (USD)",
       fill = "Job Title") +
  scale_y_continuous(labels = scales::comma_format()) +
  theme_minimal()

```

The bar plots below displays that there were a higher frequency of Data Scientist roles. 
```{r}
# Create a bar plot of job titles with count values
ggplot(data_science_data, aes(x = job_title)) +
  geom_bar() +
  geom_text(stat = 'count', aes(label=..count..), vjust = -0.1) + # Add count values on top of bars
  labs(title = "Distribution of Data Science Job Titles",
       x = "Job Title",
       y = "Frequency") +
  theme(axis.text.x = element_text())
```

More scatter plots in the US and fewer in other locations suggest differences in the distribution and representation of salary data across different geographic regions, potentially reflecting underlying socioeconomic and industrial factors.
```{r}
# Filter data for USD locations
df_usd <- total_df[total_df$salary_currency == "USD", ]

# Scatter plot: Salary (USD) vs. Location (with reversed axes)
ggplot(df_usd, aes(x = salary_in_usd, y = company_location)) +
  geom_point(alpha = 0.5) +
  labs(title = "Salary Distribution by Location (USD)",
       x = "Salary (USD)",
       y = "Location") +
  scale_x_continuous(labels = scales::comma) +  # Remove scientific notation
  theme(axis.text.y = element_text(hjust = 1))  # Adjust text alignment
```

Based on this scatter plot displaying the salary distribution by Job title, there are a crowd of scattered points for the data science roles: Data Analyst, Data Scientist and Data Engineer. This means that there is a higher representation of these job titles being prevalent in the dataset compared to other data science job titles. It also seems that these 3 roles are popular in the data science industry.

The spread of these points for each job title category reflects significant differences in compensation depending on factors such as experience, education, location, and specific industry.

The clustering of points around these job titles could also indicate higher demand or competition in the job market for roles like Data Analyst, Data Scientist, and Data Engineer. Companies may be offering a wide range of salaries to attract talent in these fields.

To add on, this might also signify ongoing trends or changes in the industry where roles related to data analysis and data science are in high demand. This could be due to advancements in technology, the increasing importance of data-driven decision-making, or emerging sectors such as artificial intelligence and machine learning.

```{r}
# Filter data for USD locations
df_usd <- total_df[total_df$salary_currency == "USD", ]

# Scatter plot: Salary (USD) vs. Job Title (with reversed axes)
ggplot(df_usd, aes(x = salary_in_usd, y = reorder(job_title, desc(salary_in_usd)))) +
  geom_point(alpha = 0.5) +
  labs(title = "Salary Distribution by Job Title",
       x = "Salary (USD)",
       y = "Job Title") +
  scale_x_continuous(labels = scales::comma_format())  # Remove scientific notation
```

We also calculated the average salary by the work year, experience level, job title and company size and visualized the results in a heat map to observe the color intensity between each variable. 

Each cell in the heatmap represents the average salary for a combination of the work year, job title, experience level, and company size.

The color intensity of each cell represents the average salary, with higher intensities indicating higher average salaries.

### Heat Maps based on Average Salary & Job Title by Work year, Experience Level, Employment Type & Company Size
```{r}
library(ggplot2)

# Calculate average salary by work year and job title
average_salary <- aggregate(salary_in_usd ~ work_year + job_title, data = df_usd, FUN = mean)
print(average_salary)

# Create a heat map
ggplot(average_salary, aes(x = work_year, y = job_title, fill = salary_in_usd)) +
  geom_tile() +
  scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) +  # Remove scientific notation
  labs(title = "Average Salary by Work Year and Job Title",
       x = "Work Year",
       y = "Job Title",
       fill = "Average Salary (USD)") +
  theme(legend.position = "right")  # Adjust legend position

```

The abbreviations "EN", "EX", "MI", and "SE" likely represent different experience levels. Below is a typical interpretation of these abbreviations:

- EN: Entry-level
- EX: Experienced
- MI: Mid-level
- SE: Senior-level

These abbreviations are commonly used in job postings or HR contexts to describe the level of experience required or preferred for a particular role.

```{r}
library(ggplot2)

# Calculate average salary by experience level, job title, and company size
average_salary <- aggregate(salary_in_usd ~ experience_level + job_title + company_size, data = df_usd, FUN = mean)
print(average_salary)

# Create a heatmap
ggplot(average_salary, aes(x = experience_level, y = job_title, fill = salary_in_usd)) +
  geom_tile() +
  scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) +  # Remove scientific notation
  labs(title = "Average Salary by Job Title and Experience Level",
       x = "Experience Level",
       y = "Job Title",
       fill = "Average Salary (USD)") +
  theme(legend.position = "right")  # Adjust legend position

```

The abbreviations "L", "M", "S" represent different company sizes. Below is a typical interpretation of these abbreviations:

- L: Large-Sized
- M: Mid-Sized
- S: small-sized

```{r}
library(ggplot2)

# Calculate average salary by company size, job title, and experience level
average_salary <- aggregate(salary_in_usd ~ company_size + job_title + experience_level, data = df_usd, FUN = mean)
print(average_salary)

# Create a heatmap
ggplot(average_salary, aes(x = company_size, y = job_title, fill = salary_in_usd)) +
  geom_tile() +
  scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) +  # Remove scientific notation
  labs(title = "Average Salary by Job Title and Company Size",
       x = "Company Size",
       y = "Job Title",
       fill = "Average Salary (USD)") +
  theme(legend.position = "right")  # Adjust legend position

```

The abbreviations "CT", "FL", "FT", and "PT" likely represent different types of employment. Here's a typical interpretation of these abbreviations:

- CT: Contract or Contractor
- FL: Freelance or Freelancer
- FT: Full-time
- PT: Part-time

These abbreviations are commonly used in employment contexts to describe the nature of the work arrangement or employment status. Each abbreviation corresponds to a different type of employment arrangement, indicating whether the position is full-time, part-time, contract-based, or freelance.

```{r}
library(ggplot2)

# Calculate average salary by employment type, job title, and experience level
average_salary <- aggregate(salary_in_usd ~ employment_type + job_title + experience_level, data = df_usd, FUN = mean)
print(average_salary)

# Create a heatmap
ggplot(average_salary, aes(x = employment_type, y = job_title, fill = salary_in_usd)) +
  geom_tile() +
  scale_fill_gradient(low = "lightblue", high = "darkblue", labels = scales::comma) +  # Remove scientific notation
  labs(title = "Average Salary by Job Title and Employment Type",
       x = "Employment Type",
       y = "Job Title",
       fill = "Average Salary (USD)") +
  theme(legend.position = "right")  # Adjust legend position

```

### Top 10 Data Science Job Titles by Average Salary

```{r}
library(dplyr)
library(ggplot2)

# Calculate average salary by job title
average_salary <- df_usd %>%
  group_by(job_title) %>%
  summarise(average_salary = mean(salary_in_usd)) %>%
  arrange(desc(average_salary)) %>%
  slice(1:10)  # Select top 10 job titles

# Create a bar plot
ggplot(average_salary, aes(x = reorder(job_title, -average_salary), y = average_salary)) +
  geom_bar(stat = "identity", fill = "darkblue") +
  geom_text(aes(label = sprintf("$%.2f", average_salary)), vjust = -0.1, size = 3) +  # Add salary labels
  labs(title = "Top 10 Data Science Job Titles by Average Salary",
       x = "Job Title",
       y = "Average Salary (USD)") +
  scale_y_continuous(labels = scales::comma_format()) +  # Remove scientific notation
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank())  # Remove x-axis label for better readability

```


### Conclusion
Based on the data analysis and visualization conducted from this dataset we obtained from Kaggle, it is evident that data science roles involving Data Analyst, Data Scientist and Machine Learning Engineer are the most valued data science skills due to high representation, salary distribution, job market demand and industry trends. Based on the data visualizations above, in 2021 and 2022, the average salary for the data science job titles: Financial Data Analyst and Data Analytics Lead were the highest. The average salary by Experience Levels (Experienced (EX), Mid-level (MI) and Senior-level (SE) ) for the data science job titles: Principal Data Engineer, Research Scientist, Financial Data Analyst and Data Analytics Manager were the highest. To add on, the average salary for data science roles were the highest for mostly large sized companies. Based on the data visualizations, the average salary for the job titles: Financial Data Analyst and Data Analytics Lead were paid the highest in large size companies. On the other hand, Research Scientists were paid the highest in mid-sized companies and Principal Data Scientists were paid the highest in small sized companies. There is a higher color intensity for data science job titles that are full time which proves that the job titles: Financial Data Analyst and Data Analytics Lead receive the highest salary. In contrast, Principal Data Scientists receive a high salary as Contractors. Therefore, data science skills involved with analytics, science, financial, machine learning and research are valued the most.

