IP
Gita
2022-07-14
#1. Introduction
Kali ini akan dilakukan analisis data dari kaggle berupa Job Posting Data Science di Glassdoor
Import Library
library(flexdashboard)
library(ggplot2)
library(scales)
library(glue)
library(plotly)
library(dplyr)
library(lubridate)#2. Read Data
Data yang akan dipakai, pastikan terletak pada folder yang sama dengan R Proj
job <- read.csv("data/DS_Jobs.csv", stringsAsFactors = T)Setelah data sudah berhasil diinput, maka tahapan selanjutnya dapat dilakukan
#3. Check data Type
Tipe data dicek terlebih dahulu, untuk memastikan kesesuaian tipe data
str(job)#> 'data.frame': 660 obs. of 27 variables:
#> $ Job.Title : Factor w/ 168 levels "AI Data Scientist",..: 152 48 48 48 48 48 62 48 161 48 ...
#> $ Salary.Estimate : Factor w/ 30 levels "101-165 ","105-167 ",..: 8 8 8 8 8 8 8 8 8 8 ...
#> $ Job.Description : Factor w/ 481 levels "--------\nImportant: The hiring process for this role is managed by Dolphin, which has made 24K+ matches at job"| __truncated__,..: 145 390 319 246 127 41 356 190 192 360 ...
#> $ Rating : num 3.1 4.2 3.8 3.5 2.9 4.2 3.9 3.5 4.4 3.6 ...
#> $ Company.Name : Factor w/ 425 levels "1-800-Flowers",..: 174 236 31 189 17 176 272 199 197 425 ...
#> $ Location : Factor w/ 202 levels "Adelphi, MD",..: 118 35 20 119 118 164 28 15 159 37 ...
#> $ Headquarters : Factor w/ 227 levels "-1","Akron, OH",..: 142 90 29 16 142 184 19 21 137 48 ...
#> $ Size : Factor w/ 9 levels "-1","1 to 50 employees",..: 4 6 4 7 8 8 3 4 6 8 ...
#> $ Type.of.ownership: Factor w/ 13 levels "-1","College / University",..: 8 4 10 4 3 3 4 4 4 3 ...
#> $ Industry : Factor w/ 58 levels "-1","Accounting",..: 31 44 13 17 3 11 7 14 11 19 ...
#> $ Sector : Factor w/ 23 levels "-1","Accounting & Legal",..: 14 6 6 15 6 13 5 20 13 13 ...
#> $ Revenue : Factor w/ 14 levels "-1","$1 to $2 billion (USD)",..: 14 2 6 6 14 14 5 2 7 14 ...
#> $ min_salary : int 137 137 137 137 137 137 137 137 137 137 ...
#> $ max_salary : int 171 171 171 171 171 171 171 171 171 171 ...
#> $ avg_salary : int 154 154 154 154 154 154 154 154 154 154 ...
#> $ job_state : Factor w/ 39 levels " AL"," AZ"," CA",..: 25 35 15 15 25 3 15 15 3 11 ...
#> $ same_state : int 1 0 1 0 1 1 0 1 0 1 ...
#> $ company_age : int 27 52 39 20 22 10 24 30 37 6 ...
#> $ python : int 0 0 1 1 1 1 1 1 0 1 ...
#> $ excel : int 0 0 1 1 1 1 0 0 0 0 ...
#> $ hadoop : int 0 1 0 0 0 1 0 0 0 0 ...
#> $ spark : int 0 0 0 0 0 1 0 0 0 0 ...
#> $ aws : int 1 0 1 1 0 0 0 0 0 0 ...
#> $ tableau : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ big_data : int 0 1 0 0 0 0 0 0 0 0 ...
#> $ job_simp : Factor w/ 7 levels "analyst","data engineer",..: 3 3 3 3 3 3 3 3 3 3 ...
#> $ seniority : Factor w/ 3 levels "jr","na","senior": 3 2 2 2 2 2 2 2 2 2 ...Berdasarkan data yang digunakan, tipe data pada setiap column sudah tepat
#4. Check Missing Value
Data yang baik, biasanya tidak memiliki missing value. Oleh karena itu data perlu untuk dicek terlebih dahulu apakah terdapat missing value atau tidak
colSums(is.na(job))#> Job.Title Salary.Estimate Job.Description Rating
#> 0 0 0 0
#> Company.Name Location Headquarters Size
#> 0 0 0 0
#> Type.of.ownership Industry Sector Revenue
#> 0 0 0 0
#> min_salary max_salary avg_salary job_state
#> 0 0 0 0
#> same_state company_age python excel
#> 0 0 0 0
#> hadoop spark aws tableau
#> 0 0 0 0
#> big_data job_simp seniority
#> 0 0 0Data yang digunakan juga tidak memiliki missing value, sehingga tahapan selanjutnya dapat dilakukan
#5. Deleting Column that Not Use
Terdapat beberapa column dari data ‘job’ yang perlu dihapus. Diantaranya yaitu: Job.Description, Headquarters, Revenue, job_simp, job_state, same_state, python, excel, hadoop, spark, aws, tableau, big_data, Type.of.ownership, min_salary, max_salary, seniority
job_clean <- job %>%
select(-c(Job.Description, Headquarters, Revenue, job_simp, job_state, same_state, python, excel, hadoop, spark, aws, tableau, big_data, Type.of.ownership, min_salary, max_salary, seniority))
job_clean#> Job.Title
#> 1 Sr Data Scientist
#> 2 Data Scientist
#> 3 Data Scientist
#> 4 Data Scientist
#> 5 Data Scientist
#> 6 Data Scientist
#> 7 Data Scientist / Machine Learning Expert
#> 8 Data Scientist
#> 9 Staff Data Scientist - Analytics
#> 10 Data Scientist
#> 11 Data Scientist
#> 12 Data Scientist
#> 13 Data Scientist - Statistics, Early Career
#> 14 Data Modeler
#> 15 Data Scientist
#> 16 Experienced Data Scientist
#> 17 Data Scientist
#> 18 Data Analyst II
#> 19 Medical Lab Scientist
#> 20 Data Scientist
#> 21 Data Scientist/Machine Learning
#> 22 Human Factors Scientist
#> 23 Business Intelligence Analyst I- Data Insights
#> 24 Data Scientist
#> 25 Data Scientist
#> 26 Data Scientist - Risk
#> 27 Data Scientist-Human Resources
#> 28 Data Scientist
#> 29 Data Scientist
#> 30 Data Scientist
#> 31 Data Scientist / Machine Learning Expert
#> 32 Data Scientist
#> 33 Senior Research Statistician- Data Scientist
#> 34 Data Engineer
#> 35 Associate Data Scientist
#> 36 Business Intelligence Analyst
#> 37 Data Scientist
#> 38 Data Scientist
#> 39 Senior Analyst/Data Scientist
#> 40 Data Analyst
#> 41 Machine Learning Engineer
#> 42 Data Analyst I
#> 43 Scientist - Molecular Biology
#> 44 Data Scientist
#> 45 Computational Scientist, Machine Learning
#> 46 Senior Data Scientist
#> 47 Data Scientist
#> 48 Data Scientist
#> 49 Data Scientist
#> 50 Data Analyst
#> 51 Data Scientist
#> 52 Data Scientist
#> 53 E-Commerce Data Analyst
#> 54 Data Analytics Engineer
#> 55 Senior Data Scientist
#> 56 Product Data Scientist - Ads Data Science
#> 57 Data Scientist
#> 58 Data Scientist - Intermediate
#> 59 Data Analyst
#> 60 Machine Learning Engineer
#> 61 Data Scientist
#> 62 Data Scientist
#> 63 Global Data Analyst
#> 64 Data Engineer
#> 65 Data Scientist
#> 66 Data Scientist
#> 67 Data & Machine Learning Scientist
#> 68 Data Scientist - Machine Learning
#> 69 Data Engineer (Remote)
#> 70 Data Scientist, Applied Machine Learning - Bay Area
#> 71 Principal Data Scientist
#> 72 Business Data Analyst
#> 73 Purification Scientist
#> 74 Data Analyst
#> 75 Data Engineer, Enterprise Analytics
#> 76 Data Scientist
#> 77 Data Scientist
#> 78 Data Scientist 3 (718)
#> 79 Real World Science, Data Scientist
#> 80 Data Scientist
#> 81 Data Engineer
#> 82 Data Scientist - Image and Video Analytics
#> 83 Data Scientist
#> 84 Data Science Manager, Payment Acceptance - USA
#> 85 Data Analyst
#> 86 Data Scientist / Applied Mathematician
#> 87 Machine Learning Engineer
#> 88 Data Scientist
#> 89 Patient Safety- Associate Data Scientist
#> 90 Machine Learning Engineer
#> 91 Senior Data Scientist
#> 92 Data Scientist
#> 93 Staff Data Scientist - Analytics
#> 94 Data Analytics Engineer
#> 95 Data Scientist, Kinship - NYC/Portland
#> 96 Senior Data Scientist
#> 97 Applied Technology Researcher / Data Scientist
#> 98 Machine Learning Engineer
#> 99 Health Data Scientist - Biomedical/Biostats
#> 100 Senior Data Scientist
#> 101 Staff Data Scientist
#> 102 Data Scientist
#> 103 Sr Data Engineer (Sr BI Developer)
#> 104 Data Engineer
#> 105 Lead Data Scientist
#> 106 Data Scientist
#> 107 RFP Data Analyst
#> 108 Data Analyst
#> 109 Data Scientist (TS/SCI)
#> 110 Data Engineer
#> 111 Data Scientist
#> 112 Data Scientist
#> 113 Software Engineer - Data Science
#> 114 Data Analyst/Engineer
#> 115 NGS Scientist
#> 116 Data Engineer
#> 117 Data Scientist
#> 118 Senior Data Engineer
#> 119 Senior Data Scientist
#> 120 Data Engineer
#> 121 Business Intelligence Analyst
#> 122 Sr. ML/Data Scientist - AI/NLP/Chatbot
#> 123 Data Scientist
#> 124 Data Scientist
#> 125 Data Engineer
#> 126 Sr. ML/Data Scientist - AI/NLP/Chatbot
#> 127 Data Scientist
#> 128 Senior Data Engineer
#> 129 Data Engineer
#> 130 Data Scientist
#> 131 Senior Data Engineer
#> 132 Data Scientist
#> 133 Data Analyst
#> 134 Data Engineer
#> 135 Data Integration and Modeling Engineer
#> 136 Tableau Data Engineer 20-0117
#> 137 Senior Data Scientist
#> 138 AI Data Scientist
#> 139 Machine Learning Engineer
#> 140 Research Scientist Patient Preferences (Remote)
#> 141 Data Scientist
#> 142 Scientist - Biomarker and Flow Cytometry
#> 143 Data Scientist
#> 144 Analytics Manager
#> 145 Data Scientist
#> 146 Staff Scientist- Upstream PD
#> 147 Data Engineer
#> 148 Sr Scientist - Extractables & Leachables
#> 149 ELISA RESEARCH SCIENTIST (CV-15)
#> 150 Sr Data Scientist
#> 151 Data Scientist
#> 152 Experienced Data Scientist
#> 153 Machine Learning Engineer
#> 154 Data Scientist
#> 155 Data Scientist
#> 156 Say Business Data Analyst
#> 157 Geospatial Data Scientist
#> 158 Data Analyst
#> 159 Computational Scientist
#> 160 Data Scientist
#> 161 Senior Data Analyst
#> 162 Senior Data Analyst
#> 163 Data Engineer
#> 164 Sr Data Analyst
#> 165 Data Scientist
#> 166 Machine Learning Scientist - Bay Area, CA
#> 167 Data Scientist
#> 168 Senior Data Scientist - Algorithms
#> 169 Data Analyst
#> 170 Senior Data & Machine Learning Scientist
#> 171 Data Engineer
#> 172 Data Scientist - Statistics, Early Career
#> 173 Data Scientist
#> 174 Machine Learning Engineer
#> 175 Research Scientist - Patient-Centered Research (Remote)
#> 176 Jr. Business Data Analyst (position added 6/12/2020)
#> 177 Sr. Data Scientist II
#> 178 Data Scientist
#> 179 Senior Data Analyst
#> 180 Lead Data Scientist
#> 181 Production Engineer - Statistics/Data Analysis
#> 182 Statistical Scientist
#> 183 Computational Behavioral Scientist
#> 184 Principal Data Scientist - Machine Learning
#> 185 Principal Machine Learning Scientist
#> 186 Data Engineer
#> 187 Principal Data Scientist
#> 188 Data Scientist
#> 189 Senior Data Scientist - R&D Oncology
#> 190 Data Scientist
#> 191 Health Plan Data Analyst, Sr
#> 192 Principal Scientist/Associate Director, Quality Control and Analytical Technologies
#> 193 Analytics - Business Assurance Data Analyst
#> 194 Data Scientist
#> 195 Data Scientist
#> 196 Machine Learning Engineer
#> 197 Data Engineer
#> 198 Senior Data Scientist â\200“ Image Analytics, Novartis AI Innovation Lab
#> 199 Data Engineer
#> 200 Data Science Instructor
#> 201 Senior Business Intelligence Analyst
#> 202 In-Line Inspection Data Analyst
#> 203 Data Scientist - TS/SCI FSP or CI Required
#> 204 Data Scientist
#> 205 Senior Data Scientist
#> 206 Data Scientist - TS/SCI Required
#> 207 Data Science Software Engineer
#> 208 ENGINEER - COMPUTER SCIENTIST - RESEARCH COMPUTER SCIENTIST - SIGNAL PROCESSING - SAN ANTONIO OR
#> 209 AI Ops Data Scientist
#> 210 Intelligence Data Analyst, Senior
#> 211 Analytics Manager - Data Mart
#> 212 Data Modeler (Analytical Systems)
#> 213 Senior Machine Learning Scientist - Bay Area, CA
#> 214 Report Writer-Data Analyst
#> 215 Data Scientist/Machine Learning
#> 216 Data Scientist - Risk
#> 217 Data Engineer
#> 218 Staff Data Scientist - Pricing
#> 219 Data Scientist
#> 220 Data Scientist
#> 221 Data Scientist
#> 222 Machine Learning Engineer
#> 223 Equity Data Insights Analyst - Quantitative Analyst
#> 224 Data Scientist
#> 225 Research Scientist - Patient-Centered Research (Remote)
#> 226 Data Scientist
#> 227 Operations Data Analyst
#> 228 Sr. Data Scientist II
#> 229 Data Scientist
#> 230 Data Scientist
#> 231 Senior Data Analyst
#> 232 Data Scientist
#> 233 Data Scientist
#> 234 Senior Research Statistician- Data Scientist
#> 235 Software Data Engineer
#> 236 Data Scientist
#> 237 Real World Evidence (RWE) Scientist
#> 238 Data Scientist
#> 239 Data Scientist
#> 240 Data Scientist
#> 241 Data Scientist
#> 242 Computer Scientist 1
#> 243 Lead Data Scientist
#> 244 Data Scientist
#> 245 Data Scientist
#> 246 Data Scientist
#> 247 Data Scientist
#> 248 Data Scientist
#> 249 Environmental Data Science
#> 250 Computational Behavioral Scientist
#> 251 Data Scientist
#> 252 Staff BI and Data Engineer
#> 253 Principal Machine Learning Scientist
#> 254 Principal Data Scientist - Machine Learning
#> 255 Data Scientist-Human Resources
#> 256 Associate Data Scientist
#> 257 Data Scientist - Statistics, Mid-Career
#> 258 Director of Data Science
#> 259 Data Scientist
#> 260 Data Scientist
#> 261 Data Scientist
#> 262 Data Engineer, Digital & Comp Pathology
#> 263 Data Engineer
#> 264 Manager / Lead, Data Science & Analytics
#> 265 Data Scientist
#> 266 Data Scientist
#> 267 Data Scientist
#> 268 Data Scientist
#> 269 Diversity and Inclusion Data Analyst
#> 270 Data Scientist
#> 271 Data Scientist
#> 272 Data Scientist
#> 273 Data Scientist
#> 274 Data Scientist
#> 275 Data Scientist
#> 276 Senior Analyst/Data Scientist
#> 277 Data Scientist
#> 278 Data Scientist
#> 279 Senior Data Scientist - R&D Oncology
#> 280 Data Scientist
#> 281 Data Scientist
#> 282 Data Scientist
#> 283 Data Scientist
#> 284 Data Scientist
#> 285 Data Scientist Machine Learning
#> 286 Data Scientist
#> 287 Data Scientist
#> 288 Data Scientist
#> 289 Data Scientist
#> 290 Data Analyst
#> 291 Data Scientist
#> 292 Data Scientist
#> 293 Data Scientist
#> 294 Data Scientist
#> 295 Data Scientist
#> 296 Data Scientist
#> 297 Data Scientist
#> 298 Data Scientist
#> 299 Data Scientist
#> 300 Data Scientist
#> 301 Data Scientist
#> 302 Data Scientist
#> 303 Health Plan Data Analyst, Sr
#> 304 Principal Scientist/Associate Director, Quality Control and Analytical Technologies
#> 305 Data Scientist
#> 306 Senior Data Scientist
#> 307 Data Scientist
#> 308 Data Scientist
#> 309 Data Scientist
#> 310 Analytics - Business Assurance Data Analyst
#> 311 Data Scientist
#> 312 Data Scientist
#> 313 Development Scientist, Voltaren
#> 314 Data Scientist
#> 315 Data Scientist
#> 316 Data Scientist
#> 317 Data Engineer
#> 318 Data Scientist
#> 319 Data Scientist
#> 320 Data Scientist
#> 321 Data Scientist
#> 322 Machine Learning Engineer
#> 323 Principal Data & Analytics Platform Engineer
#> 324 Machine Learning Engineer/Scientist
#> 325 Data Scientist
#> 326 Senior Data Scientist â\200“ Image Analytics, Novartis AI Innovation Lab
#> 327 Data Scientist
#> 328 Data Scientist
#> 329 Data Scientist
#> 330 Data Scientist
#> 331 Machine Learning Engineer
#> 332 Data Analyst - Unilever Prestige
#> 333 Data Scientist
#> 334 VP, Data Science
#> 335 Data Scientist
#> 336 Data Scientist
#> 337 Senior Business Intelligence Analyst
#> 338 Data Scientist
#> 339 Data Engineer - Kafka
#> 340 Data Scientist
#> 341 Data Scientist
#> 342 Data Scientist
#> 343 Data Scientist
#> 344 Decision Scientist
#> 345 Data Scientist
#> 346 Data Science All Star Program - Data Engineer Track
#> 347 Data Scientist - TS/SCI FSP or CI Required
#> 348 Data Analyst
#> 349 Data Scientist
#> 350 Data Scientist
#> 351 Data Scientist
#> 352 Data Scientist
#> 353 Data Scientist
#> 354 Data Scientist
#> 355 Senior Data Scientist
#> 356 Data Scientist
#> 357 Data Scientist
#> 358 Data Scientist
#> 359 ENGINEER - COMPUTER SCIENTIST - RESEARCH COMPUTER SCIENTIST - SIGNAL PROCESSING - SAN ANTONIO OR
#> 360 Data Scientist
#> 361 Data Science Software Engineer
#> 362 AI Ops Data Scientist
#> 363 Data Scientist
#> 364 Data Scientist
#> 365 Data Scientist
#> 366 Scientist - Machine Learning
#> 367 Data Scientist
#> 368 Data Scientist
#> 369 Sr. Data Scientist
#> 370 Data Scientist
#> 371 Data Scientist
#> 372 Analytics Manager - Data Mart
#> 373 Data Modeler (Analytical Systems)
#> 374 Applied AI Scientist / Engineer
#> 375 Data Engineer (Analytics, SQL, Python, AWS)
#> 376 Senior Data Analyst - Finance & Platform Analytics
#> 377 Senior Data Scientist
#> 378 Market Research Data Scientist
#> 379 IT Partner Digital Health Technology and Data Science
#> 380 Data Scientist
#> 381 Data Scientist
#> 382 Senior Machine Learning Scientist - Bay Area, CA
#> 383 Software Engineer (Data Scientist, C,C++,Linux,Unix) - SISW - MG
#> 384 Senior Clinical Data Scientist Programmer
#> 385 Data Engineer
#> 386 Computer Vision / Deep Learning Scientist
#> 387 Data Solutions Engineer - Data Modeler
#> 388 Data Scientist
#> 389 Data Scientist (TS/SCI w/ Poly)
#> 390 Weapons and Sensors Engineer/Scientist
#> 391 Applied Computer Scientist
#> 392 Senior Data Analyst
#> 393 Cloud Data Engineer (Azure)
#> 394 Lead Certified Clinical Laboratory Scientist - Saturday - Tuesday, 8:00pm - 6:30am shift
#> 395 Sr. Data Analyst
#> 396 Senior Scientist - Toxicologist - Product Integrity (Stewardship)
#> 397 Senior Machine Learning Engineer
#> 398 Product Data Scientist - Ads Data Science
#> 399 Data Scientist
#> 400 Data Scientist
#> 401 Data Scientist
#> 402 Data Scientist
#> 403 Data Scientist
#> 404 Data Scientist
#> 405 Data Scientist
#> 406 Data Scientist
#> 407 Data Scientist
#> 408 Data Scientist
#> 409 Data Scientist
#> 410 Data Scientist
#> 411 Data Scientist
#> 412 Data Scientist
#> 413 Analytics - Business Assurance Data Analyst
#> 414 Data Scientist
#> 415 Data Scientist
#> 416 Data Scientist
#> 417 Data Scientist
#> 418 Data Scientist
#> 419 Data Scientist
#> 420 Senior Data Scientist
#> 421 Data Scientist
#> 422 Data Scientist
#> 423 Data Scientist
#> 424 Data Engineer
#> 425 Data Scientist
#> 426 Data Scientist
#> 427 Data Scientist
#> 428 Data Scientist - Machine Learning
#> 429 Data Scientist
#> 430 Data Scientist
#> 431 Data Scientist
#> 432 Data Scientist
#> 433 Data Scientist
#> 434 Data Scientist
#> 435 Data Scientist
#> 436 Data Scientist
#> 437 Machine Learning Engineer/Scientist
#> 438 Data Scientist - Intermediate
#> 439 Principal Data Scientist
#> 440 Data Scientist
#> 441 Senior Data Scientist â\200“ Image Analytics, Novartis AI Innovation Lab
#> 442 Data Scientist
#> 443 Data Scientist
#> 444 Data Scientist- Industrial Discrete Sector Industry
#> 445 Data Scientist
#> 446 Data Scientist
#> 447 Data Scientist
#> 448 Data Scientist
#> 449 Data Scientist
#> 450 Machine Learning Engineer
#> 451 Data Scientist
#> 452 Data Scientist
#> 453 Data Scientist
#> 454 Data Scientist
#> 455 Data Analyst - Unilever Prestige
#> 456 Data Scientist
#> 457 Data Scientist
#> 458 Data Scientist
#> 459 Data & Machine Learning Scientist
#> 460 Data Scientist
#> 461 VP, Data Science
#> 462 Senior Business Intelligence Analyst
#> 463 Data Scientist
#> 464 Data Engineer - Kafka
#> 465 Data Scientist
#> 466 Data Scientist
#> 467 Data Scientist
#> 468 Data Scientist
#> 469 Data Scientist
#> 470 Data Scientist
#> 471 In-Line Inspection Data Analyst
#> 472 Data Scientist
#> 473 Data Scientist
#> 474 Data Scientist
#> 475 Data Scientist
#> 476 Decision Scientist
#> 477 Data Scientist
#> 478 Data Scientist - TS/SCI FSP or CI Required
#> 479 Data Scientist, Applied Machine Learning - Bay Area
#> 480 Data Scientist
#> 481 Data Scientist
#> 482 Data Scientist
#> 483 Data Analyst
#> 484 Data Scientist
#> 485 Data Scientist
#> 486 Data Scientist
#> 487 Data Scientist
#> 488 Data Scientist
#> 489 Data Scientist
#> 490 Data Scientist
#> 491 Data Scientist
#> 492 Data Scientist
#> 493 Data Scientist
#> 494 Data Scientist
#> 495 Data Scientist
#> 496 Data Scientist
#> 497 Data Scientist
#> 498 Data Scientist
#> 499 Data Scientist - TS/SCI Required
#> 500 Senior Principal Data Scientist (Python/R)
#> 501 Real World Science, Data Scientist
#> 502 Data Scientist
#> 503 Data Scientist(s)/Machine Learning Engineer
#> 504 Senior Data Scientist
#> 505 Data Scientist
#> 506 Data Scientist
#> 507 Data Scientist
#> 508 ENGINEER - COMPUTER SCIENTIST - RESEARCH COMPUTER SCIENTIST - SIGNAL PROCESSING - SAN ANTONIO OR
#> 509 Data Scientist
#> 510 Data Science Software Engineer
#> 511 Scientist / Group Lead, Cancer Biology
#> 512 Data Scientist
#> 513 Data Scientist
#> 514 Manager, Field Application Scientist, Southeast
#> 515 COMPUTER SCIENTIST - ENGINEER - RESEARCH COMPUTER SCIENTIST - SIGNAL PROCESSING
#> 516 Data Scientist
#> 517 Data Scientist
#> 518 Scientist - Machine Learning
#> 519 Data Scientist
#> 520 Data Scientist
#> 521 Data Scientist - Image and Video Analytics
#> 522 Data Scientist
#> 523 Data Scientist
#> 524 Data Scientist
#> 525 Senior Data Engineer
#> 526 Data Scientist
#> 527 Machine Learning Scientist / Engineer
#> 528 Data Scientist
#> 529 Sr. Data Scientist
#> 530 Intelligence Data Analyst, Senior
#> 531 Data Science Analyst
#> 532 Data Scientist
#> 533 Data Scientist
#> 534 COMPUTER SCIENTIST - ENGINEER - RESEARCH COMPUTER SCIENTIST - TRANSPORTATION TECHNOLOGY
#> 535 Data Scientist
#> 536 Data Modeler (Analytical Systems)
#> 537 Data Scientist
#> 538 Data Scientist
#> 539 Data Scientist
#> 540 Data Engineer (Analytics, SQL, Python, AWS)
#> 541 Data Scientist 3 (718)
#> 542 Data Scientist
#> 543 Senior Data Scientist
#> 544 Senior Data Analyst - Finance & Platform Analytics
#> 545 Software Engineer - Machine Learning & Data Science (Applied Intelligence Services Team)
#> 546 IT Partner Digital Health Technology and Data Science
#> 547 Data Scientist
#> 548 Senior Machine Learning Scientist - Bay Area, CA
#> 549 Data Scientist
#> 550 Clinical Data Analyst
#> 551 Data Scientist
#> 552 Data Engineer
#> 553 Data Scientist Technical Specialist
#> 554 Senior Data Scientist
#> 555 Data Science Manager
#> 556 Computer Vision / Deep Learning Scientist
#> 557 Big Data Engineer
#> 558 Data Architect
#> 559 Machine Learning Engineer
#> 560 Data Scientist
#> 561 Aviation AI/ML Data Scientist
#> 562 Data Scientist
#> 563 Data Scientist / Applied Mathematician
#> 564 Machine Learning Engineer, Sr.
#> 565 Data Scientist (TS/SCI w/ Poly)
#> 566 Weapons and Sensors Engineer/Scientist
#> 567 Applied Computer Scientist
#> 568 Senior Data Analyst
#> 569 Information Systems Engineering Specialist (Engineering Scientist)
#> 570 Cloud Data Engineer (Azure)
#> 571 Scientist/Research Associate-Metabolic Engineering
#> 572 Vice President, Biometrics and Clinical Data Management
#> 573 Enterprise Data Analyst (Enterprise Portfolio Management Office)
#> 574 Senior Scientist - Toxicologist - Product Integrity (Stewardship)
#> 575 Lead Data Scientist â\200“ Network Analysis and Control
#> 576 Senior Machine Learning Engineer
#> 577 Sr. Research Associate/ Scientist, NGS prep & Molecular Genomics
#> 578 Data Engineer
#> 579 Developer III - Data Science
#> 580 Senior Data Engineer
#> 581 Hydrogen/Tritium Materials Scientist (Experienced)
#> 582 Data Engineer
#> 583 Patient Safety- Associate Data Scientist
#> 584 Senior Data Scientist
#> 585 Data Scientist
#> 586 Data Scientist
#> 587 Data Scientist
#> 588 Data Scientist
#> 589 Data Scientist
#> 590 Data Scientist
#> 591 Data Scientist
#> 592 Data Scientist
#> 593 Decision Scientist
#> 594 Data Scientist - TS/SCI FSP or CI Required
#> 595 Data Scientist
#> 596 Data Scientist
#> 597 Data Scientist
#> 598 Data Scientist
#> 599 Data Scientist
#> 600 Data Scientist
#> 601 Data Scientist
#> 602 Senior Data Scientist
#> 603 Data Scientist
#> 604 Data Scientist
#> 605 Data Scientist
#> 606 Data Scientist
#> 607 Data Scientist
#> 608 Data Scientist
#> 609 Data Scientist
#> 610 Data Scientist
#> 611 Data Scientist
#> 612 Data Engineer
#> 613 Data Scientist
#> 614 Data Scientist
#> 615 Data Scientist
#> 616 Data Scientist/Data Analytics Practitioner
#> 617 Data Scientist
#> 618 Data Scientist
#> 619 Data Scientist
#> 620 Data Scientist
#> 621 Data Scientist, Kinship - NYC/Portland
#> 622 Data Scientist
#> 623 Data Scientist
#> 624 Data Scientist
#> 625 Data Scientist - TS/SCI Required
#> 626 Data Scientist
#> 627 Senior Data Scientist
#> 628 Data Scientist
#> 629 Data Scientist(s)/Machine Learning Engineer
#> 630 Data Scientist
#> 631 Data Engineer
#> 632 Data Scientist
#> 633 Data Scientist
#> 634 AI/ML - Machine Learning Scientist, Siri Understanding
#> 635 Data Scientist
#> 636 ENGINEER - COMPUTER SCIENTIST - RESEARCH COMPUTER SCIENTIST - SIGNAL PROCESSING - SAN ANTONIO OR
#> 637 Data Scientist
#> 638 Data Science Software Engineer
#> 639 Scientist / Group Lead, Cancer Biology
#> 640 AI Ops Data Scientist
#> 641 Senior Data Scientist
#> 642 Sr. ML/Data Scientist - AI/NLP/Chatbot
#> 643 Data Scientist
#> 644 Data Scientist
#> 645 Data Scientist
#> 646 Data Scientist
#> 647 Data Scientist
#> 648 Data Scientist
#> 649 Data Scientist
#> 650 Data Scientist
#> 651 Data Scientist
#> 652 Data Scientist
#> 653 Data Scientist
#> 654 Data Scientist
#> 655 Data Scientist
#> 656 Data Scientist
#> 657 Data Scientist
#> 658 Data Scientist
#> 659 Data Scientist
#> 660 Data Scientist
#> Salary.Estimate Rating Company.Name
#> 1 137-171 3.1 Healthfirst
#> 2 137-171 4.2 ManTech
#> 3 137-171 3.8 Analysis Group
#> 4 137-171 3.5 INFICON
#> 5 137-171 2.9 Affinity Solutions
#> 6 137-171 4.2 HG Insights
#> 7 137-171 3.9 Novartis
#> 8 137-171 3.5 iRobot
#> 9 137-171 4.4 Intuit - Data
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#> 572 138-158 4.8 Alector
#> 573 138-158 3.9 Navy Federal Credit Union
#> 574 138-158 3.3 Reynolds American
#> 575 138-158 3.3 MITRE
#> 576 138-158 5.0 LifeOmic
#> 577 138-158 4.4 Homology Medicines, Inc.
#> 578 138-158 3.4 Spectrum Communications and Consulting
#> 579 138-158 3.3 Inland Empire Health Plan
#> 580 138-158 2.9 Affinity Solutions
#> 581 138-158 3.8 Sandia National Laboratories
#> 582 138-158 3.5 Maxar Technologies
#> 583 80-132 4.0 AstraZeneca
#> 584 80-132 4.0 Autodesk
#> 585 80-132 3.4 USI
#> 586 80-132 3.9 GSK
#> 587 80-132 3.8 CIA
#> 588 80-132 4.5 Maven Wave Partners
#> 589 80-132 4.3 Ovative Group
#> 590 80-132 3.6 Kognetics
#> 591 80-132 2.9 Envision Healthcare
#> 592 80-132 2.9 Pragmatics, Inc.
#> 593 80-132 4.5 Johns Hopkins University Applied Physics Laboratory
#> 594 80-132 5.0 Phoenix Operations Group
#> 595 80-132 3.5 Crossover Health
#> 596 80-132 4.0 Lorven Technologies Inc
#> 597 80-132 4.4 Meridian Knowledge Solutions
#> 598 80-132 4.2 UST Global
#> 599 80-132 3.2 IMG Systems
#> 600 80-132 3.4 Trident Systems Inc
#> 601 80-132 4.7 Tygart Technology, Inc
#> 602 87-141 4.2 ManTech
#> 603 87-141 0.0 GrainBridge, LLC
#> 604 87-141 3.2 First Health Group
#> 605 87-141 0.0 Sprezzatura Management Consulting
#> 606 87-141 2.8 Progress Rail, A Caterpillar Company
#> 607 87-141 4.2 UST Global
#> 608 87-141 4.5 Axiologic Solutions
#> 609 87-141 3.0 Indigo Slate
#> 610 87-141 3.3 Cubic
#> 611 87-141 4.0 Advanced Bio-Logic Solutions Corp
#> 612 87-141 3.5 Alignment Healthcare
#> 613 87-141 3.5 WGSN
#> 614 87-141 3.6 ISYS Technologies, Inc.
#> 615 87-141 3.0 TransVoyant
#> 616 87-141 4.0 Noblis
#> 617 87-141 4.6 Murray Resources
#> 618 87-141 4.3 Geotab
#> 619 87-141 4.7 CRS Group
#> 620 87-141 3.7 EGlobalTech
#> 621 92-155 3.9 Mars
#> 622 92-155 3.0 Central Business Solutions, Inc
#> 623 92-155 2.5 KeHE Distributors
#> 624 92-155 3.5 Blackstone Talent Group
#> 625 92-155 5.0 Phoenix Operations Group
#> 626 92-155 0.0 Sophinea
#> 627 92-155 3.5 Maxar Technologies
#> 628 92-155 3.0 Moxie Software
#> 629 92-155 5.0 Blue Horizon Tek Solutions
#> 630 92-155 4.7 Unicom Technologies INC
#> 631 92-155 3.3 Americo Life
#> 632 92-155 3.3 Tokio Marine HCC
#> 633 92-155 3.5 CACI International
#> 634 92-155 4.1 Apple
#> 635 92-155 3.5 Maxar Technologies
#> 636 92-155 3.9 Southwest Research Institute
#> 637 92-155 4.7 Compass Consulting Group
#> 638 92-155 4.8 Klaviyo
#> 639 92-155 0.0 Monte Rosa Therapeutics
#> 640 92-155 4.0 AstraZeneca
#> 641 105-167 3.8 GutCheck
#> 642 105-167 3.8 Juniper Networks
#> 643 105-167 2.7 Hexagon US Federal
#> 644 105-167 4.0 Comtech Global Inc
#> 645 105-167 0.0 Berico Technologies
#> 646 105-167 0.0 Kehe Food Distributors
#> 647 105-167 3.8 Aveshka, Inc.
#> 648 105-167 3.2 Perspecta
#> 649 105-167 0.0 Pactera Edge
#> 650 105-167 3.6 Qurate Retail Group
#> 651 105-167 4.4 SolutionIT, Inc.
#> 652 105-167 4.1 A-Line Staffing Solutions
#> 653 105-167 0.0 Clear Ridge Defense
#> 654 105-167 3.8 Criterion Systems, Inc.
#> 655 105-167 4.0 Foundation Medicine
#> 656 105-167 3.6 TRANZACT
#> 657 105-167 0.0 JKGT
#> 658 105-167 0.0 AccessHope
#> 659 105-167 5.0 ChaTeck Incorporated
#> 660 105-167 2.7 1-800-Flowers
#> Location Size
#> 1 New York, NY 1001 to 5000 employees
#> 2 Chantilly, VA 5001 to 10000 employees
#> 3 Boston, MA 1001 to 5000 employees
#> 4 Newton, MA 501 to 1000 employees
#> 5 New York, NY 51 to 200 employees
#> 6 Santa Barbara, CA 51 to 200 employees
#> 7 Cambridge, MA 10000+ employees
#> 8 Bedford, MA 1001 to 5000 employees
#> 9 San Diego, CA 5001 to 10000 employees
#> 10 Chicago, IL 51 to 200 employees
#> 11 Herndon, VA 501 to 1000 employees
#> 12 Saint Louis, MO 51 to 200 employees
#> 13 Richland, WA 1001 to 5000 employees
#> 14 Northbrook, IL 201 to 500 employees
#> 15 Washington, DC 1001 to 5000 employees
#> 16 Washington, DC 1 to 50 employees
#> 17 Memphis, TN 1001 to 5000 employees
#> 18 Plano, TX 5001 to 10000 employees
#> 19 West Grove, PA 5001 to 10000 employees
#> 20 New York, NY 51 to 200 employees
#> 21 New York, NY 51 to 200 employees
#> 22 Phoenix, AZ 1001 to 5000 employees
#> 23 Appleton, WI 5001 to 10000 employees
#> 24 Chicago, IL 51 to 200 employees
#> 25 Atlanta, GA 51 to 200 employees
#> 26 Orlando, FL 51 to 200 employees
#> 27 Lexington, MA 1001 to 5000 employees
#> 28 McLean, VA 51 to 200 employees
#> 29 San Francisco, CA 51 to 200 employees
#> 30 Boston, MA 1001 to 5000 employees
#> 31 Cambridge, MA 10000+ employees
#> 32 Boston, MA 1 to 50 employees
#> 33 Sheboygan, WI 1001 to 5000 employees
#> 34 United States 201 to 500 employees
#> 35 Bothell, WA 1001 to 5000 employees
#> 36 Lincoln, NE 1001 to 5000 employees
#> 37 Overland Park, KS 501 to 1000 employees
#> 38 Chantilly, VA 1 to 50 employees
#> 39 Santa Monica, CA 501 to 1000 employees
#> 40 Boston, MA 501 to 1000 employees
#> 41 Portsmouth, NH 10000+ employees
#> 42 Ewing, NJ 1001 to 5000 employees
#> 43 South San Francisco, CA 51 to 200 employees
#> 44 Palo Alto, CA 51 to 200 employees
#> 45 Cambridge, MA 10000+ employees
#> 46 Boston, MA 201 to 500 employees
#> 47 Bellevue, WA 501 to 1000 employees
#> 48 San Francisco, CA 1001 to 5000 employees
#> 49 Cambridge, MA 201 to 500 employees
#> 50 San Francisco, CA 1 to 50 employees
#> 51 Cambridge, MA 10000+ employees
#> 52 San Francisco, CA 501 to 1000 employees
#> 53 New Orleans, LA 51 to 200 employees
#> 54 Akron, OH 5001 to 10000 employees
#> 55 Saint Louis, MO 201 to 500 employees
#> 56 San Francisco, CA 1001 to 5000 employees
#> 57 San Francisco, CA 1001 to 5000 employees
#> 58 Saint Louis, MO 201 to 500 employees
#> 59 Fort Wayne, IN 10000+ employees
#> 60 Woburn, MA 201 to 500 employees
#> 61 Bedford, MA 1001 to 5000 employees
#> 62 Santa Barbara, CA 51 to 200 employees
#> 63 Carson, CA 201 to 500 employees
#> 64 Coral Gables, FL 1001 to 5000 employees
#> 65 New York, NY 501 to 1000 employees
#> 66 Santa Clara, CA 1001 to 5000 employees
#> 67 Chicago, IL 501 to 1000 employees
#> 68 Brisbane, CA 1 to 50 employees
#> 69 Winter Park, FL 51 to 200 employees
#> 70 Redwood City, CA 501 to 1000 employees
#> 71 San Francisco, CA 5001 to 10000 employees
#> 72 Peoria, IL 10000+ employees
#> 73 Ipswich, MA 201 to 500 employees
#> 74 Carmel, IN 1001 to 5000 employees
#> 75 New York, NY 1001 to 5000 employees
#> 76 Washington, DC 1001 to 5000 employees
#> 77 New York, NY 201 to 500 employees
#> 78 Emeryville, CA 201 to 500 employees
#> 79 Gaithersburg, MD 10000+ employees
#> 80 Washington, DC 51 to 200 employees
#> 81 Saint Louis, MO 51 to 200 employees
#> 82 Longmont, CO 1 to 50 employees
#> 83 Austin, TX 51 to 200 employees
#> 84 San Francisco, CA 1001 to 5000 employees
#> 85 Yakima, WA 501 to 1000 employees
#> 86 Santa Cruz, CA 51 to 200 employees
#> 87 Boston, MA 201 to 500 employees
#> 88 United States 10000+ employees
#> 89 Gaithersburg, MD 10000+ employees
#> 90 Springfield, VA 51 to 200 employees
#> 91 Alexandria, VA 5001 to 10000 employees
#> 92 Washington, DC 1001 to 5000 employees
#> 93 San Diego, CA 5001 to 10000 employees
#> 94 Reston, VA 51 to 200 employees
#> 95 New York, NY 10000+ employees
#> 96 Denver, CO 51 to 200 employees
#> 97 Washington, DC 1001 to 5000 employees
#> 98 Aurora, CO 1 to 50 employees
#> 99 Santa Barbara, CA 51 to 200 employees
#> 100 San Francisco, CA 5001 to 10000 employees
#> 101 San Diego, CA 5001 to 10000 employees
#> 102 Hill AFB, UT 501 to 1000 employees
#> 103 Chandler, AZ 501 to 1000 employees
#> 104 Indianapolis, IN 51 to 200 employees
#> 105 San Diego, CA 10000+ employees
#> 106 Chantilly, VA 10000+ employees
#> 107 Nashville, TN 1001 to 5000 employees
#> 108 Timonium, MD 51 to 200 employees
#> 109 Herndon, VA 5001 to 10000 employees
#> 110 Burlingame, CA 1 to 50 employees
#> 111 Reston, VA 1001 to 5000 employees
#> 112 New York, NY 1 to 50 employees
#> 113 Annapolis Junction, MD 51 to 200 employees
#> 114 Nashville, TN 1001 to 5000 employees
#> 115 South San Francisco, CA 51 to 200 employees
#> 116 Bethesda, MD 201 to 500 employees
#> 117 Dayton, OH 51 to 200 employees
#> 118 San Francisco, CA 51 to 200 employees
#> 119 Herndon, VA 501 to 1000 employees
#> 120 Winter Park, FL 51 to 200 employees
#> 121 Schaumburg, IL 51 to 200 employees
#> 122 Cupertino, CA 5001 to 10000 employees
#> 123 Saint Louis, MO 51 to 200 employees
#> 124 Chicago, IL 51 to 200 employees
#> 125 Winter Park, FL 51 to 200 employees
#> 126 Cupertino, CA 5001 to 10000 employees
#> 127 San Francisco, CA 1 to 50 employees
#> 128 Lehi, UT 201 to 500 employees
#> 129 Culver City, CA 51 to 200 employees
#> 130 Lake Oswego, OR 1001 to 5000 employees
#> 131 Lehi, UT 201 to 500 employees
#> 132 San Mateo, CA 201 to 500 employees
#> 133 San Francisco, CA 201 to 500 employees
#> 134 Palo Alto, CA 201 to 500 employees
#> 135 Holyoke, MA 501 to 1000 employees
#> 136 Chicago, IL 1001 to 5000 employees
#> 137 San Francisco, CA 51 to 200 employees
#> 138 McLean, VA 5001 to 10000 employees
#> 139 Herndon, VA 1 to 50 employees
#> 140 Bethesda, MD 501 to 1000 employees
#> 141 Woodbridge, NJ 1001 to 5000 employees
#> 142 San Diego, CA 501 to 1000 employees
#> 143 Cambridge, MA 51 to 200 employees
#> 144 Dearborn, MI 201 to 500 employees
#> 145 Maryland Heights, MO 10000+ employees
#> 146 Rockville, MD 201 to 500 employees
#> 147 Carpinteria, CA 1001 to 5000 employees
#> 148 Columbia, SC 201 to 500 employees
#> 149 Hauppauge, NY -1
#> 150 Saint Louis, MO 10000+ employees
#> 151 Herndon, VA 501 to 1000 employees
#> 152 Washington, DC 1 to 50 employees
#> 153 Reston, VA -1
#> 154 Fort Meade, MD 10000+ employees
#> 155 San Francisco, CA 201 to 500 employees
#> 156 Columbia, MO 1001 to 5000 employees
#> 157 Chantilly, VA 5001 to 10000 employees
#> 158 Washington, DC 501 to 1000 employees
#> 159 Vicksburg, MS 10000+ employees
#> 160 Chandler, AZ 201 to 500 employees
#> 161 San Francisco, CA 5001 to 10000 employees
#> 162 San Francisco, CA 5001 to 10000 employees
#> 163 Birmingham, AL 10000+ employees
#> 164 Blue Bell, PA 1001 to 5000 employees
#> 165 Chicago, IL 5001 to 10000 employees
#> 166 Redwood City, CA 501 to 1000 employees
#> 167 Redwood City, CA 201 to 500 employees
#> 168 New York, NY 201 to 500 employees
#> 169 San Diego, CA 1001 to 5000 employees
#> 170 Chicago, IL 501 to 1000 employees
#> 171 Cincinnati, OH 201 to 500 employees
#> 172 Richland, WA 1001 to 5000 employees
#> 173 Memphis, TN 1001 to 5000 employees
#> 174 Memphis, TN 501 to 1000 employees
#> 175 Bethesda, MD 501 to 1000 employees
#> 176 Harrisburg, PA 201 to 500 employees
#> 177 New York, NY 1001 to 5000 employees
#> 178 Oak Ridge, TN 5001 to 10000 employees
#> 179 San Francisco, CA 51 to 200 employees
#> 180 Boston, MA 5001 to 10000 employees
#> 181 San Carlos, CA 501 to 1000 employees
#> 182 South San Francisco, CA 10000+ employees
#> 183 Herndon, VA 201 to 500 employees
#> 184 Waltham, MA Unknown
#> 185 San Francisco, CA 501 to 1000 employees
#> 186 Santa Clara, CA 51 to 200 employees
#> 187 Gaithersburg, MD 10000+ employees
#> 188 San Francisco, CA Unknown
#> 189 New York, NY 10000+ employees
#> 190 San Carlos, CA 51 to 200 employees
#> 191 Fort Worth, TX 5001 to 10000 employees
#> 192 Smithfield, RI 201 to 500 employees
#> 193 Cedar Rapids, IA 501 to 1000 employees
#> 194 Chicago, IL 51 to 200 employees
#> 195 McLean, VA 51 to 200 employees
#> 196 Fort Belvoir, VA 501 to 1000 employees
#> 197 San Francisco, CA 51 to 200 employees
#> 198 Cambridge, MA 10000+ employees
#> 199 Linthicum Heights, MD 1001 to 5000 employees
#> 200 Herndon, VA 501 to 1000 employees
#> 201 Maple Plain, MN 1001 to 5000 employees
#> 202 Tulsa, OK 201 to 500 employees
#> 203 Annapolis Junction, MD 1 to 50 employees
#> 204 Newton, MA 201 to 500 employees
#> 205 Herndon, VA 5001 to 10000 employees
#> 206 Baltimore, MD 1 to 50 employees
#> 207 Boston, MA 201 to 500 employees
#> 208 Oklahoma City, OK 1001 to 5000 employees
#> 209 Gaithersburg, MD 10000+ employees
#> 210 Fort Belvoir, VA 501 to 1000 employees
#> 211 Scotts Valley, CA 501 to 1000 employees
#> 212 Boston, MA 5001 to 10000 employees
#> 213 Redwood City, CA 501 to 1000 employees
#> 214 Spartanburg, SC 1001 to 5000 employees
#> 215 New York, NY 51 to 200 employees
#> 216 Orlando, FL 51 to 200 employees
#> 217 Cincinnati, OH 201 to 500 employees
#> 218 San Francisco, CA 501 to 1000 employees
#> 219 Hartford, CT 51 to 200 employees
#> 220 Chicago, IL 1001 to 5000 employees
#> 221 United States 51 to 200 employees
#> 222 Beavercreek, OH 501 to 1000 employees
#> 223 Baltimore, MD 5001 to 10000 employees
#> 224 Norfolk, VA 1 to 50 employees
#> 225 Bethesda, MD 501 to 1000 employees
#> 226 Oak Ridge, TN 5001 to 10000 employees
#> 227 Charlotte, NC 1001 to 5000 employees
#> 228 New York, NY 1001 to 5000 employees
#> 229 Champaign, IL 10000+ employees
#> 230 San Francisco, CA 1 to 50 employees
#> 231 San Francisco, CA 51 to 200 employees
#> 232 Plano, TX 1 to 50 employees
#> 233 Atlanta, GA 51 to 200 employees
#> 234 Sheboygan, WI 1001 to 5000 employees
#> 235 New York, NY 201 to 500 employees
#> 236 Palo Alto, CA 201 to 500 employees
#> 237 Hoboken, NJ 1 to 50 employees
#> 238 Cambridge, MA 10000+ employees
#> 239 Lebanon, IN 10000+ employees
#> 240 Oakland, CA 51 to 200 employees
#> 241 San Francisco, CA 1 to 50 employees
#> 242 Melbourne, FL 201 to 500 employees
#> 243 Boston, MA 5001 to 10000 employees
#> 244 Birmingham, AL 1 to 50 employees
#> 245 Cleveland, OH 10000+ employees
#> 246 New York, NY 1001 to 5000 employees
#> 247 New York, NY 201 to 500 employees
#> 248 San Francisco, CA 201 to 500 employees
#> 249 Norwell, MA 10000+ employees
#> 250 Herndon, VA 201 to 500 employees
#> 251 New York, NY Unknown
#> 252 San Jose, CA 51 to 200 employees
#> 253 San Francisco, CA 501 to 1000 employees
#> 254 Waltham, MA Unknown
#> 255 Lexington, MA 1001 to 5000 employees
#> 256 Bothell, WA 1001 to 5000 employees
#> 257 Richland, WA 1001 to 5000 employees
#> 258 Boston, MA 1001 to 5000 employees
#> 259 Rockville, MD 1001 to 5000 employees
#> 260 Piscataway, NJ 201 to 500 employees
#> 261 San Jose, CA 10000+ employees
#> 262 New York, NY 1 to 50 employees
#> 263 Santa Clara, CA 51 to 200 employees
#> 264 Danvers, MA 1001 to 5000 employees
#> 265 New York, NY 10000+ employees
#> 266 Vienna, VA 51 to 200 employees
#> 267 San Francisco, CA Unknown
#> 268 Livermore, CA 1001 to 5000 employees
#> 269 Pittsburgh, PA 201 to 500 employees
#> 270 Herndon, VA 10000+ employees
#> 271 Boston, MA 201 to 500 employees
#> 272 Northbrook, IL 10000+ employees
#> 273 Irvine, CA 1001 to 5000 employees
#> 274 Irvine, CA 51 to 200 employees
#> 275 San Francisco, CA Unknown
#> 276 Santa Monica, CA 501 to 1000 employees
#> 277 Boston, MA 1 to 50 employees
#> 278 San Francisco, CA Unknown
#> 279 New York, NY 10000+ employees
#> 280 Oshkosh, WI 10000+ employees
#> 281 San Carlos, CA 51 to 200 employees
#> 282 Menlo Park, CA 51 to 200 employees
#> 283 San Francisco, CA 51 to 200 employees
#> 284 Dallas, TX 201 to 500 employees
#> 285 Arlington, VA 10000+ employees
#> 286 San Francisco, CA 1001 to 5000 employees
#> 287 Monroe, WI 501 to 1000 employees
#> 288 Sacramento, CA 10000+ employees
#> 289 New York, NY 1 to 50 employees
#> 290 Reston, VA 1 to 50 employees
#> 291 New York, NY 1001 to 5000 employees
#> 292 Hampton, VA 10000+ employees
#> 293 Richmond, VA 10000+ employees
#> 294 New York, NY 501 to 1000 employees
#> 295 Monterey, CA 501 to 1000 employees
#> 296 Chicago, IL 51 to 200 employees
#> 297 McLean, VA 51 to 200 employees
#> 298 Woodlawn, MD 201 to 500 employees
#> 299 United States 1 to 50 employees
#> 300 New York, NY Unknown
#> 301 Reston, VA 5001 to 10000 employees
#> 302 New York, NY 51 to 200 employees
#> 303 Fort Worth, TX 5001 to 10000 employees
#> 304 Smithfield, RI 201 to 500 employees
#> 305 Atlanta, GA 201 to 500 employees
#> 306 San Francisco, CA 201 to 500 employees
#> 307 Ann Arbor, MI 1001 to 5000 employees
#> 308 Pittsburgh, PA 51 to 200 employees
#> 309 New York, NY 51 to 200 employees
#> 310 Cedar Rapids, IA 501 to 1000 employees
#> 311 New York, NY 1 to 50 employees
#> 312 Concord, CA 51 to 200 employees
#> 313 Richmond, VA 10000+ employees
#> 314 Cincinnati, OH 1 to 50 employees
#> 315 San Francisco, CA 1001 to 5000 employees
#> 316 Bellevue, WA 501 to 1000 employees
#> 317 San Francisco, CA 51 to 200 employees
#> 318 Oakland, CA 1001 to 5000 employees
#> 319 San Francisco, CA 201 to 500 employees
#> 320 Boston, MA 51 to 200 employees
#> 321 Reston, VA 51 to 200 employees
#> 322 Fort Belvoir, VA 501 to 1000 employees
#> 323 Durham, NC 1001 to 5000 employees
#> 324 New York, NY 501 to 1000 employees
#> 325 McLean, VA 1 to 50 employees
#> 326 Cambridge, MA 10000+ employees
#> 327 Chicago, IL 501 to 1000 employees
#> 328 Kent, OH 10000+ employees
#> 329 Reston, VA 501 to 1000 employees
#> 330 San Francisco, CA 1 to 50 employees
#> 331 Pittsburgh, PA 501 to 1000 employees
#> 332 Carson, CA 201 to 500 employees
#> 333 New York, NY 1 to 50 employees
#> 334 New York, NY 51 to 200 employees
#> 335 Santa Clara, CA 501 to 1000 employees
#> 336 Palo Alto, CA 51 to 200 employees
#> 337 Maple Plain, MN 1001 to 5000 employees
#> 338 Cincinnati, OH 1001 to 5000 employees
#> 339 San Jose, CA 10000+ employees
#> 340 Santa Clara, CA 1 to 50 employees
#> 341 New York, NY 1 to 50 employees
#> 342 Chantilly, VA 201 to 500 employees
#> 343 Palo Alto, CA -1
#> 344 Laurel, MD 5001 to 10000 employees
#> 345 Cambridge, MA 1 to 50 employees
#> 346 Columbia, MD 51 to 200 employees
#> 347 Annapolis Junction, MD 1 to 50 employees
#> 348 Falls Church, VA 51 to 200 employees
#> 349 San Francisco, CA -1
#> 350 San Francisco, CA -1
#> 351 San Francisco, CA -1
#> 352 San Francisco, CA -1
#> 353 San Francisco, CA -1
#> 354 San Francisco, CA -1
#> 355 Herndon, VA 5001 to 10000 employees
#> 356 Cambridge, MA 201 to 500 employees
#> 357 Cambridge, MA 10000+ employees
#> 358 Herndon, VA 5001 to 10000 employees
#> 359 Oklahoma City, OK 1001 to 5000 employees
#> 360 Thousand Oaks, CA 51 to 200 employees
#> 361 Boston, MA 201 to 500 employees
#> 362 Gaithersburg, MD 10000+ employees
#> 363 Edison, NJ 51 to 200 employees
#> 364 Adelphi, MD 10000+ employees
#> 365 Washington, DC 1 to 50 employees
#> 366 Seattle, WA 201 to 500 employees
#> 367 Plano, TX 1001 to 5000 employees
#> 368 Sunnyvale, CA 1 to 50 employees
#> 369 Denver, CO 201 to 500 employees
#> 370 Thousand Oaks, CA 1001 to 5000 employees
#> 371 McLean, VA 5001 to 10000 employees
#> 372 Scotts Valley, CA 501 to 1000 employees
#> 373 Boston, MA 5001 to 10000 employees
#> 374 Boston, MA 1 to 50 employees
#> 375 Hartford, CT 10000+ employees
#> 376 San Francisco, CA 1001 to 5000 employees
#> 377 Boston, MA 201 to 500 employees
#> 378 Denver, CO 51 to 200 employees
#> 379 Cambridge, MA 5001 to 10000 employees
#> 380 San Francisco, CA -1
#> 381 San Francisco, CA -1
#> 382 Redwood City, CA 501 to 1000 employees
#> 383 Fremont, CA 5001 to 10000 employees
#> 384 Hamilton, NJ 1001 to 5000 employees
#> 385 Falls Church, VA 51 to 200 employees
#> 386 Chicago, IL 501 to 1000 employees
#> 387 Indianapolis, IN 10000+ employees
#> 388 Washington, DC 501 to 1000 employees
#> 389 Herndon, VA 5001 to 10000 employees
#> 390 Huntsville, AL 1001 to 5000 employees
#> 391 Woburn, MA 201 to 500 employees
#> 392 Herndon, VA 501 to 1000 employees
#> 393 Merrifield, VA 10000+ employees
#> 394 Madison, WI 1001 to 5000 employees
#> 395 Philadelphia, PA 201 to 500 employees
#> 396 Winston-Salem, NC 5001 to 10000 employees
#> 397 Raleigh, NC 51 to 200 employees
#> 398 San Francisco, CA 1001 to 5000 employees
#> 399 San Francisco, CA 1001 to 5000 employees
#> 400 Columbia, MD 51 to 200 employees
#> 401 San Mateo, CA Unknown
#> 402 Boston, MA 201 to 500 employees
#> 403 Burbank, CA 1 to 50 employees
#> 404 Sunnyvale, CA 1 to 50 employees
#> 405 San Ramon, CA 1 to 50 employees
#> 406 Washington, DC 1 to 50 employees
#> 407 Springfield, VA 51 to 200 employees
#> 408 Phoenix, AZ 201 to 500 employees
#> 409 Oxnard, CA 10000+ employees
#> 410 Kansas City, MO 5001 to 10000 employees
#> 411 Jersey City, NJ 201 to 500 employees
#> 412 Manchester, NH 10000+ employees
#> 413 Cedar Rapids, IA 501 to 1000 employees
#> 414 Winters, TX 1 to 50 employees
#> 415 Ann Arbor, MI 1001 to 5000 employees
#> 416 Brooklyn, NY Unknown
#> 417 New York, NY 1 to 50 employees
#> 418 McLean, VA 51 to 200 employees
#> 419 New York, NY 501 to 1000 employees
#> 420 Saint Louis, MO 201 to 500 employees
#> 421 Concord, CA 51 to 200 employees
#> 422 Fort Belvoir, VA Unknown
#> 423 Cincinnati, OH 1 to 50 employees
#> 424 San Francisco, CA 51 to 200 employees
#> 425 Germantown, MD 51 to 200 employees
#> 426 Omaha, NE 10000+ employees
#> 427 McLean, VA 1 to 50 employees
#> 428 Open Fork, VA 1 to 50 employees
#> 429 Reston, VA 51 to 200 employees
#> 430 United States 1 to 50 employees
#> 431 San Carlos, CA 201 to 500 employees
#> 432 Boston, MA 1 to 50 employees
#> 433 Ashburn, VA 1 to 50 employees
#> 434 Lombard, IL 10000+ employees
#> 435 Hampton, VA 1001 to 5000 employees
#> 436 San Francisco, CA Unknown
#> 437 New York, NY 501 to 1000 employees
#> 438 Saint Louis, MO 201 to 500 employees
#> 439 San Francisco, CA 5001 to 10000 employees
#> 440 Edison, NJ 10000+ employees
#> 441 Cambridge, MA 10000+ employees
#> 442 Alpharetta, GA 1001 to 5000 employees
#> 443 Boulder, CO 1001 to 5000 employees
#> 444 Atlanta, GA 10000+ employees
#> 445 Falls Church, VA 51 to 200 employees
#> 446 Mountain View, CA 1 to 50 employees
#> 447 Kent, OH 10000+ employees
#> 448 Trumbull, CT 501 to 1000 employees
#> 449 San Francisco, CA 1 to 50 employees
#> 450 Pittsburgh, PA 501 to 1000 employees
#> 451 Sterling, VA -1
#> 452 New York, NY 1001 to 5000 employees
#> 453 Alpharetta, GA 501 to 1000 employees
#> 454 Philadelphia, PA 1 to 50 employees
#> 455 Carson, CA 201 to 500 employees
#> 456 Foster City, CA 51 to 200 employees
#> 457 New York, NY 1 to 50 employees
#> 458 Frederick, MD 51 to 200 employees
#> 459 Chicago, IL 501 to 1000 employees
#> 460 Santa Clara, CA 1001 to 5000 employees
#> 461 New York, NY 51 to 200 employees
#> 462 Maple Plain, MN 1001 to 5000 employees
#> 463 Colorado Springs, CO 501 to 1000 employees
#> 464 San Jose, CA 10000+ employees
#> 465 United States 1 to 50 employees
#> 466 Cincinnati, OH 1001 to 5000 employees
#> 467 Atlanta, GA 5001 to 10000 employees
#> 468 Washington, DC 1 to 50 employees
#> 469 Arlington, VA 51 to 200 employees
#> 470 Santa Clara, CA 201 to 500 employees
#> 471 Tulsa, OK 201 to 500 employees
#> 472 Chantilly, VA 201 to 500 employees
#> 473 Southfield, MI 1001 to 5000 employees
#> 474 San Francisco, CA 1001 to 5000 employees
#> 475 United States 201 to 500 employees
#> 476 Laurel, MD 5001 to 10000 employees
#> 477 Cambridge, MA 1 to 50 employees
#> 478 Annapolis Junction, MD 1 to 50 employees
#> 479 Redwood City, CA 501 to 1000 employees
#> 480 Washington, DC 1001 to 5000 employees
#> 481 San Clemente, CA 1 to 50 employees
#> 482 New York, NY 1 to 50 employees
#> 483 Falls Church, VA 51 to 200 employees
#> 484 Newton, MA 201 to 500 employees
#> 485 San Francisco, CA 10000+ employees
#> 486 Washington, DC 1 to 50 employees
#> 487 San Francisco, CA -1
#> 488 San Francisco, CA -1
#> 489 San Francisco, CA -1
#> 490 San Francisco, CA -1
#> 491 San Francisco, CA -1
#> 492 San Francisco, CA -1
#> 493 The Woodlands, TX 1 to 50 employees
#> 494 Seattle, WA 51 to 200 employees
#> 495 Chicago, IL 1 to 50 employees
#> 496 Chantilly, VA 1 to 50 employees
#> 497 Chicago, IL 51 to 200 employees
#> 498 San Francisco, CA 51 to 200 employees
#> 499 Baltimore, MD 1 to 50 employees
#> 500 Pleasanton, CA 10000+ employees
#> 501 Wilmington, DE 10000+ employees
#> 502 United States 201 to 500 employees
#> 503 New York, NY 1 to 50 employees
#> 504 Herndon, VA 5001 to 10000 employees
#> 505 Seattle, WA Unknown
#> 506 Herndon, VA 5001 to 10000 employees
#> 507 Fort Sam Houston, TX 501 to 1000 employees
#> 508 Oklahoma City, OK 1001 to 5000 employees
#> 509 Lexington Park, MD 51 to 200 employees
#> 510 Boston, MA 201 to 500 employees
#> 511 Cambridge, MA -1
#> 512 Washington, DC 51 to 200 employees
#> 513 Washington, DC 51 to 200 employees
#> 514 Raleigh, NC 501 to 1000 employees
#> 515 Dayton, OH 1001 to 5000 employees
#> 516 New York, NY Unknown
#> 517 Washington, DC 1 to 50 employees
#> 518 Seattle, WA 201 to 500 employees
#> 519 New York, NY 1001 to 5000 employees
#> 520 Washington, DC 51 to 200 employees
#> 521 Longmont, CO 1 to 50 employees
#> 522 Washington, DC 51 to 200 employees
#> 523 Washington, DC 1 to 50 employees
#> 524 Sunnyvale, CA 1 to 50 employees
#> 525 San Francisco, CA 501 to 1000 employees
#> 526 Patuxent, Anne Arundel, MD 51 to 200 employees
#> 527 Santa Clara, CA 10000+ employees
#> 528 Fairfax, VA 10000+ employees
#> 529 Denver, CO 201 to 500 employees
#> 530 Fort Belvoir, VA 501 to 1000 employees
#> 531 Chicago, IL 201 to 500 employees
#> 532 Thousand Oaks, CA 1001 to 5000 employees
#> 533 McLean, VA 5001 to 10000 employees
#> 534 San Antonio, TX 1001 to 5000 employees
#> 535 Silver Spring, MD 501 to 1000 employees
#> 536 Boston, MA 5001 to 10000 employees
#> 537 Washington, DC 501 to 1000 employees
#> 538 New York, NY 51 to 200 employees
#> 539 McLean, VA 201 to 500 employees
#> 540 Hartford, CT 10000+ employees
#> 541 Emeryville, CA 201 to 500 employees
#> 542 Austin, TX 51 to 200 employees
#> 543 San Francisco, CA 501 to 1000 employees
#> 544 San Francisco, CA 1001 to 5000 employees
#> 545 Portland, OR 1001 to 5000 employees
#> 546 Cambridge, MA 5001 to 10000 employees
#> 547 San Francisco, CA -1
#> 548 Redwood City, CA 501 to 1000 employees
#> 549 San Diego, CA 501 to 1000 employees
#> 550 New York, NY 51 to 200 employees
#> 551 Chantilly, VA 501 to 1000 employees
#> 552 Falls Church, VA 51 to 200 employees
#> 553 Chantilly, VA 1001 to 5000 employees
#> 554 Chicago, IL 1001 to 5000 employees
#> 555 San Francisco, CA 5001 to 10000 employees
#> 556 Chicago, IL 501 to 1000 employees
#> 557 Raleigh, NC 1001 to 5000 employees
#> 558 Orlando, FL 51 to 200 employees
#> 559 New York, NY 1 to 50 employees
#> 560 Philadelphia, PA Unknown
#> 561 McLean, VA 5001 to 10000 employees
#> 562 United States 10000+ employees
#> 563 Santa Cruz, CA 51 to 200 employees
#> 564 Simi Valley, CA 501 to 1000 employees
#> 565 Herndon, VA 5001 to 10000 employees
#> 566 Huntsville, AL 1001 to 5000 employees
#> 567 Woburn, MA 201 to 500 employees
#> 568 Herndon, VA 501 to 1000 employees
#> 569 Austin, TX 501 to 1000 employees
#> 570 Merrifield, VA 10000+ employees
#> 571 New Bedford, MA 51 to 200 employees
#> 572 South San Francisco, CA 51 to 200 employees
#> 573 Merrifield, VA 10000+ employees
#> 574 Winston-Salem, NC 5001 to 10000 employees
#> 575 Bedford, MA 5001 to 10000 employees
#> 576 Raleigh, NC 51 to 200 employees
#> 577 New Bedford, MA 51 to 200 employees
#> 578 Chicago, IL 51 to 200 employees
#> 579 Rancho Cucamonga, CA 1001 to 5000 employees
#> 580 New York, NY 51 to 200 employees
#> 581 Livermore, CA 10000+ employees
#> 582 McLean, VA 5001 to 10000 employees
#> 583 Gaithersburg, MD 10000+ employees
#> 584 San Francisco, CA 5001 to 10000 employees
#> 585 Southfield, MI 1001 to 5000 employees
#> 586 Collegeville, PA 10000+ employees
#> 587 Washington, DC Unknown
#> 588 Chicago, IL 201 to 500 employees
#> 589 Minneapolis, MN 51 to 200 employees
#> 590 Gahanna, OH 1 to 50 employees
#> 591 Nashville, TN 10000+ employees
#> 592 United States 201 to 500 employees
#> 593 Laurel, MD 5001 to 10000 employees
#> 594 Annapolis Junction, MD 1 to 50 employees
#> 595 San Clemente, CA 1 to 50 employees
#> 596 New York, NY 1 to 50 employees
#> 597 Washington, DC 51 to 200 employees
#> 598 Atlanta, GA 10000+ employees
#> 599 Plano, TX 1 to 50 employees
#> 600 Fairfax, VA 51 to 200 employees
#> 601 Washington, DC 1 to 50 employees
#> 602 Alexandria, VA 5001 to 10000 employees
#> 603 Omaha, NE -1
#> 604 Wellesley, MA 10000+ employees
#> 605 Washington, VA Unknown
#> 606 Fort Worth, TX 1001 to 5000 employees
#> 607 Atlanta, GA 10000+ employees
#> 608 Alexandria, VA 51 to 200 employees
#> 609 Bellevue, WA 51 to 200 employees
#> 610 United States 5001 to 10000 employees
#> 611 Thousand Oaks, CA 1 to 50 employees
#> 612 Orange, CA 501 to 1000 employees
#> 613 Baltimore, MD 1 to 50 employees
#> 614 Herndon, VA 51 to 200 employees
#> 615 Alexandria, VA 51 to 200 employees
#> 616 Bridgeport, WV 1001 to 5000 employees
#> 617 The Woodlands, TX 1 to 50 employees
#> 618 Oakville, CA 1001 to 5000 employees
#> 619 Chicago, IL 51 to 200 employees
#> 620 Arlington, VA 201 to 500 employees
#> 621 New York, NY 10000+ employees
#> 622 Seattle, WA 51 to 200 employees
#> 623 Naperville, IL 5001 to 10000 employees
#> 624 San Francisco, CA 51 to 200 employees
#> 625 Baltimore, MD 1 to 50 employees
#> 626 Chantilly, VA 1 to 50 employees
#> 627 Herndon, VA 5001 to 10000 employees
#> 628 Bellevue, WA 51 to 200 employees
#> 629 New York, NY 1 to 50 employees
#> 630 Chicago, IL 51 to 200 employees
#> 631 Kansas City, MO 201 to 500 employees
#> 632 Houston, TX 1001 to 5000 employees
#> 633 Chantilly, VA 10000+ employees
#> 634 Santa Clara, CA 10000+ employees
#> 635 Herndon, VA 5001 to 10000 employees
#> 636 Oklahoma City, OK 1001 to 5000 employees
#> 637 Thousand Oaks, CA 51 to 200 employees
#> 638 Boston, MA 201 to 500 employees
#> 639 Cambridge, MA -1
#> 640 Gaithersburg, MD 10000+ employees
#> 641 Denver, CO 51 to 200 employees
#> 642 Cupertino, CA 5001 to 10000 employees
#> 643 Lexington Park, MD 51 to 200 employees
#> 644 Washington, DC 51 to 200 employees
#> 645 Fort Belvoir, VA -1
#> 646 Naperville, IL -1
#> 647 Washington, DC 51 to 200 employees
#> 648 Adelphi, MD 10000+ employees
#> 649 Redmond, WA -1
#> 650 West Chester, PA 10000+ employees
#> 651 Edison, NJ 51 to 200 employees
#> 652 Durham, NC 501 to 1000 employees
#> 653 Quantico, VA 1 to 50 employees
#> 654 Vienna, VA 201 to 500 employees
#> 655 Boston, MA 1001 to 5000 employees
#> 656 Fort Lee, NJ 1001 to 5000 employees
#> 657 San Francisco, CA -1
#> 658 Irwindale, CA -1
#> 659 San Francisco, CA 1 to 50 employees
#> 660 New York, NY 1001 to 5000 employees
#> Industry Sector
#> 1 Insurance Carriers Insurance
#> 2 Research & Development Business Services
#> 3 Consulting Business Services
#> 4 Electrical & Electronic Manufacturing Manufacturing
#> 5 Advertising & Marketing Business Services
#> 6 Computer Hardware & Software Information Technology
#> 7 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 8 Consumer Electronics & Appliances Stores Retail
#> 9 Computer Hardware & Software Information Technology
#> 10 Enterprise Software & Network Solutions Information Technology
#> 11 Enterprise Software & Network Solutions Information Technology
#> 12 IT Services Information Technology
#> 13 Energy Oil, Gas, Energy & Utilities
#> 14 Chemical Manufacturing Manufacturing
#> 15 Consulting Business Services
#> 16 Federal Agencies Government
#> 17 Chemical Manufacturing Manufacturing
#> 18 Enterprise Software & Network Solutions Information Technology
#> 19 Health Care Services & Hospitals Health Care
#> 20 Computer Hardware & Software Information Technology
#> 21 Internet Information Technology
#> 22 Consulting Business Services
#> 23 Insurance Carriers Insurance
#> 24 Advertising & Marketing Business Services
#> 25 Computer Hardware & Software Information Technology
#> 26 Investment Banking & Asset Management Finance
#> 27 Aerospace & Defense Aerospace & Defense
#> 28 Federal Agencies Government
#> 29 Enterprise Software & Network Solutions Information Technology
#> 30 Consulting Business Services
#> 31 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 32 IT Services Information Technology
#> 33 Insurance Carriers Insurance
#> 34 Enterprise Software & Network Solutions Information Technology
#> 35 Utilities Oil, Gas, Energy & Utilities
#> 36 Enterprise Software & Network Solutions Information Technology
#> 37 Health Care Services & Hospitals Health Care
#> 38 Enterprise Software & Network Solutions Information Technology
#> 39 Internet Information Technology
#> 40 Investment Banking & Asset Management Finance
#> 41 Insurance Carriers Insurance
#> 42 -1 -1
#> 43 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 44 Enterprise Software & Network Solutions Information Technology
#> 45 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 46 Computer Hardware & Software Information Technology
#> 47 Investment Banking & Asset Management Finance
#> 48 Federal Agencies Government
#> 49 Enterprise Software & Network Solutions Information Technology
#> 50 Advertising & Marketing Business Services
#> 51 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 52 Enterprise Software & Network Solutions Information Technology
#> 53 Enterprise Software & Network Solutions Information Technology
#> 54 Health Care Services & Hospitals Health Care
#> 55 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 56 Internet Information Technology
#> 57 Express Delivery Services Transportation & Logistics
#> 58 Staffing & Outsourcing Business Services
#> 59 Insurance Agencies & Brokerages Insurance
#> 60 Aerospace & Defense Aerospace & Defense
#> 61 Consumer Electronics & Appliances Stores Retail
#> 62 Computer Hardware & Software Information Technology
#> 63 Consumer Products Manufacturing Manufacturing
#> 64 Investment Banking & Asset Management Finance
#> 65 Internet Information Technology
#> 66 Enterprise Software & Network Solutions Information Technology
#> 67 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 68 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 69 Advertising & Marketing Business Services
#> 70 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 71 Computer Hardware & Software Information Technology
#> 72 Industrial Manufacturing Manufacturing
#> 73 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 74 Insurance Agencies & Brokerages Insurance
#> 75 Insurance Carriers Insurance
#> 76 Internet Information Technology
#> 77 Insurance Carriers Insurance
#> 78 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 79 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 80 IT Services Information Technology
#> 81 Consulting Business Services
#> 82 Staffing & Outsourcing Business Services
#> 83 Computer Hardware & Software Information Technology
#> 84 Internet Information Technology
#> 85 Health Care Services & Hospitals Health Care
#> 86 Computer Hardware & Software Information Technology
#> 87 Computer Hardware & Software Information Technology
#> 88 Food & Beverage Manufacturing Manufacturing
#> 89 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 90 IT Services Information Technology
#> 91 Research & Development Business Services
#> 92 Consulting Business Services
#> 93 Computer Hardware & Software Information Technology
#> 94 Enterprise Software & Network Solutions Information Technology
#> 95 Food & Beverage Manufacturing Manufacturing
#> 96 Advertising & Marketing Business Services
#> 97 Banks & Credit Unions Finance
#> 98 Aerospace & Defense Aerospace & Defense
#> 99 Internet Information Technology
#> 100 Computer Hardware & Software Information Technology
#> 101 Computer Hardware & Software Information Technology
#> 102 Aerospace & Defense Aerospace & Defense
#> 103 Health Care Services & Hospitals Health Care
#> 104 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 105 Computer Hardware & Software Information Technology
#> 106 Enterprise Software & Network Solutions Information Technology
#> 107 Investment Banking & Asset Management Finance
#> 108 Video Games Media
#> 109 Aerospace & Defense Aerospace & Defense
#> 110 Computer Hardware & Software Information Technology
#> 111 Consulting Business Services
#> 112 Health Care Services & Hospitals Health Care
#> 113 Computer Hardware & Software Information Technology
#> 114 Investment Banking & Asset Management Finance
#> 115 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 116 Health Care Services & Hospitals Health Care
#> 117 Consulting Business Services
#> 118 Internet Information Technology
#> 119 Enterprise Software & Network Solutions Information Technology
#> 120 Advertising & Marketing Business Services
#> 121 Shipping Transportation & Logistics
#> 122 Telecommunications Services Telecommunications
#> 123 IT Services Information Technology
#> 124 Enterprise Software & Network Solutions Information Technology
#> 125 Advertising & Marketing Business Services
#> 126 Telecommunications Services Telecommunications
#> 127 Enterprise Software & Network Solutions Information Technology
#> 128 Lending Finance
#> 129 Advertising & Marketing Business Services
#> 130 Computer Hardware & Software Information Technology
#> 131 Lending Finance
#> 132 Lending Finance
#> 133 Staffing & Outsourcing Business Services
#> 134 Video Games Media
#> 135 Utilities Oil, Gas, Energy & Utilities
#> 136 Enterprise Software & Network Solutions Information Technology
#> 137 Electrical & Electronic Manufacturing Manufacturing
#> 138 Federal Agencies Government
#> 139 Aerospace & Defense Aerospace & Defense
#> 140 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 141 Insurance Carriers Insurance
#> 142 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 143 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 144 Advertising & Marketing Business Services
#> 145 Cable, Internet & Telephone Providers Telecommunications
#> 146 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 147 Computer Hardware & Software Information Technology
#> 148 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 149 -1 -1
#> 150 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 151 Enterprise Software & Network Solutions Information Technology
#> 152 Federal Agencies Government
#> 153 -1 -1
#> 154 Aerospace & Defense Aerospace & Defense
#> 155 Computer Hardware & Software Information Technology
#> 156 Insurance Carriers Insurance
#> 157 Aerospace & Defense Aerospace & Defense
#> 158 Telecommunications Services Telecommunications
#> 159 IT Services Information Technology
#> 160 Real Estate Real Estate
#> 161 Computer Hardware & Software Information Technology
#> 162 Research & Development Business Services
#> 163 -1 -1
#> 164 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 165 Staffing & Outsourcing Business Services
#> 166 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 167 Enterprise Software & Network Solutions Information Technology
#> 168 Enterprise Software & Network Solutions Information Technology
#> 169 Banks & Credit Unions Finance
#> 170 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 171 Venture Capital & Private Equity Finance
#> 172 Energy Oil, Gas, Energy & Utilities
#> 173 Chemical Manufacturing Manufacturing
#> 174 Federal Agencies Government
#> 175 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 176 Advertising & Marketing Business Services
#> 177 Insurance Carriers Insurance
#> 178 Aerospace & Defense Aerospace & Defense
#> 179 Internet Information Technology
#> 180 Insurance Carriers Insurance
#> 181 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 182 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 183 IT Services Information Technology
#> 184 Advertising & Marketing Business Services
#> 185 Computer Hardware & Software Information Technology
#> 186 IT Services Information Technology
#> 187 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 188 -1 -1
#> 189 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 190 -1 -1
#> 191 Health Care Services & Hospitals Health Care
#> 192 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 193 Lending Finance
#> 194 Advertising & Marketing Business Services
#> 195 Consulting Business Services
#> 196 Aerospace & Defense Aerospace & Defense
#> 197 Computer Hardware & Software Information Technology
#> 198 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 199 Research & Development Business Services
#> 200 Enterprise Software & Network Solutions Information Technology
#> 201 Miscellaneous Manufacturing Manufacturing
#> 202 Oil & Gas Services Oil, Gas, Energy & Utilities
#> 203 IT Services Information Technology
#> 204 Staffing & Outsourcing Business Services
#> 205 Aerospace & Defense Aerospace & Defense
#> 206 IT Services Information Technology
#> 207 Computer Hardware & Software Information Technology
#> 208 Research & Development Business Services
#> 209 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 210 IT Services Information Technology
#> 211 Health Care Services & Hospitals Health Care
#> 212 Insurance Carriers Insurance
#> 213 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 214 Lending Finance
#> 215 Internet Information Technology
#> 216 Investment Banking & Asset Management Finance
#> 217 Venture Capital & Private Equity Finance
#> 218 Real Estate Real Estate
#> 219 IT Services Information Technology
#> 220 IT Services Information Technology
#> 221 Transportation Equipment Manufacturing Manufacturing
#> 222 Federal Agencies Government
#> 223 Investment Banking & Asset Management Finance
#> 224 IT Services Information Technology
#> 225 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 226 Aerospace & Defense Aerospace & Defense
#> 227 Insurance Carriers Insurance
#> 228 Insurance Carriers Insurance
#> 229 Telecommunications Manufacturing Telecommunications
#> 230 Computer Hardware & Software Information Technology
#> 231 Internet Information Technology
#> 232 Consulting Business Services
#> 233 Computer Hardware & Software Information Technology
#> 234 Insurance Carriers Insurance
#> 235 Computer Hardware & Software Information Technology
#> 236 Enterprise Software & Network Solutions Information Technology
#> 237 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 238 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 239 Transportation Management Transportation & Logistics
#> 240 Insurance Carriers Insurance
#> 241 -1 -1
#> 242 Aerospace & Defense Aerospace & Defense
#> 243 Insurance Carriers Insurance
#> 244 -1 -1
#> 245 Industrial Manufacturing Manufacturing
#> 246 Consulting Business Services
#> 247 News Outlet Media
#> 248 IT Services Information Technology
#> 249 Research & Development Business Services
#> 250 IT Services Information Technology
#> 251 -1 -1
#> 252 Advertising & Marketing Business Services
#> 253 Computer Hardware & Software Information Technology
#> 254 Advertising & Marketing Business Services
#> 255 Aerospace & Defense Aerospace & Defense
#> 256 Utilities Oil, Gas, Energy & Utilities
#> 257 Energy Oil, Gas, Energy & Utilities
#> 258 Consulting Business Services
#> 259 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 260 IT Services Information Technology
#> 261 Internet Information Technology
#> 262 Enterprise Software & Network Solutions Information Technology
#> 263 IT Services Information Technology
#> 264 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 265 -1 -1
#> 266 IT Services Information Technology
#> 267 -1 -1
#> 268 Federal Agencies Government
#> 269 Banks & Credit Unions Finance
#> 270 Architectural & Engineering Services Business Services
#> 271 Computer Hardware & Software Information Technology
#> 272 Consulting Business Services
#> 273 Real Estate Real Estate
#> 274 Staffing & Outsourcing Business Services
#> 275 -1 -1
#> 276 Internet Information Technology
#> 277 IT Services Information Technology
#> 278 -1 -1
#> 279 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 280 Transportation Equipment Manufacturing Manufacturing
#> 281 -1 -1
#> 282 Architectural & Engineering Services Business Services
#> 283 -1 -1
#> 284 Staffing & Outsourcing Business Services
#> 285 Aerospace & Defense Aerospace & Defense
#> 286 Video Games Media
#> 287 Food & Beverage Stores Retail
#> 288 Health Care Services & Hospitals Health Care
#> 289 Other Retail Stores Retail
#> 290 IT Services Information Technology
#> 291 Hotels, Motels, & Resorts Travel & Tourism
#> 292 Electrical & Electronic Manufacturing Manufacturing
#> 293 State & Regional Agencies Government
#> 294 Computer Hardware & Software Information Technology
#> 295 Other Retail Stores Retail
#> 296 Computer Hardware & Software Information Technology
#> 297 Federal Agencies Government
#> 298 IT Services Information Technology
#> 299 IT Services Information Technology
#> 300 -1 -1
#> 301 Aerospace & Defense Aerospace & Defense
#> 302 IT Services Information Technology
#> 303 Health Care Services & Hospitals Health Care
#> 304 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 305 Financial Transaction Processing Finance
#> 306 Insurance Carriers Insurance
#> 307 Internet Information Technology
#> 308 -1 -1
#> 309 Staffing & Outsourcing Business Services
#> 310 Lending Finance
#> 311 Consulting Business Services
#> 312 Consulting Business Services
#> 313 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 314 -1 -1
#> 315 Federal Agencies Government
#> 316 Investment Banking & Asset Management Finance
#> 317 Computer Hardware & Software Information Technology
#> 318 IT Services Information Technology
#> 319 Staffing & Outsourcing Business Services
#> 320 Computer Hardware & Software Information Technology
#> 321 IT Services Information Technology
#> 322 Aerospace & Defense Aerospace & Defense
#> 323 Insurance Carriers Insurance
#> 324 Investment Banking & Asset Management Finance
#> 325 Computer Hardware & Software Information Technology
#> 326 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 327 -1 -1
#> 328 Timber Operations Agriculture & Forestry
#> 329 Aerospace & Defense Aerospace & Defense
#> 330 -1 -1
#> 331 Colleges & Universities Education
#> 332 Consumer Products Manufacturing Manufacturing
#> 333 IT Services Information Technology
#> 334 Research & Development Business Services
#> 335 Enterprise Software & Network Solutions Information Technology
#> 336 Enterprise Software & Network Solutions Information Technology
#> 337 Miscellaneous Manufacturing Manufacturing
#> 338 Insurance Agencies & Brokerages Insurance
#> 339 Computer Hardware & Software Information Technology
#> 340 -1 -1
#> 341 Staffing & Outsourcing Business Services
#> 342 Computer Hardware & Software Information Technology
#> 343 -1 -1
#> 344 Aerospace & Defense Aerospace & Defense
#> 345 Enterprise Software & Network Solutions Information Technology
#> 346 Consulting Business Services
#> 347 IT Services Information Technology
#> 348 Consulting Business Services
#> 349 -1 -1
#> 350 -1 -1
#> 351 -1 -1
#> 352 -1 -1
#> 353 -1 -1
#> 354 -1 -1
#> 355 Aerospace & Defense Aerospace & Defense
#> 356 Enterprise Software & Network Solutions Information Technology
#> 357 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 358 Aerospace & Defense Aerospace & Defense
#> 359 Research & Development Business Services
#> 360 Consulting Business Services
#> 361 Computer Hardware & Software Information Technology
#> 362 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 363 -1 -1
#> 364 Aerospace & Defense Aerospace & Defense
#> 365 Staffing & Outsourcing Business Services
#> 366 Research & Development Business Services
#> 367 Staffing & Outsourcing Business Services
#> 368 Staffing & Outsourcing Business Services
#> 369 Travel Agencies Travel & Tourism
#> 370 IT Services Information Technology
#> 371 Energy Oil, Gas, Energy & Utilities
#> 372 Health Care Services & Hospitals Health Care
#> 373 Insurance Carriers Insurance
#> 374 Computer Hardware & Software Information Technology
#> 375 Insurance Carriers Insurance
#> 376 Internet Information Technology
#> 377 Computer Hardware & Software Information Technology
#> 378 Advertising & Marketing Business Services
#> 379 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 380 -1 -1
#> 381 -1 -1
#> 382 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 383 Computer Hardware & Software Information Technology
#> 384 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 385 Federal Agencies Government
#> 386 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 387 Insurance Carriers Insurance
#> 388 IT Services Information Technology
#> 389 Aerospace & Defense Aerospace & Defense
#> 390 Aerospace & Defense Aerospace & Defense
#> 391 Aerospace & Defense Aerospace & Defense
#> 392 Enterprise Software & Network Solutions Information Technology
#> 393 Banks & Credit Unions Finance
#> 394 Health Care Services & Hospitals Health Care
#> 395 Health Care Services & Hospitals Health Care
#> 396 Consumer Products Manufacturing Manufacturing
#> 397 Enterprise Software & Network Solutions Information Technology
#> 398 Internet Information Technology
#> 399 Express Delivery Services Transportation & Logistics
#> 400 Enterprise Software & Network Solutions Information Technology
#> 401 -1 -1
#> 402 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 403 IT Services Information Technology
#> 404 Enterprise Software & Network Solutions Information Technology
#> 405 Advertising & Marketing Business Services
#> 406 Consulting Business Services
#> 407 Aerospace & Defense Aerospace & Defense
#> 408 Staffing & Outsourcing Business Services
#> 409 Consulting Business Services
#> 410 Architectural & Engineering Services Business Services
#> 411 IT Services Information Technology
#> 412 Staffing & Outsourcing Business Services
#> 413 Lending Finance
#> 414 Accounting Accounting & Legal
#> 415 Internet Information Technology
#> 416 -1 -1
#> 417 Consulting Business Services
#> 418 Consulting Business Services
#> 419 Internet Information Technology
#> 420 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 421 Consulting Business Services
#> 422 -1 -1
#> 423 -1 -1
#> 424 Computer Hardware & Software Information Technology
#> 425 IT Services Information Technology
#> 426 Logistics & Supply Chain Transportation & Logistics
#> 427 -1 -1
#> 428 Staffing & Outsourcing Business Services
#> 429 IT Services Information Technology
#> 430 -1 -1
#> 431 Farm Support Services Agriculture & Forestry
#> 432 Health Care Services & Hospitals Health Care
#> 433 -1 -1
#> 434 IT Services Information Technology
#> 435 Research & Development Business Services
#> 436 Telecommunications Services Telecommunications
#> 437 Investment Banking & Asset Management Finance
#> 438 Staffing & Outsourcing Business Services
#> 439 Computer Hardware & Software Information Technology
#> 440 Health Care Services & Hospitals Health Care
#> 441 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 442 Staffing & Outsourcing Business Services
#> 443 Internet Information Technology
#> 444 IT Services Information Technology
#> 445 Consulting Business Services
#> 446 IT Services Information Technology
#> 447 Timber Operations Agriculture & Forestry
#> 448 Insurance Carriers Insurance
#> 449 -1 -1
#> 450 Colleges & Universities Education
#> 451 -1 -1
#> 452 Advertising & Marketing Business Services
#> 453 Consulting Business Services
#> 454 Staffing & Outsourcing Business Services
#> 455 Consumer Products Manufacturing Manufacturing
#> 456 IT Services Information Technology
#> 457 IT Services Information Technology
#> 458 Staffing & Outsourcing Business Services
#> 459 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 460 Enterprise Software & Network Solutions Information Technology
#> 461 Research & Development Business Services
#> 462 Miscellaneous Manufacturing Manufacturing
#> 463 Aerospace & Defense Aerospace & Defense
#> 464 Computer Hardware & Software Information Technology
#> 465 Investment Banking & Asset Management Finance
#> 466 Insurance Agencies & Brokerages Insurance
#> 467 Financial Transaction Processing Finance
#> 468 -1 -1
#> 469 Social Assistance Non-Profit
#> 470 -1 -1
#> 471 Oil & Gas Services Oil, Gas, Energy & Utilities
#> 472 Computer Hardware & Software Information Technology
#> 473 Construction Construction, Repair & Maintenance
#> 474 Staffing & Outsourcing Business Services
#> 475 IT Services Information Technology
#> 476 Aerospace & Defense Aerospace & Defense
#> 477 Enterprise Software & Network Solutions Information Technology
#> 478 IT Services Information Technology
#> 479 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 480 Internet Information Technology
#> 481 Health Care Services & Hospitals Health Care
#> 482 Accounting Accounting & Legal
#> 483 Consulting Business Services
#> 484 Staffing & Outsourcing Business Services
#> 485 Department, Clothing, & Shoe Stores Retail
#> 486 -1 -1
#> 487 -1 -1
#> 488 -1 -1
#> 489 -1 -1
#> 490 -1 -1
#> 491 -1 -1
#> 492 -1 -1
#> 493 Staffing & Outsourcing Business Services
#> 494 Computer Hardware & Software Information Technology
#> 495 Staffing & Outsourcing Business Services
#> 496 -1 -1
#> 497 Staffing & Outsourcing Business Services
#> 498 Consulting Business Services
#> 499 IT Services Information Technology
#> 500 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 501 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 502 Staffing & Outsourcing Business Services
#> 503 Staffing & Outsourcing Business Services
#> 504 Aerospace & Defense Aerospace & Defense
#> 505 Publishing Media
#> 506 Aerospace & Defense Aerospace & Defense
#> 507 -1 -1
#> 508 Research & Development Business Services
#> 509 Aerospace & Defense Aerospace & Defense
#> 510 Computer Hardware & Software Information Technology
#> 511 -1 -1
#> 512 Health, Beauty, & Fitness Consumer Services
#> 513 IT Services Information Technology
#> 514 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 515 Research & Development Business Services
#> 516 -1 -1
#> 517 Staffing & Outsourcing Business Services
#> 518 Research & Development Business Services
#> 519 Wholesale Business Services
#> 520 Consulting Business Services
#> 521 Staffing & Outsourcing Business Services
#> 522 IT Services Information Technology
#> 523 -1 -1
#> 524 Staffing & Outsourcing Business Services
#> 525 Internet Information Technology
#> 526 IT Services Information Technology
#> 527 Computer Hardware & Software Information Technology
#> 528 Staffing & Outsourcing Business Services
#> 529 Travel Agencies Travel & Tourism
#> 530 IT Services Information Technology
#> 531 Lending Finance
#> 532 IT Services Information Technology
#> 533 Energy Oil, Gas, Energy & Utilities
#> 534 Research & Development Business Services
#> 535 IT Services Information Technology
#> 536 Insurance Carriers Insurance
#> 537 IT Services Information Technology
#> 538 IT Services Information Technology
#> 539 Enterprise Software & Network Solutions Information Technology
#> 540 Insurance Carriers Insurance
#> 541 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 542 Computer Hardware & Software Information Technology
#> 543 Internet Information Technology
#> 544 Internet Information Technology
#> 545 Computer Hardware & Software Information Technology
#> 546 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 547 -1 -1
#> 548 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 549 Insurance Carriers Insurance
#> 550 Consulting Business Services
#> 551 Consulting Business Services
#> 552 Federal Agencies Government
#> 553 Aerospace & Defense Aerospace & Defense
#> 554 Staffing & Outsourcing Business Services
#> 555 Computer Hardware & Software Information Technology
#> 556 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 557 Industrial Manufacturing Manufacturing
#> 558 Investment Banking & Asset Management Finance
#> 559 IT Services Information Technology
#> 560 -1 -1
#> 561 Federal Agencies Government
#> 562 Food & Beverage Manufacturing Manufacturing
#> 563 Computer Hardware & Software Information Technology
#> 564 Aerospace & Defense Aerospace & Defense
#> 565 Aerospace & Defense Aerospace & Defense
#> 566 Aerospace & Defense Aerospace & Defense
#> 567 Aerospace & Defense Aerospace & Defense
#> 568 Enterprise Software & Network Solutions Information Technology
#> 569 Colleges & Universities Education
#> 570 Banks & Credit Unions Finance
#> 571 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 572 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 573 Banks & Credit Unions Finance
#> 574 Consumer Products Manufacturing Manufacturing
#> 575 Federal Agencies Government
#> 576 Enterprise Software & Network Solutions Information Technology
#> 577 -1 -1
#> 578 Advertising & Marketing Business Services
#> 579 Health Care Services & Hospitals Health Care
#> 580 Advertising & Marketing Business Services
#> 581 Federal Agencies Government
#> 582 Aerospace & Defense Aerospace & Defense
#> 583 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 584 Computer Hardware & Software Information Technology
#> 585 Construction Construction, Repair & Maintenance
#> 586 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 587 Federal Agencies Government
#> 588 Consulting Business Services
#> 589 Advertising & Marketing Business Services
#> 590 -1 -1
#> 591 Health Care Services & Hospitals Health Care
#> 592 IT Services Information Technology
#> 593 Aerospace & Defense Aerospace & Defense
#> 594 IT Services Information Technology
#> 595 Health Care Services & Hospitals Health Care
#> 596 Accounting Accounting & Legal
#> 597 Enterprise Software & Network Solutions Information Technology
#> 598 IT Services Information Technology
#> 599 Consulting Business Services
#> 600 Aerospace & Defense Aerospace & Defense
#> 601 -1 -1
#> 602 Research & Development Business Services
#> 603 -1 -1
#> 604 Insurance Carriers Insurance
#> 605 -1 -1
#> 606 Rail Transportation & Logistics
#> 607 IT Services Information Technology
#> 608 Aerospace & Defense Aerospace & Defense
#> 609 Consulting Business Services
#> 610 Aerospace & Defense Aerospace & Defense
#> 611 -1 -1
#> 612 Health Care Services & Hospitals Health Care
#> 613 Consulting Business Services
#> 614 Computer Hardware & Software Information Technology
#> 615 Enterprise Software & Network Solutions Information Technology
#> 616 Consulting Business Services
#> 617 Staffing & Outsourcing Business Services
#> 618 Computer Hardware & Software Information Technology
#> 619 Staffing & Outsourcing Business Services
#> 620 IT Services Information Technology
#> 621 Food & Beverage Manufacturing Manufacturing
#> 622 Consulting Business Services
#> 623 Wholesale Business Services
#> 624 Consulting Business Services
#> 625 IT Services Information Technology
#> 626 -1 -1
#> 627 Aerospace & Defense Aerospace & Defense
#> 628 Enterprise Software & Network Solutions Information Technology
#> 629 Staffing & Outsourcing Business Services
#> 630 IT Services Information Technology
#> 631 Insurance Carriers Insurance
#> 632 Insurance Carriers Insurance
#> 633 Aerospace & Defense Aerospace & Defense
#> 634 Computer Hardware & Software Information Technology
#> 635 Aerospace & Defense Aerospace & Defense
#> 636 Research & Development Business Services
#> 637 Consulting Business Services
#> 638 Computer Hardware & Software Information Technology
#> 639 -1 -1
#> 640 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 641 Advertising & Marketing Business Services
#> 642 Telecommunications Services Telecommunications
#> 643 Aerospace & Defense Aerospace & Defense
#> 644 Health, Beauty, & Fitness Consumer Services
#> 645 -1 -1
#> 646 -1 -1
#> 647 IT Services Information Technology
#> 648 Aerospace & Defense Aerospace & Defense
#> 649 -1 -1
#> 650 Other Retail Stores Retail
#> 651 -1 -1
#> 652 Staffing & Outsourcing Business Services
#> 653 -1 -1
#> 654 IT Services Information Technology
#> 655 Biotech & Pharmaceuticals Biotech & Pharmaceuticals
#> 656 Advertising & Marketing Business Services
#> 657 -1 -1
#> 658 -1 -1
#> 659 Advertising & Marketing Business Services
#> 660 Wholesale Business Services
#> avg_salary company_age
#> 1 154 27
#> 2 154 52
#> 3 154 39
#> 4 154 20
#> 5 154 22
#> 6 154 10
#> 7 154 24
#> 8 154 30
#> 9 154 37
#> 10 154 6
#> 11 154 8
#> 12 154 4
#> 13 154 55
#> 14 154 47
#> 15 154 34
#> 16 154 23
#> 17 154 75
#> 18 154 32
#> 19 154 3
#> 20 154 5
#> 21 154 9
#> 22 154 53
#> 23 154 160
#> 24 154 28
#> 25 154 17
#> 26 154 24
#> 27 154 69
#> 28 154 15
#> 29 154 5
#> 30 103 39
#> 31 103 24
#> 32 103 1
#> 33 103 95
#> 34 103 12
#> 35 103 21
#> 36 103 42
#> 37 103 20
#> 38 103 20
#> 39 103 54
#> 40 103 47
#> 41 103 108
#> 42 103 62
#> 43 103 1
#> 44 103 7
#> 45 103 171
#> 46 103 8
#> 47 103 20
#> 48 103 4
#> 49 103 21
#> 50 103 6
#> 51 103 239
#> 52 103 8
#> 53 103 21
#> 54 103 94
#> 55 103 8
#> 56 103 14
#> 57 103 9
#> 58 103 26
#> 59 103 157
#> 60 103 10
#> 61 105 30
#> 62 105 10
#> 63 105 34
#> 64 105 25
#> 65 105 8
#> 66 105 14
#> 67 105 5
#> 68 105 -1
#> 69 105 14
#> 70 105 5
#> 71 105 38
#> 72 105 95
#> 73 105 46
#> 74 105 19
#> 75 105 27
#> 76 105 1
#> 77 105 35
#> 78 105 17
#> 79 105 107
#> 80 105 19
#> 81 105 24
#> 82 105 4
#> 83 105 21
#> 84 105 10
#> 85 105 49
#> 86 105 17
#> 87 105 8
#> 88 105 109
#> 89 105 107
#> 90 105 21
#> 91 105 52
#> 92 115 34
#> 93 115 37
#> 94 115 5
#> 95 115 109
#> 96 115 11
#> 97 115 61
#> 98 115 13
#> 99 115 8
#> 100 115 38
#> 101 115 37
#> 102 115 47
#> 103 115 39
#> 104 115 9
#> 105 115 81
#> 106 115 7
#> 107 115 53
#> 108 115 7
#> 109 115 -1
#> 110 115 4
#> 111 115 24
#> 112 115 4
#> 113 115 18
#> 114 115 53
#> 115 115 1
#> 116 115 20
#> 117 115 14
#> 118 115 5
#> 119 115 8
#> 120 115 14
#> 121 115 14
#> 122 115 24
#> 123 99 4
#> 124 99 6
#> 125 99 14
#> 126 99 24
#> 127 99 4
#> 128 99 9
#> 129 99 17
#> 130 99 8
#> 131 99 9
#> 132 99 8
#> 133 99 26
#> 134 99 8
#> 135 99 23
#> 136 99 19
#> 137 99 7
#> 138 99 62
#> 139 99 7
#> 140 99 7
#> 141 99 38
#> 142 99 14
#> 143 99 20
#> 144 99 53
#> 145 99 4
#> 146 99 59
#> 147 99 18
#> 148 99 25
#> 149 99 -1
#> 150 99 57
#> 151 133 8
#> 152 133 23
#> 153 133 -1
#> 154 133 51
#> 155 133 14
#> 156 133 74
#> 157 133 -1
#> 158 133 23
#> 159 133 24
#> 160 133 5
#> 161 133 38
#> 162 133 52
#> 163 133 63
#> 164 133 17
#> 165 133 74
#> 166 133 5
#> 167 133 11
#> 168 133 6
#> 169 133 67
#> 170 133 5
#> 171 133 22
#> 172 76 55
#> 173 76 75
#> 174 76 53
#> 175 76 7
#> 176 76 23
#> 177 76 72
#> 178 76 170
#> 179 76 12
#> 180 76 169
#> 181 76 16
#> 182 76 44
#> 183 76 10
#> 184 76 25
#> 185 76 21
#> 186 76 9
#> 187 76 107
#> 188 76 -1
#> 189 76 107
#> 190 76 -1
#> 191 76 102
#> 192 76 7
#> 193 76 28
#> 194 92 28
#> 195 92 12
#> 196 92 66
#> 197 92 12
#> 198 92 24
#> 199 92 19
#> 200 92 8
#> 201 92 21
#> 202 92 24
#> 203 92 9
#> 204 92 30
#> 205 92 -1
#> 206 92 9
#> 207 92 8
#> 208 92 73
#> 209 92 107
#> 210 92 10
#> 211 92 24
#> 212 92 169
#> 213 92 5
#> 214 92 65
#> 215 97 9
#> 216 97 24
#> 217 97 22
#> 218 97 6
#> 219 97 12
#> 220 97 21
#> 221 97 2
#> 222 97 53
#> 223 97 83
#> 224 97 -1
#> 225 97 7
#> 226 97 170
#> 227 97 3
#> 228 97 72
#> 229 97 49
#> 230 97 -1
#> 231 97 12
#> 232 97 -1
#> 233 107 17
#> 234 107 95
#> 235 107 20
#> 236 107 14
#> 237 107 11
#> 238 107 47
#> 239 107 9
#> 240 107 20
#> 241 107 -1
#> 242 107 37
#> 243 107 169
#> 244 107 -1
#> 245 107 103
#> 246 107 85
#> 247 107 13
#> 248 107 11
#> 249 107 91
#> 250 107 10
#> 251 107 -1
#> 252 107 22
#> 253 107 21
#> 254 107 25
#> 255 120 69
#> 256 120 21
#> 257 120 55
#> 258 120 39
#> 259 120 200
#> 260 120 10
#> 261 120 25
#> 262 120 2
#> 263 120 9
#> 264 120 39
#> 265 120 57
#> 266 120 9
#> 267 120 -1
#> 268 120 68
#> 269 120 88
#> 270 120 73
#> 271 120 8
#> 272 120 126
#> 273 120 15
#> 274 120 2
#> 275 120 -1
#> 276 183 54
#> 277 183 1
#> 278 183 -1
#> 279 183 107
#> 280 183 103
#> 281 183 -1
#> 282 183 60
#> 283 183 -1
#> 284 183 14
#> 285 183 51
#> 286 183 27
#> 287 183 -1
#> 288 183 -1
#> 289 183 3
#> 290 183 3
#> 291 183 30
#> 292 183 13
#> 293 183 232
#> 294 183 20
#> 295 183 28
#> 296 185 5
#> 297 185 15
#> 298 185 16
#> 299 185 -1
#> 300 185 -1
#> 301 185 17
#> 302 185 5
#> 303 185 102
#> 304 185 7
#> 305 185 28
#> 306 185 9
#> 307 185 15
#> 308 185 -1
#> 309 185 20
#> 310 185 28
#> 311 185 -1
#> 312 185 -1
#> 313 185 190
#> 314 185 -1
#> 315 113 4
#> 316 113 20
#> 317 113 12
#> 318 113 23
#> 319 113 25
#> 320 113 36
#> 321 113 -1
#> 322 113 66
#> 323 113 87
#> 324 113 20
#> 325 113 17
#> 326 113 24
#> 327 113 -1
#> 328 113 140
#> 329 113 21
#> 330 113 -1
#> 331 113 36
#> 332 113 34
#> 333 113 9
#> 334 113 8
#> 335 134 8
#> 336 134 7
#> 337 134 21
#> 338 134 133
#> 339 134 50
#> 340 134 -1
#> 341 134 6
#> 342 134 18
#> 343 134 -1
#> 344 134 78
#> 345 134 10
#> 346 134 18
#> 347 134 9
#> 348 134 8
#> 349 134 -1
#> 350 134 -1
#> 351 134 -1
#> 352 134 -1
#> 353 134 -1
#> 354 134 -1
#> 355 134 -1
#> 356 114 21
#> 357 114 239
#> 358 114 -1
#> 359 114 73
#> 360 114 12
#> 361 114 8
#> 362 114 107
#> 363 114 -1
#> 364 114 2
#> 365 114 40
#> 366 114 17
#> 367 114 31
#> 368 114 19
#> 369 114 9
#> 370 114 21
#> 371 114 112
#> 372 114 24
#> 373 114 169
#> 374 114 17
#> 375 114 167
#> 376 114 9
#> 377 136 8
#> 378 136 11
#> 379 136 42
#> 380 136 -1
#> 381 136 -1
#> 382 136 5
#> 383 136 39
#> 384 136 -1
#> 385 136 15
#> 386 136 5
#> 387 136 108
#> 388 136 24
#> 389 136 -1
#> 390 136 46
#> 391 136 10
#> 392 136 8
#> 393 136 87
#> 394 136 25
#> 395 136 26
#> 396 136 145
#> 397 136 4
#> 398 161 14
#> 399 161 9
#> 400 161 10
#> 401 161 -1
#> 402 161 2
#> 403 161 15
#> 404 161 -1
#> 405 161 -1
#> 406 161 -1
#> 407 161 6
#> 408 161 21
#> 409 161 106
#> 410 161 122
#> 411 161 10
#> 412 161 72
#> 413 161 28
#> 414 161 -1
#> 415 161 15
#> 416 161 -1
#> 417 161 -1
#> 418 161 12
#> 419 106 8
#> 420 106 8
#> 421 106 -1
#> 422 106 -1
#> 423 106 -1
#> 424 106 12
#> 425 106 -1
#> 426 106 64
#> 427 106 -1
#> 428 106 2
#> 429 106 -1
#> 430 106 -1
#> 431 106 6
#> 432 106 -1
#> 433 106 -1
#> 434 106 13
#> 435 106 43
#> 436 106 1
#> 437 106 20
#> 438 92 26
#> 439 92 38
#> 440 92 -1
#> 441 92 24
#> 442 92 13
#> 443 92 11
#> 444 92 109
#> 445 92 13
#> 446 92 11
#> 447 92 140
#> 448 92 14
#> 449 92 -1
#> 450 92 36
#> 451 92 -1
#> 452 92 52
#> 453 92 11
#> 454 92 -1
#> 455 92 34
#> 456 92 -1
#> 457 92 9
#> 458 92 31
#> 459 43 5
#> 460 43 14
#> 461 43 8
#> 462 43 21
#> 463 43 16
#> 464 43 50
#> 465 43 8
#> 466 43 133
#> 467 43 20
#> 468 43 -1
#> 469 43 33
#> 470 43 -1
#> 471 43 24
#> 472 43 18
#> 473 43 22
#> 474 43 25
#> 475 43 35
#> 476 43 78
#> 477 43 10
#> 478 43 9
#> 479 107 5
#> 480 107 1
#> 481 107 10
#> 482 107 -1
#> 483 107 8
#> 484 107 30
#> 485 107 51
#> 486 107 -1
#> 487 107 -1
#> 488 107 -1
#> 489 107 -1
#> 490 107 -1
#> 491 107 -1
#> 492 107 -1
#> 493 107 32
#> 494 107 21
#> 495 107 -1
#> 496 107 -1
#> 497 107 26
#> 498 107 22
#> 499 107 9
#> 500 271 124
#> 501 271 107
#> 502 271 18
#> 503 271 33
#> 504 271 -1
#> 505 271 -1
#> 506 271 -1
#> 507 271 -1
#> 508 271 73
#> 509 271 10
#> 510 271 8
#> 511 271 -1
#> 512 271 -1
#> 513 271 10
#> 514 271 8
#> 515 271 73
#> 516 271 -1
#> 517 271 40
#> 518 271 17
#> 519 271 44
#> 520 271 8
#> 521 89 4
#> 522 89 19
#> 523 89 -1
#> 524 89 19
#> 525 89 11
#> 526 89 34
#> 527 89 44
#> 528 89 54
#> 529 89 9
#> 530 89 10
#> 531 89 8
#> 532 89 21
#> 533 89 112
#> 534 89 73
#> 535 89 14
#> 536 89 169
#> 537 89 -1
#> 538 89 8
#> 539 89 23
#> 540 89 167
#> 541 164 17
#> 542 164 21
#> 543 164 11
#> 544 164 9
#> 545 164 12
#> 546 164 42
#> 547 164 -1
#> 548 164 5
#> 549 164 48
#> 550 164 24
#> 551 164 16
#> 552 164 15
#> 553 164 3
#> 554 164 46
#> 555 164 38
#> 556 164 5
#> 557 164 44
#> 558 164 24
#> 559 164 -1
#> 560 164 -1
#> 561 164 62
#> 562 148 109
#> 563 148 17
#> 564 148 49
#> 565 148 -1
#> 566 148 46
#> 567 148 10
#> 568 148 8
#> 569 148 -1
#> 570 148 87
#> 571 148 -1
#> 572 148 7
#> 573 148 87
#> 574 148 145
#> 575 148 62
#> 576 148 4
#> 577 148 -1
#> 578 148 28
#> 579 148 24
#> 580 148 22
#> 581 148 71
#> 582 148 -1
#> 583 106 107
#> 584 106 38
#> 585 106 22
#> 586 106 190
#> 587 106 73
#> 588 106 12
#> 589 106 11
#> 590 106 -1
#> 591 106 15
#> 592 106 35
#> 593 106 78
#> 594 106 9
#> 595 106 10
#> 596 106 -1
#> 597 106 23
#> 598 106 21
#> 599 106 -1
#> 600 106 35
#> 601 106 -1
#> 602 114 52
#> 603 114 -1
#> 604 114 167
#> 605 114 -1
#> 606 114 37
#> 607 114 21
#> 608 114 11
#> 609 114 14
#> 610 114 69
#> 611 114 -1
#> 612 114 7
#> 613 114 22
#> 614 114 30
#> 615 114 8
#> 616 114 24
#> 617 114 32
#> 618 114 20
#> 619 114 26
#> 620 114 16
#> 621 123 109
#> 622 123 -1
#> 623 123 66
#> 624 123 22
#> 625 123 9
#> 626 123 -1
#> 627 123 -1
#> 628 123 21
#> 629 123 33
#> 630 123 -1
#> 631 123 28
#> 632 123 46
#> 633 123 58
#> 634 123 44
#> 635 123 -1
#> 636 123 73
#> 637 123 12
#> 638 123 8
#> 639 123 -1
#> 640 123 107
#> 641 136 11
#> 642 136 24
#> 643 136 10
#> 644 136 -1
#> 645 136 -1
#> 646 136 -1
#> 647 136 10
#> 648 136 2
#> 649 136 -1
#> 650 136 -1
#> 651 136 -1
#> 652 136 16
#> 653 136 -1
#> 654 136 15
#> 655 136 10
#> 656 136 31
#> 657 136 -1
#> 658 136 -1
#> 659 136 -1
#> 660 136 44#6. Making Plot
Pembuatan variabel ‘job_count’ berdasarkan sector
job_count <- job %>%
group_by(Sector) %>%
summarise(count_cat = n()) %>%
arrange(desc(count_cat)) %>%
ungroup()
job_count#> # A tibble: 23 x 2
#> Sector count_cat
#> <fct> <int>
#> 1 Information Technology 178
#> 2 Business Services 120
#> 3 -1 71
#> 4 Biotech & Pharmaceuticals 66
#> 5 Aerospace & Defense 46
#> 6 Insurance 32
#> 7 Finance 31
#> 8 Manufacturing 23
#> 9 Health Care 21
#> 10 Government 17
#> # ... with 13 more rowsSektor yang paling banyak membutuhkan data scientist adalah sektor ‘Information Technology’sebanyak 178, sedangkan sektor ’Non-Profit’ merupakan sektor yang paling sedikit membutuhkan data scientist yaitu hanya sebanyak 1.
Penamaan label
job_count2 <- job_count %>%
mutate(label = glue("Sector {Sector}
Job Count: {count_cat}"))
head(job_count2)#> # A tibble: 6 x 3
#> Sector count_cat label
#> <fct> <int> <glue>
#> 1 Information Technology 178 "Sector Information Technology\nJob Count~
#> 2 Business Services 120 "Sector Business Services\nJob Count: 120"
#> 3 -1 71 "Sector -1\nJob Count: 71"
#> 4 Biotech & Pharmaceuticals 66 "Sector Biotech & Pharmaceuticals\nJob Co~
#> 5 Aerospace & Defense 46 "Sector Aerospace & Defense\nJob Count: 4~
#> 6 Insurance 32 "Sector Insurance\nJob Count: 32"Pembuatan plot dari ‘job_count’
plot1<- ggplot(job_count2,aes(x=count_cat,y= reorder(Sector,count_cat), text = label)) +
geom_col(aes(fill=count_cat)) +
scale_fill_gradient(low = 'black', high = 'green') +
labs(title="The Most Sector Needed Data Science", x="job count", y=NULL) +
theme_bw() + theme(legend.position = "none")
plot1
*Pembuatan tooltip**
library (plotly)
ggplotly(plot1, tooltip = "text")Pembuatan variabel ‘job_age’ untuk membentuk plot ke-2
job_age <- job_clean %>%
mutate(label = glue("Company Age: {company_age}
Avg Salary: {avg_salary}"))
head(job_age)#> Job.Title Salary.Estimate Rating Company.Name Location
#> 1 Sr Data Scientist 137-171 3.1 Healthfirst New York, NY
#> 2 Data Scientist 137-171 4.2 ManTech Chantilly, VA
#> 3 Data Scientist 137-171 3.8 Analysis Group Boston, MA
#> 4 Data Scientist 137-171 3.5 INFICON Newton, MA
#> 5 Data Scientist 137-171 2.9 Affinity Solutions New York, NY
#> 6 Data Scientist 137-171 4.2 HG Insights Santa Barbara, CA
#> Size Industry
#> 1 1001 to 5000 employees Insurance Carriers
#> 2 5001 to 10000 employees Research & Development
#> 3 1001 to 5000 employees Consulting
#> 4 501 to 1000 employees Electrical & Electronic Manufacturing
#> 5 51 to 200 employees Advertising & Marketing
#> 6 51 to 200 employees Computer Hardware & Software
#> Sector avg_salary company_age
#> 1 Insurance 154 27
#> 2 Business Services 154 52
#> 3 Business Services 154 39
#> 4 Manufacturing 154 20
#> 5 Business Services 154 22
#> 6 Information Technology 154 10
#> label
#> 1 Company Age: 27\nAvg Salary: 154
#> 2 Company Age: 52\nAvg Salary: 154
#> 3 Company Age: 39\nAvg Salary: 154
#> 4 Company Age: 20\nAvg Salary: 154
#> 5 Company Age: 22\nAvg Salary: 154
#> 6 Company Age: 10\nAvg Salary: 154Plot ke-2 dari variabel ‘job_age’
plot2 <- job_age %>% ggplot() +
geom_point(aes(x = company_age, y = avg_salary, text = label, col = job_age$avg_salary)) +
labs(x = "Company Age", y = "Avg Salary",
title = "Company Age vs Avg Salary")
plot2
Plot di atas menunjukan korelasi dari company age dengan salary
cor(job_clean$company_age, job_clean$avg_salary)#> [1] -0.04538952Korelasi dari company age dengan salary adalah sebesar -0.04538952
Pembuatan tooltip
ggplotly(plot2, tooltip = "text")Pembuatan variabel ’job_10 untuk plot ke-3
job_10 <- job_clean %>%
filter(Industry == "Consulting") %>%
group_by(Location) %>%
ungroup() %>%
arrange(desc(avg_salary))
head(job_10, 10)#> # A tibble: 10 x 10
#> Job.Title Salary.Estimate Rating Company.Name Location Size Industry Sector
#> <fct> <fct> <dbl> <fct> <fct> <fct> <fct> <fct>
#> 1 Data Scie~ "212-331 " 3.5 Aptive Washing~ 51 t~ Consult~ Busin~
#> 2 Data Scie~ "145-225" 0 Microagility New Yor~ 1 to~ Consult~ Busin~
#> 3 Data Scie~ "145-225" 4.6 Conch Techn~ Concord~ 51 t~ Consult~ Busin~
#> 4 Clinical ~ "128-201 " 3.8 NYSTEC New Yor~ 51 t~ Consult~ Busin~
#> 5 Data Scie~ "128-201 " 4.5 E3 Federal ~ Chantil~ 501 ~ Consult~ Busin~
#> 6 Data Scie~ "124-198 " 4.1 Latitude, I~ Washing~ 1 to~ Consult~ Busin~
#> 7 Data Scie~ "124-198 " 3.7 Booz Allen ~ Oxnard,~ 1000~ Consult~ Busin~
#> 8 Data Scie~ "124-198 " 0 Microagility New Yor~ 1 to~ Consult~ Busin~
#> 9 Data Scie~ "124-198 " 4.1 Coverent McLean,~ 51 t~ Consult~ Busin~
#> 10 Data Scie~ "137-171 " 3.8 Analysis Gr~ Boston,~ 1001~ Consult~ Busin~
#> # ... with 2 more variables: avg_salary <int>, company_age <int>Pembuatan label untuk plot ke-3
library(scales)
job_10_2 <- head(job_10, 10) %>%
mutate(label = glue("Industry: {Location}
Average Salary: {comma(avg_salary)}"))
job_10_2#> # A tibble: 10 x 11
#> Job.Title Salary.Estimate Rating Company.Name Location Size Industry Sector
#> <fct> <fct> <dbl> <fct> <fct> <fct> <fct> <fct>
#> 1 Data Scie~ "212-331 " 3.5 Aptive Washing~ 51 t~ Consult~ Busin~
#> 2 Data Scie~ "145-225" 0 Microagility New Yor~ 1 to~ Consult~ Busin~
#> 3 Data Scie~ "145-225" 4.6 Conch Techn~ Concord~ 51 t~ Consult~ Busin~
#> 4 Clinical ~ "128-201 " 3.8 NYSTEC New Yor~ 51 t~ Consult~ Busin~
#> 5 Data Scie~ "128-201 " 4.5 E3 Federal ~ Chantil~ 501 ~ Consult~ Busin~
#> 6 Data Scie~ "124-198 " 4.1 Latitude, I~ Washing~ 1 to~ Consult~ Busin~
#> 7 Data Scie~ "124-198 " 3.7 Booz Allen ~ Oxnard,~ 1000~ Consult~ Busin~
#> 8 Data Scie~ "124-198 " 0 Microagility New Yor~ 1 to~ Consult~ Busin~
#> 9 Data Scie~ "124-198 " 4.1 Coverent McLean,~ 51 t~ Consult~ Busin~
#> 10 Data Scie~ "137-171 " 3.8 Analysis Gr~ Boston,~ 1001~ Consult~ Busin~
#> # ... with 3 more variables: avg_salary <int>, company_age <int>, label <glue>Pembuatan Plot ke-3
plot3 <- ggplot(job_10_2, aes(x = avg_salary, y = reorder(Location, avg_salary), text = label)) +
geom_col(aes(fill = avg_salary)) +
labs(title = "Avarege Salary of Every Country",
x = "Avarage Salary",
y = NULL) +
scale_fill_gradient(low = "orange", high= "maroon") +
theme_minimal() +
theme(legend.position = "none")
plot3
Pembuatan tooltip
ggplotly(plot3, tooltip = "text")Berdasarkan plot di atas, diketahui bahwa pada ‘Consulting’ di Washington DC avarage salary paling banyak berada di sektar 271