Education and Career Success
Deepthi Bommashetihalli Lakshmipathi (S4113338)
Introduction
- Goal: This project explores how educational achievements, soft skills, and internships influence career success indicators like starting salary, promotions, and job satisfaction. .
- Why it matters: Students and career planners often ask—does higher GPA or field of study actually affect starting salary or job satisfaction?
- Target Audience:
- Students planning their careers
- University advisors
- Employers evaluating educational impact
- General public interested in education and jobs
Load libraries
Data Source & Preparation
- Open dataset from “https://www.kaggle.com/datasets/adilshamim8/education-and-career-success”.
- Key variables: GPA, Field of Study, Internship, Starting Salary, Job Level, Job Satisfaction, Gender.
- Data cleaned for missing values, inconsistent categories, and renamed for clarity.
Load the CSV dataset
## # A tibble: 6 × 20
## Student_ID Age Gender High_School_GPA SAT_Score University_Ranking
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 S00001 24 Male 3.58 1052 291
## 2 S00002 21 Other 2.52 1211 112
## 3 S00003 28 Female 3.42 1193 715
## 4 S00004 25 Male 2.43 1497 170
## 5 S00005 22 Male 2.08 1012 599
## 6 S00006 24 Male 2.4 1600 631
## # ℹ 14 more variables: University_GPA <dbl>, Field_of_Study <chr>,
## # Internships_Completed <dbl>, Projects_Completed <dbl>,
## # Certifications <dbl>, Soft_Skills_Score <dbl>, Networking_Score <dbl>,
## # Job_Offers <dbl>, Starting_Salary <dbl>, Career_Satisfaction <dbl>,
## # Years_to_Promotion <dbl>, Current_Job_Level <chr>, Work_Life_Balance <dbl>,
## # Entrepreneurship <chr>## Rows: 5,000
## Columns: 20
## $ Student_ID <chr> "S00001", "S00002", "S00003", "S00004", "S00005"…
## $ Age <dbl> 24, 21, 28, 25, 22, 24, 27, 20, 24, 28, 28, 25, …
## $ Gender <chr> "Male", "Other", "Female", "Male", "Male", "Male…
## $ High_School_GPA <dbl> 3.58, 2.52, 3.42, 2.43, 2.08, 2.40, 2.36, 2.68, …
## $ SAT_Score <dbl> 1052, 1211, 1193, 1497, 1012, 1600, 1011, 1074, …
## $ University_Ranking <dbl> 291, 112, 715, 170, 599, 631, 610, 240, 337, 138…
## $ University_GPA <dbl> 3.96, 3.63, 2.63, 2.81, 2.48, 3.78, 3.83, 2.84, …
## $ Field_of_Study <chr> "Arts", "Law", "Medicine", "Computer Science", "…
## $ Internships_Completed <dbl> 3, 4, 4, 3, 4, 2, 0, 1, 2, 1, 2, 2, 2, 0, 1, 3, …
## $ Projects_Completed <dbl> 7, 7, 8, 9, 6, 3, 1, 5, 3, 5, 7, 2, 0, 4, 2, 5, …
## $ Certifications <dbl> 2, 3, 1, 1, 4, 2, 3, 5, 0, 3, 5, 3, 5, 3, 3, 2, …
## $ Soft_Skills_Score <dbl> 9, 8, 1, 10, 10, 2, 3, 5, 5, 10, 8, 2, 2, 8, 1, …
## $ Networking_Score <dbl> 8, 1, 9, 6, 9, 2, 3, 1, 5, 2, 1, 9, 9, 6, 8, 9, …
## $ Job_Offers <dbl> 5, 4, 0, 1, 4, 1, 2, 2, 2, 0, 5, 5, 2, 2, 1, 3, …
## $ Starting_Salary <dbl> 27200, 25000, 42400, 57400, 47600, 68400, 55500,…
## $ Career_Satisfaction <dbl> 4, 1, 9, 7, 9, 9, 7, 2, 2, 4, 9, 7, 9, 4, 9, 7, …
## $ Years_to_Promotion <dbl> 5, 1, 3, 5, 5, 2, 4, 3, 2, 2, 1, 4, 4, 3, 3, 4, …
## $ Current_Job_Level <chr> "Entry", "Mid", "Entry", "Mid", "Entry", "Entry"…
## $ Work_Life_Balance <dbl> 7, 7, 7, 5, 2, 8, 3, 3, 2, 2, 2, 6, 8, 3, 6, 3, …
## $ Entrepreneurship <chr> "No", "No", "No", "No", "No", "Yes", "No", "No",…Initial data inspection
# Summary statistics for numeric columns
summary(education_data)## Student_ID Age Gender High_School_GPA
## Length:5000 Min. :18.00 Length:5000 Min. :2.000
## Class :character 1st Qu.:20.00 Class :character 1st Qu.:2.500
## Mode :character Median :23.00 Mode :character Median :2.990
## Mean :23.44 Mean :2.997
## 3rd Qu.:26.00 3rd Qu.:3.500
## Max. :29.00 Max. :4.000
## SAT_Score University_Ranking University_GPA Field_of_Study
## Min. : 900 Min. : 1.0 Min. :2.00 Length:5000
## 1st Qu.:1076 1st Qu.: 256.0 1st Qu.:2.52 Class :character
## Median :1257 Median : 501.5 Median :3.03 Mode :character
## Mean :1254 Mean : 504.3 Mean :3.02
## 3rd Qu.:1432 3rd Qu.: 759.0 3rd Qu.:3.51
## Max. :1600 Max. :1000.0 Max. :4.00
## Internships_Completed Projects_Completed Certifications Soft_Skills_Score
## Min. :0.000 Min. :0.000 Min. :0.000 Min. : 1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 3.000
## Median :2.000 Median :5.000 Median :3.000 Median : 6.000
## Mean :1.982 Mean :4.563 Mean :2.512 Mean : 5.546
## 3rd Qu.:3.000 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.: 8.000
## Max. :4.000 Max. :9.000 Max. :5.000 Max. :10.000
## Networking_Score Job_Offers Starting_Salary Career_Satisfaction
## Min. : 1.000 Min. :0.000 Min. : 25000 Min. : 1.000
## 1st Qu.: 3.000 1st Qu.:1.000 1st Qu.: 40200 1st Qu.: 3.000
## Median : 6.000 Median :2.000 Median : 50300 Median : 6.000
## Mean : 5.538 Mean :2.489 Mean : 50564 Mean : 5.578
## 3rd Qu.: 8.000 3rd Qu.:4.000 3rd Qu.: 60500 3rd Qu.: 8.000
## Max. :10.000 Max. :5.000 Max. :101000 Max. :10.000
## Years_to_Promotion Current_Job_Level Work_Life_Balance Entrepreneurship
## Min. :1.000 Length:5000 Min. : 1.000 Length:5000
## 1st Qu.:2.000 Class :character 1st Qu.: 3.000 Class :character
## Median :3.000 Mode :character Median : 6.000 Mode :character
## Mean :3.016 Mean : 5.482
## 3rd Qu.:4.000 3rd Qu.: 8.000
## Max. :5.000 Max. :10.000# Check for missing values per column
colSums(is.na(education_data))## Student_ID Age Gender
## 0 0 0
## High_School_GPA SAT_Score University_Ranking
## 0 0 0
## University_GPA Field_of_Study Internships_Completed
## 0 0 0
## Projects_Completed Certifications Soft_Skills_Score
## 0 0 0
## Networking_Score Job_Offers Starting_Salary
## 0 0 0
## Career_Satisfaction Years_to_Promotion Current_Job_Level
## 0 0 0
## Work_Life_Balance Entrepreneurship
## 0 0# Preview in interactive table
datatable(education_data, options = list(scrollX = TRUE, pageLength = 5))Clean and preprocess data
# Clean column names (e.g., High_School_GPA → high_school_gpa)
education_data <- clean_names(education_data)
# View column names to verify changes
colnames(education_data)## [1] "student_id" "age" "gender"
## [4] "high_school_gpa" "sat_score" "university_ranking"
## [7] "university_gpa" "field_of_study" "internships_completed"
## [10] "projects_completed" "certifications" "soft_skills_score"
## [13] "networking_score" "job_offers" "starting_salary"
## [16] "career_satisfaction" "years_to_promotion" "current_job_level"
## [19] "work_life_balance" "entrepreneurship"# Convert character columns to factors
education_data <- education_data %>%
mutate(
gender = factor(gender),
field_of_study = factor(field_of_study),
entrepreneurship = factor(entrepreneurship, levels = c("No", "Yes")),
current_job_level = factor(current_job_level, ordered = TRUE, levels = c("Entry", "Mid", "Senior", "Executive"))
)
# Optional: Remove rows with missing values
education_data <- drop_na(education_data)
# Preview the cleaned data
glimpse(education_data)## Rows: 5,000
## Columns: 20
## $ student_id <chr> "S00001", "S00002", "S00003", "S00004", "S00005"…
## $ age <dbl> 24, 21, 28, 25, 22, 24, 27, 20, 24, 28, 28, 25, …
## $ gender <fct> Male, Other, Female, Male, Male, Male, Male, Mal…
## $ high_school_gpa <dbl> 3.58, 2.52, 3.42, 2.43, 2.08, 2.40, 2.36, 2.68, …
## $ sat_score <dbl> 1052, 1211, 1193, 1497, 1012, 1600, 1011, 1074, …
## $ university_ranking <dbl> 291, 112, 715, 170, 599, 631, 610, 240, 337, 138…
## $ university_gpa <dbl> 3.96, 3.63, 2.63, 2.81, 2.48, 3.78, 3.83, 2.84, …
## $ field_of_study <fct> Arts, Law, Medicine, Computer Science, Engineeri…
## $ internships_completed <dbl> 3, 4, 4, 3, 4, 2, 0, 1, 2, 1, 2, 2, 2, 0, 1, 3, …
## $ projects_completed <dbl> 7, 7, 8, 9, 6, 3, 1, 5, 3, 5, 7, 2, 0, 4, 2, 5, …
## $ certifications <dbl> 2, 3, 1, 1, 4, 2, 3, 5, 0, 3, 5, 3, 5, 3, 3, 2, …
## $ soft_skills_score <dbl> 9, 8, 1, 10, 10, 2, 3, 5, 5, 10, 8, 2, 2, 8, 1, …
## $ networking_score <dbl> 8, 1, 9, 6, 9, 2, 3, 1, 5, 2, 1, 9, 9, 6, 8, 9, …
## $ job_offers <dbl> 5, 4, 0, 1, 4, 1, 2, 2, 2, 0, 5, 5, 2, 2, 1, 3, …
## $ starting_salary <dbl> 27200, 25000, 42400, 57400, 47600, 68400, 55500,…
## $ career_satisfaction <dbl> 4, 1, 9, 7, 9, 9, 7, 2, 2, 4, 9, 7, 9, 4, 9, 7, …
## $ years_to_promotion <dbl> 5, 1, 3, 5, 5, 2, 4, 3, 2, 2, 1, 4, 4, 3, 3, 4, …
## $ current_job_level <ord> Entry, Mid, Entry, Mid, Entry, Entry, Mid, Entry…
## $ work_life_balance <dbl> 7, 7, 7, 5, 2, 8, 3, 3, 2, 2, 2, 6, 8, 3, 6, 3, …
## $ entrepreneurship <fct> No, No, No, No, No, Yes, No, No, No, No, No, Yes…# Preview in interactive table
datatable(education_data, options = list(scrollX = TRUE, pageLength = 5))Distribution of Starting Salaries
- The distribution is slightly right-skewed, with most salaries clustered around $40,000–$60,000.
Starting Salary by Gender
- Male students tend to have a slightly higher starting salary on average. This finding prompts further analysis of other influencing variables.
ggplot(education_data, aes(x = gender, y = starting_salary, fill = gender)) +
geom_boxplot() +
scale_y_continuous(labels = comma) + # Format y-axis with commas
labs(
title = "Starting Salary by Gender",
y = "Starting Salary",
x = "Gender"
) +
theme_minimal()
GPA vs Salary
- This plot suggests a weak-to-moderate positive correlation between university GPA and starting salary.
ggplot(education_data, aes(x = university_gpa, y = starting_salary)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", col = "red") +
scale_y_continuous(labels = comma) + # This formats y-axis labels with commas
labs(title = "University GPA vs Starting Salary",
x = "University GPA",
y = "Starting Salary") +
theme_minimal()
Field of Study vs Job Level
- This visualisation highlights how certain fields, like Engineering or Business, may lead more often to higher job levels early in careers.
ggplot(education_data, aes(x = field_of_study, fill = current_job_level)) +
geom_bar(position = "fill") +
labs(title = "Job Level Distribution by Field of Study",
y = "Proportion",
x = "Field of Study") +
theme_minimal()
Internship and Career Success
- Internships appear to positively influence starting salaries, with those completing more internships earning higher pay.
ggplot(education_data, aes(x = as.factor(internships_completed), y = starting_salary)) +
geom_boxplot(fill = "lightgreen") +
scale_y_continuous(labels = comma) + # This will show numbers like 40,000 instead of 4e+04
labs(title = "Impact of Internships Completed on Starting Salary",
x = "Number of Internships Completed",
y = "Starting Salary")
Key Findings
- University GPA has a positive correlation with starting salary, suggesting academic performance plays a role in career outcomes.
- Internship experience is associated with higher starting salaries and quicker promotions, emphasizing the importance of practical exposure.
- Field of Study influences early job level attainment—e.g., Engineering and Business often lead to Mid/Senior roles faster.
- Gender disparities in starting salary persist, warranting further investigation and institutional reflection.
- Soft skills and networking scores also contribute meaningfully to career satisfaction, highlighting the non-academic dimensions of success.
Conclusion
This analysis confirms that multiple educational and experiential factors shape early career outcomes. Higher university GPA and internship experience consistently correlate with higher starting salaries and faster promotions. Additionally, certain fields of study offer clearer paths to senior roles, while soft skills and networking abilities significantly enhance job satisfaction. However, observed gender-based salary gaps suggest systemic issues that require deeper institutional analysis. These insights are valuable for students, educators, and employers striving to align educational pathways with meaningful career success.
Ethical Considerations
- The dataset is anonymized but may reflect systemic biases in salary or career advancement.
- Interpretations of gender differences should be approached with caution.
- Data cleaning choices (e.g., dropping missing rows) may affect representativeness.
Summary
This analysis of education and career data reveals that academic performance, practical experience, and field of study play important roles in shaping early career success. Higher GPAs and internship experience are consistently linked with better starting salaries and quicker progression to higher job levels. Certain fields such as Engineering and Business provide clearer paths to advancement, while soft skills and networking also significantly contribute to job satisfaction. Notably, observed gender differences in salary highlight the need for further investigation into systemic disparities. These findings are valuable for students, educators, and employers in making informed decisions about education and career planning.
References
- Dataset: [Kaggle - Education and Career Success]
(https://www.kaggle.com/datasets/adilshamim8/education-and-career-success)