Objective

The objective of this analysis is to explore future AI career opportunities by examining industry growth, salary trends, education requirements and experience levels.

Constraints

The analysis is limited to the variables available in the dataset and may not fully represent real-world labour market conditions. Some variables show limited variation across categories, which may reduce the strength of certain insights. In addition, external factors such as economic conditions, government policies and industry-specific trends are not considered in this analysis.

industry_growth <- jobs %>%
  group_by(Industry) %>%
  summarise(
    openings_2024 = mean(`Job Openings (2024)`, na.rm = TRUE),
    openings_2030 = mean(`Projected Openings (2030)`, na.rm = TRUE)
  )

growth_long <- industry_growth %>%
  pivot_longer(
    cols = c(openings_2024, openings_2030),
    names_to = "Year",
    values_to = "Openings"
  )

plot_ly(
  growth_long,
  x = ~Year,
  y = ~Openings,
  color = ~Industry,
  type = "scatter",
  mode = "lines+markers"
) %>%
  layout(
    title = "Future Job Demand by Industry (2024 vs 2030)",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Average Job Openings")
  )

Chart 1: Future Job Demand by Industry (2024 vs 2030)

This chart compares average job openings in 2024 with projected openings in 2030 across industries. Most industries show an increase in projected openings, suggesting that demand for AI-related careers is expected to continue growing in the future. Industries such as Information Technology and Healthcare appear to have stronger growth compared to other sectors.

heatmap_data <- jobs %>%
  group_by(Industry, `Required Education`) %>%
  summarise(
    avg_salary = mean(`Median Salary (USD)`, na.rm = TRUE),
    .groups = "drop"
  )

ggplot(
  heatmap_data,
  aes(
    x = Industry,
    y = `Required Education`,
    fill = avg_salary
  )
) +
  geom_tile(color = "white") +
  labs(
    title = "Average Salary by Industry and Education Level",
    x = "Industry",
    y = "Required Education",
    fill = "Average Salary"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Chart 2: Average Salary by Industry and Education Level

This heatmap shows how average salaries vary across industries and education levels. Higher education levels generally correspond with higher salaries, although salary differences also exist between industries. The results suggest that both educational qualifications and industry selection can influence earning potential.

salary_edu <- jobs %>%
  group_by(`Required Education`, `AI Impact Level`) %>%
  summarise(
    avg_salary = mean(`Median Salary (USD)`, na.rm = TRUE),
    .groups = "drop"
  )

plot_ly(
  salary_edu,
  x = ~`Required Education`,
  y = ~avg_salary,
  color = ~`AI Impact Level`,
  type = "bar"
) %>%
  layout(
    barmode = "group",
    title = "Average Salary by Education Level and AI Impact Level",
    xaxis = list(title = "Required Education"),
    yaxis = list(title = "Average Salary (USD)")
  )

Chart 3: Average Salary by Education Level and AI Impact Level

This chart compares average salaries across education levels and AI impact categories. Individuals with higher qualifications generally receive higher salaries regardless of AI impact level. The findings suggest that education remains an important factor in achieving higher-paying roles in an AI-driven workforce.

growth_chart <- jobs %>%
  group_by(Industry) %>%
  summarise(
    openings_2024 = mean(`Job Openings (2024)`, na.rm = TRUE),
    openings_2030 = mean(`Projected Openings (2030)`, na.rm = TRUE)
  ) %>%
  mutate(
    growth = openings_2030 - openings_2024
  ) %>%
  arrange(growth)

plot_ly(
  growth_chart,
  x = ~growth,
  y = ~reorder(Industry, growth),
  type = "bar",
  orientation = "h"
) %>%
  layout(
    title = "Projected Job Growth by Industry (2024–2030)",
    xaxis = list(title = "Growth in Job Openings"),
    yaxis = list(title = "Industry")
  )

Chart 4: Projected Job Growth by Industry (2024–2030)

This chart highlights the industries expected to experience the greatest growth in job openings between 2024 and 2030. Information Technology and Healthcare show stronger projected growth compared to other industries. This suggests that AI adoption may continue to create employment opportunities in technology-focused sectors.

experience_salary <- jobs %>%
  group_by(`Experience Required (Years)`, `AI Impact Level`) %>%
  summarise(
    avg_salary = mean(`Median Salary (USD)`, na.rm = TRUE),
    .groups = "drop"
  )

plot_ly(
  experience_salary,
  x = ~`Experience Required (Years)`,
  y = ~avg_salary,
  color = ~`AI Impact Level`,
  type = "scatter",
  mode = "lines+markers"
) %>%
  layout(
    title = "Average Salary by Experience and AI Impact Level",
    xaxis = list(title = "Experience Required (Years)"),
    yaxis = list(title = "Average Salary (USD)")
  )

Chart 5: Average Salary by Experience and AI Impact Level

This chart examines how salaries change with experience across different AI impact levels. In general, employees with more experience tend to earn higher salaries than those with less experience. Although some fluctuations are visible, the overall trend indicates that experience remains an important factor influencing salary growth.

Conclusion

Future job demand is expected to increase across most industries. Information Technology and Healthcare demonstrate strong projected growth. Higher education levels are generally associated with higher salaries. Professional experience contributes to increased earning potential. AI-related careers continue to provide promising future employment opportunities.

References

Kaggle. (2025). AI Job Trends Dataset. https://www.kaggle.com/datasets/sahilislam007/ai-impact-on-job-market-20242030

Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2024). dplyr: A grammar of data manipulation (R package version 1.1.4). https://CRAN.R-project.org/package=dplyr