Analyze Question Template

[N8sGit]

1. Pick a question from our list. Use a few sentences to describe it [DESCRIBE QUESTION]
   2. Analyze question. Use Pandas's analytical methods, Where applicable use .plot and .describe() , standard deviation .std() , aggregation, correlation (.corr()) , comparison, summary statistics, sentiment analysis, and time series analysis . [ANALYZE QUESTION]
   3. Visualize question. Pick a plot type that best suits the data. https://matplotlib.org/stable/plot_types/index.html [VISUALIZATION X 1-3]
   4. Summarize findings for that question. Write a paragraph or two. What did the data say? Did what we see match what we expected? What might explain the relationships we did see? [SUMMARIZE QUESTION FINDINGS]

[kalAnglin]

List of Questions 1.Geographic distribution of ML jobs ? Where are the jobs being concentrated ? Onsite/Remote/Hybrid? / How do job opportunities in AI vary by geographic location (ml_linkedin_postings) -Geographic distribution/Concentration & ML jobs Onsite/Remote/Hybrid

2. What is the relative proportion of ML jobs to non-ai in tech? (linkedin_2023-2024)
3. What’s the average  education level expectation?
4. Correlations between investment and job growth?
5. Correlations between layoffs and ML hirings/between unemployment rates and ML hirings? (2022_census_data)
6. Linkedin Posting: What are the most in-demand skills in job listings related to machine learning and AI? (ml_linked_postings)
7. What are the trends in seniority levels for AI and tech-related job postings? (ml_jobs_json)
8. Which industries are hiring the most machine learning engineers? (ai_company_profit)
9. What educational backgrounds are most common in job listings for machine learning engineers?
10. How do salaries for machine learning positions compare across different cities or states?