This dataset contains information about data science job postings scraped from Glassdoor. The original data was uncleaned and then cleaned using Power Query in Excel. Various transformations and cleaning processes were applied to make the data suitable for analysis.
- Can you make the salary column into integers?
- What information can you extract out of job descriptions?
- How can you remove the numbers from the company name?
- How can you create some new features? (e.g., state column from the location column)
- Job Title: Title of the job posting
- Salary Estimate: Salary range for that particular job
- Salary Estimate Min: Minimum Salary for that particular job
- Salary Estimate Max: Maximum Salary for that particular job
- Job Description: Full description of the job
- Rating: Rating of the post
- Company: Name of the company
- State: State location of the company
- Location: Location of the company
- Headquarter: Location of the headquarters
- Size: Total employees in that company
- Founded: Date when the company was founded
- Type of ownership: Describes the company type (non-profit/public/private farm, etc.)
- Industry, Sector: Field the applicant will work in
- Revenue: Total revenue of the company
- Skills (Python, SQL, Stats & Probability, Excel, ML, DL, Tableau, Power BI, Hadoop, Spark, Hive, AWS, GCP, Azure): Boolean columns indicating the presence of specific skills
- Simple Job Title: Job type
- Avg Salary: Average Salary offered for that particular job
- What are the most common job titles among the listed positions?
- What is the distribution of average salary estimates across different job titles?
- What are the primary locations for job opportunities?
- What are the most common skills required for the data science field?
- What is the distribution of Minimum Average salary and Maximum Average salary estimates across different job titles?
- Which are the most common cloud platforms?
- Which are the most common Data Visualization tools?
These questions can serve as a starting point for exploring the dataset and extracting meaningful insights to inform decision-making processes in the job market.
Feel free to explore the dataset further and derive additional insights!
