This project helps you analyse the demand for data analysis skills using real job listings. The goal is to convert job descriptions into a clear, evidence-based skill roadmap that you can use for learning, resume writing, and interview preparation. By the end, you will have a small dataset, summary tables, and charts that show which skills are most requested.
Start by defining the scope. Choose a role and level such as Data Analyst (Fresher / 0–2 years) and a location focus such as India or your city. Then collect 30 to 50 job listings from platforms like LinkedIn, Naukri, Indeed, and company career pages. For each job, capture job title, company, location, experience range, salary if available, and the job description text. Store this in a spreadsheet or a CSV so you can analyse it in Python.
Next, create a skill dictionary. Make a list of skills you want to track, such as Python, SQL, Excel, Power BI, Tableau, statistics, data cleaning, dashboards, ETL, communication, and domain knowledge. Use simple keyword matching to mark whether each job mentions each skill. Keep the matching logic transparent and document it.
Then analyse and visualise. Calculate how often each skill appears and rank skills from highest to lowest demand. Create charts such as a bar chart of top skills, a heatmap of skill co-occurrence, and a comparison chart across job types or locations if you have enough data. Identify the most common skill combinations, such as SQL + dashboards, or Python + Pandas + visualisation.
Finally, write insights. Summarise the top 10 skills, the most common tool stacks, and what you should prioritise first. End with a 2 to 4 week learning plan based on your findings and create a README that explains your process and results.
