Data Scientist Roadmap for India: A Stage by Stage Plan

Months 1 to 2: Python and spreadsheets. Learn Python fundamentals, Pandas and advanced Excel. Your project: analyse a public Indian dataset, like IPL matches or metro rail ridership, and publish a clean notebook.

Months 3 to 4: Statistics and SQL. Learn descriptive statistics, hypothesis testing and write SQL daily. Your project: build an analytics report on an e-commerce dataset with SQL and present five insights with charts.

Months 5 to 6: Machine learning foundations. Regression, classification, clustering and model evaluation with scikit-learn. Your project: predict customer churn for a telecom company and explain every feature in plain language.

Months 7 to 8: Specialisation and storytelling. Pick NLP, computer vision or forecasting. Learn Tableau or Power BI for storytelling. Your project: an end-to-end dashboard backed by a model, deployed so anyone can click it.

Months 9 to 10: Interview preparation. DSA basics for analysts, SQL interviews, case studies and ML theory. Build your resume around impact metrics, not course lists.

Freshers should target data analyst, business analyst and data science intern roles first; they hire in volume and convert internally. Working professionals should target analytics roles inside their own domain first, where domain knowledge is the unfair advantage.

The roadmap is honest about one thing: skills compound only with projects. One project per stage, published publicly, beats ten certificates sitting in a drive.