Data science roadmap after first-year engineering (India edition)

In brief: Most first-year students jump to advanced AI courses too early. Foundations still decide who gets internships.

Year one priorities: Python fluency, Excel/Sheets discipline, basic statistics intuition, and one public analysis of a real dataset with a clear question.

Year two: SQL, pandas, visualization, and a domain story (campus ops, sports, climate, ecommerce). Ship notebooks that a non-engineer can understand.

Only then specialize: ML, NLP, or analytics engineering. Employers hire the narrative and the reliability of your pipeline as much as the model choice.

Why it matters

A sequenced roadmap prevents burnout and creates internship-ready artifacts sooner.

What to watch next

• Kaggle-style projects with clean READMEs
• SQL interview drills
• mentor feedback on storytelling

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