Data analyst is the pragmatic on-ramp into data careers — hireable at fresher level, tool-driven, and portfolio-provable. The roadmap, in the order that compounds (≈4-5 months at an hour daily):
The skill ladder
- Excel/Sheets, seriously (2-3 weeks): lookups, pivots, cleaning, charts — still the daily language of Indian business data. Skipping it is fashionable and wrong.
- SQL — the core hiring skill (4-5 weeks): SELECT through JOINs, GROUP BY, subqueries, window functions. Most analyst interviews are substantially SQL interviews; practise on real schemas until queries feel like sentences.
- Python for analysis (4-6 weeks): pandas (cleaning, grouping, merging), matplotlib/seaborn basics — the escape hatch beyond spreadsheet scale. Your programming base makes this fast.
- A BI tool (2-3 weeks): Power BI (India-market default) or Tableau — dashboards, filters, publishing. This is where your work becomes visible to non-technical stakeholders — the actual job.
- Statistics literacy (ongoing): distributions, correlation-vs-causation, A/B basics — the thinking layer interviews probe with “so what does this number mean?”
The portfolio that interviews well
Three projects on GitHub + one live dashboard: (1) a public-dataset deep-dive (Indian data reads well — transport, cricket, e-commerce) with a written insights summary; (2) an end-to-end SQL case (raw → cleaned → queried → answered business questions); (3) a Power BI dashboard someone can click. Each must answer a question, not just display charts — “what did you find?” is the entire interview.
Honest market notes
Entry titles blur (analyst/MIS/reporting/business analyst) — read JDs by skills, not titles; fresher pay sits in normal fresher bands with growth following SQL depth and domain sense. The path also stacks cleanly toward data-engineering or the AI-adjacent skills ladder later — nothing here is wasted motion.