Back to Data Scientist

Data Scientist — Step-by-Step Roadmap

Your journey, phase by phase

  1. Phase 1 · Class 10–12 Class 10–12: foundation subjects Build maths and logic habits; no single stream is mandatory.
    • Data science is open to students of any stream, but Maths and Computer Science/Informatics Practices in Class 11–12 give a faster start.
    • Learn basic programming (Python or C) and use free resources such as SWAYAM, NPTEL and official language documentation.
    • Statistics and Maths in Class 11–12 help, and Excel is a good first tool.
    • Practise typing, English reading and logical problem-solving; they matter in every technical interview.
    Streams
    Any (PCM helps)
    Start with
    Python or C, plus Excel
  2. Phase 2 · After Class 12 Degree route and admission Choose a degree that fits your budget and goals; skills matter as much as the degree.
    • Common routes into data science: B.Tech/B.E. (CSE, IT, ECE), BCA, BSc Computer Science/IT, and BSc in related subjects; JEE Main, state CETs and CUET-UG are the main admission exams.
    • Choose a college that offers labs, active coding clubs and a record of internships and campus placements.
    • Non-degree path: self-learning plus certifications and a strong GitHub portfolio can open entry-level roles, though many employers still prefer a degree.
    • Confirm AICTE and UGC approval of the college before paying fees.
    Degrees
    B.Tech, BCA, BSc CS/IT
    Exams
    JEE Main, CETs, CUET-UG
  3. Phase 3 · Year 1–2 Data fundamentals Excel, SQL, statistics and visualisation.
    • Learn Excel, SQL queries and joins, and a BI tool such as Power BI or Tableau.
    • Study descriptive and inferential statistics, probability and hypothesis testing.
    • Learn Python with Pandas and Matplotlib.
    • Complete small analyses on real datasets.
    Tools
    Excel, SQL, Power BI, Python
  4. Phase 4 · Year 2–3 Machine learning and experimentation Build predictive models and measure them.
    • Learn supervised and unsupervised ML, feature engineering and model evaluation.
    • Learn A/B testing, forecasting and basic causal thinking.
    • Learn big-data basics (Spark) and cloud data tools.
    • Write clear reports with charts and recommendations.
    Methods
    ML, forecasting, A/B testing
  5. Phase 5 · Year 3–4 Projects and domain skill Solve business problems end to end.
    • Build projects in finance, e-commerce, health or sports analytics and present insights.
    • Share notebooks and dashboards in a portfolio.
    • Participate in Kaggle competitions.
    • Practise telling a story with data to non-technical audiences.
    Output
    Portfolio of analyses and dashboards
  6. Phase 6 · Final year / after graduation Internship, portfolio and first job Turn skills into proof and apply through campuses and job portals.
    • Build 3–4 solid projects, host the code on GitHub and write short READMEs that explain the problem and your decisions.
    • Apply for internships in your second and third year; start-ups and remote internships often take beginners.
    • Typical first roles: data analyst, business analyst, junior data scientist.
    • Over time you can become a senior data scientist, ML engineer, analytics manager or data product lead.
    • Prepare for aptitude tests, technical interviews and HR rounds; practise mock interviews and communication.
    Entry roles
    data analyst, business analyst, junior data scientist

Verify on the official source

Exam dates, fees, paper patterns and eligibility rules are revised by the regulator from time to time. Treat this roadmap as your map, and confirm the current rules on the official site before you register or pay any fee.

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