AI and Data Scientist

From data to models you can defend.

The complete data-science path: math that matters, the Python stack, data wrangling and EDA, classical machine learning done honestly, deep learning, transformers and LLMs, and the portfolio that proves it.

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8 stages32 topics₹0 to follow
  1. 1

    Math That Matters

    Just enough math, deeply understood.

    3 weeks0/3
  2. 2

    Python & The PyData Stack

    Make Python your native tool for data.

    3–4 weeks0/4
  3. 3

    Data Wrangling & EDA

    Interrogate a dataset before modelling it.

    2–3 weeks0/4

    Build: EDA case study

    Pick a public dataset; deliver a notebook with cleaning, five insights and honest caveats.

  4. 4

    Statistics for Inference

    Reason under uncertainty like a scientist.

    2–3 weeks0/3
  5. 5

    Classical Machine Learning

    Model tabular data - still most industry ML.

    4–5 weeks0/7

    Build: End-to-end prediction model

    A churn or price model: EDA, features, model comparison, error analysis and a business-facing summary.

  6. 6

    Deep Learning

    Neural networks from intuition to practice.

    4–5 weeks0/4
  7. 7

    Transformers & LLMs

    The modern layer on top of the fundamentals.

    3–4 weeks0/4
  8. 8

    Portfolio & Proof

    Evidence that survives recruiter scrutiny.

    2 weeks0/3

  9. Final stop: prove it.

    Knowledge without proof is just a claim. Take the free AI and Data Scientist certification — a timed, scored assessment with a public verification link recruiters can check in one click.