AI & Data Science

Statistics, modelling and the experimental discipline that separates analysis from guessing.

1. Step 01

   Maths foundations

   Linear algebra, calculus intuition, probability distributions and hypothesis testing.

   StatisticsLinear Algebra

2. Step 02

   Python for data

   NumPy, pandas, plotting and reproducible notebooks that someone else can rerun.

   pandasNumPy

3. Step 03

   Data wrangling

   Joins in SQL, missing data, leakage, sampling bias and feature engineering.

   SQLFeature Engineering

4. Step 04

   Classical ML

   Regression, trees, gradient boosting, cross-validation and honest metric selection.

   scikit-learnXGBoost

5. Step 05

   Deep learning

   Neural nets, training loops, transfer learning, transformers and fine-tuning.

   PyTorchTransformers

6. Step 06

   Experimentation

   A/B tests, power analysis, causal pitfalls and communicating uncertainty to non-technical readers.

   A/B TestingCausal Inference

7. Step 07

   Deployment

   Model serving, batch vs real-time inference, monitoring drift and retraining triggers.

   MLOpsMonitoring