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Syllabus — Module-wise Breakdown

In-depth module-by-module curriculum — from Python basics to deployment and capstone projects.

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Syllabus

Module 1 — Foundations & Python

Get comfortable with Python, data tools and EDA

Python basics, control flow, functions, OOP
Jupyter notebooks, virtual environments, Git basics
NumPy, Pandas for data wrangling
Exploratory Data Analysis, data cleaning, Matplotlib/Seaborn

Module 2 — Statistics & SQL

Descriptive & inferential statistics, probability
Hypothesis testing, confidence intervals
SQL for analytics, joins, aggregations, window functions

Module 3 — Machine Learning

Supervised learning: regression, classification
Tree-based models, ensembling, model tuning
Unsupervised: clustering, PCA, dimensionality reduction

Module 4 — Deep Learning & Deployment

Neural networks, CNNs, RNNs basics
Model deployment (Flask, Docker), intro to MLOps
Capstone project: full real-world implementation

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