Python + Data Science + Machine Learning Combo
Course Duration: 20–24 Weeks (Flexible) | Mode: Offline/Online
Goal: Students become job-ready Data Analysts, Data Scientists, or ML Engineers.
MODULE 1 — Python Programming Foundations (Weeks 1–4)
Python Basics
- Introduction to Python & setup (Anaconda, VS Code, Jupyter)
- Variables, datatypes, input/output
- Operators & Expressions
- Conditional statements (if/elif/else)
Loops, Functions & Data Structures
- Loops (for, while), break/continue
- Functions, arguments, return values
- Lists, Tuples, Sets, Dictionaries
- List comprehensions
File Handling, Modules, OOP
- File read/write
- Python modules & packages
- OOP: classes, objects, methods, inheritance
- Exception handling
Python for Data & Automation
- Lambda, map, filter, reduce
- Iterators, generators
- Working with APIs
- Virtual environments
MODULE 2 — Data Analysis & Visualization (Weeks 5–8)
NumPy Foundations
- Arrays, indexing, slicing
- Vectorization
- Mathematical & statistical operations
Pandas for Data Analysis
- DataFrames, Series
- Import/export CSV, Excel, SQL
- Handling missing data
- Merging, grouping, pivot tables
Data Visualization
- Histograms, Bar, Line charts
- Heatmaps, Pairplots
- Interactive dashboards
Exploratory Data Analysis (EDA)
- Descriptive statistics
- Outliers, distributions
- Correlation
- Insights reporting
MODULE 3 — SQL + Databases (Weeks 9–10)
SQL Basics
- SELECT, WHERE, ORDER BY
- JOINs, GROUP BY, HAVING
- Subqueries
SQL for Analytics
- Window functions
- CTEs
- Indexing, optimization
- Integrating SQL with Python
MODULE 4 — Statistics & Mathematics for ML (Weeks 11–12)
Statistics Essentials
- Descriptive vs Inferential statistics
- Probability & distributions
- Hypothesis testing (t-test, chi-square)
- Confidence intervals
Math for Machine Learning
- Linear algebra basics
- Vectors, matrices
- Calculus for ML (derivatives)
- Gradient descent intuition
MODULE 5 — Machine Learning Foundations (Weeks 13–16)
Introduction to ML
- ML workflow
- Data preprocessing
- Train-test split
- Feature scaling & encoding
Supervised Learning (Regression Models)
- Linear Regression
- Polynomial Regression
- Regularization (Lasso, Ridge)
Supervised Learning (Classification Models)
- Logistic Regression
- Decision Trees
- Random Forest
- SVM
- Model evaluation metrics
Ensemble & Boosting Methods
- Gradient Boosting
- XGBoost, LightGBM, CatBoost
MODULE 6 — Unsupervised Learning & Feature Engineering (Weeks 17–18)
Unsupervised Learning
- K-Means clustering
- Hierarchical clustering
- PCA (Dimensionality reduction)
Feature Engineering
- Encoding techniques
- Feature selection
- Handling imbalance (SMOTE)
MODULE 7 — Deep Learning Foundations (Weeks 19–20)
Neural Networks Intro
- Perceptron & ANN
- Activation functions
- Loss functions
- Training & backpropagation
Deep Learning Models
- CNN basics (optional)
- NLP basics (tokenization, embeddings)
- Train a small DL model
MODULE 7 — Deep Learning Foundations (Weeks 19–20)
Neural Networks Intro
- Perceptron & ANN
- Activation functions
- Loss functions
- Training & backpropagation
Deep Learning Models
- CNN basics (optional)
- NLP basics (tokenization, embeddings)
- Train a small DL model
MODULE 8 — Practical & Interview Preparation (Week 20-24)
- Practical
- Interview Preparation