Course Curriculum
MODULE 1: Python Fundamentals (Weeks 1-2)
- Introduction to Programming Concepts
- Setting up Python Development Environment
- Variables, Data Types, and Type Conversion
- Arithmetic and Comparison Operators
- String Operations and Methods
- Input/Output Operations
- Conditional Statements (if, elif, else)
- Loops (for, while) and Loop Control
- Lists: Creation, Indexing, Slicing
- List Methods and List Comprehensions
Project: Build a calculator and a number guessing game
MODULE 2: Intermediate Python (Weeks 3-4)
- Functions: Definition, Parameters, Return Values
- *args and **kwargs
- Lambda Functions and Map/Filter/Reduce
- String Formatting and f-strings
- Understanding and Handling Syntax Errors
- Exception Handling (try, except, finally)
- Custom Exceptions
- File Handling (read, write, append)
- Working with CSV and JSON files
- Modules and Packages
Project: File-based contact management system
MODULE 3: Data Structures & OOP (Weeks 5-6)
- Tuples: Immutability and Use Cases
- Sets: Operations and Methods
- Dictionaries: Key-Value Pairs
- Nested Data Structures
- Object-Oriented Programming Concepts
- Classes and Objects
- Constructors and Instance Variables
- Methods: Instance, Class, and Static
- Inheritance and Polymorphism
- Encapsulation and Abstraction
Project: Object-oriented library management system
MODULE 4: Data Analysis with NumPy & Pandas (Weeks 7-8)
- Introduction to NumPy Arrays
- Array Operations and Broadcasting
- Array Indexing and Slicing
- Statistical Functions in NumPy
- Introduction to Pandas DataFrames
- Reading Data from Various Sources (CSV, Excel, SQL)
- Data Selection: loc, iloc, Boolean Indexing
- Data Cleaning: Missing Values, Duplicates
- Data Transformation: Apply, Map, GroupBy
- Merging, Joining, and Concatenating DataFrames
- Pivot Tables and Cross-tabulation
Project: Exploratory data analysis on real-world dataset
MODULE 5: Data Visualization (Week 9)
- Introduction to Matplotlib
- Line Plots, Bar Charts, Histograms
- Scatter Plots and Pie Charts
- Customizing Plots: Colors, Labels, Legends
- Subplots and Figure Layouts
- Introduction to Seaborn
- Statistical Visualizations
- Heatmaps and Correlation Matrices
- Interactive Visualizations with Plotly
Project: Create a comprehensive visualization dashboard
MODULE 6: Machine Learning Fundamentals (Weeks 10-11)
- Introduction to Machine Learning Concepts
- Supervised vs Unsupervised Learning
- Training, Validation, and Test Sets
- Introduction to Scikit-learn
- Linear Regression: Theory and Implementation
- Logistic Regression for Classification
- Decision Trees and Random Forests
- Visualizing Decision Trees
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1)
- Cross-Validation Techniques
- Feature Engineering and Selection
- Handling Imbalanced Datasets
Project: Build a classification model (e.g., spam detection, churn prediction)
MODULE 7: Advanced ML & Deep Learning Intro (Week 12)
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Clustering with K-Means
- Dimensionality Reduction with PCA
- Introduction to Neural Networks
- TensorFlow and Keras Basics
- Building a Simple Neural Network
- Model Training and Optimization
- Saving and Loading Models
- ML Pipelines with Scikit-learn
Final Project: End-to-end ML project with deployment
CAPSTONE PROJECT (Throughout Week 12)
Choose from one of these real-world projects:
- Option A: Customer Churn Prediction System
- Option B: House Price Prediction Model
- Option C: Image Classification with Neural Networks
- Option D: Sentiment Analysis on Product Reviews
Requirements
- Data collection and preprocessing
- Exploratory data analysis with visualizations
- Model training and evaluation
- Model optimization and tuning
- Documentation and presentation