Week 1: Python Essentials

• Introduction to Python
• Variables, Data Types, and Operators
• Control Flow: If-Else, Loops
• Functions, Lambda Functions
• Understanding Python Modules and Packages

Week 2: Python Data Structures

• Lists, Tuples, Sets, and Dictionaries
• List Comprehensions
• Iterators and Generators
• Error and Exception Handling

Week 3: Data Manipulation with Pandas

• Introduction to Pandas
• Working with DataFrames and Series
• Reading and Writing CSV/Excel/JSON Files
• Data Cleaning: Handling Missing Data, Removing Duplicates
• Data Aggregation and Grouping

Week 4: Data Visualization

• Introduction to Matplotlib
• Plotting Line Graphs, Bar Charts, and Pie Charts
• Data Visualization with Seaborn
• Customizing Plots: Titles, Legends, and Annotations
• Heatmaps and Pair Plots

Month 2: Statistics and Data Science Foundations

Week 5: Statistics for Data Science

• Descriptive Statistics: Mean, Median, Mode
• Variance, Standard Deviation, Percentiles
• Probability Basics and Distributions
• Correlation and Covariance

Week 6: NumPy for Numerical Computations

• NumPy Arrays: Creation, Indexing, and Slicing
• Array Operations: Arithmetic, Logical, and Statistical Functions
• NumPy Broadcasting
• Working with Random Data and Distributions

Week 7: Data Science Workflow

• Understanding the CRISP-DM Framework
• Data Preparation Techniques
• Exploratory Data Analysis (EDA)
• Feature Engineering Basics

Week 8: Introduction to Machine Learning

• Machine Learning Concepts: Types and Applications
• Supervised vs Unsupervised Learning
• Key ML Metrics: Accuracy, Precision, Recall
• Introduction to Scikit-Learn

Month 3: Machine Learning Models

Week 9: Regression Models

• Linear Regression
• Polynomial Regression
• R-Squared and Adjusted R-Squared
• Cross-Validation and Hyperparameter Tuning

Week 10: Classification Models

• Logistic Regression
• Decision Trees
• Random Forest Classifier
• Evaluation Metrics: Confusion Matrix, ROC Curve

Week 11: Clustering Algorithms

• K-Means Clustering
• Hierarchical Clustering
• Choosing the Number of Clusters

Week 12: Advanced Machine Learning Concepts

• Dimensionality Reduction with PCA
• Ensemble Techniques: Bagging, Boosting
• Introduction to XGBoost and LightGBM

Month 4: Deep Learning & AI Applications

Week 13: Neural Networks

• Introduction to Neural Networks
• Forward and Backward Propagation
• Activation Functions
• Cost Functions and Gradient Descent

Week 14: Deep Learning with TensorFlow

• Introduction to TensorFlow
• Building and Training Models
• Optimizers: SGD, Adam
• Evaluating Deep Learning Models

Week 15: Natural Language Processing (NLP)

• Text Preprocessing: Tokenization, Lemmatization
• Bag of Words and TF-IDF
• Sentiment Analysis
• Introduction to Word Embeddings

Week 16: Capstone Project

• Choose a Real-World Problem
• Define the Problem Statement
• Apply EDA, Feature Engineering, and ML/DL Models
• Present Findings and Solutions