Module 1: Introduction to Data Science

• Overview of Data Science
• Importance of Data Science in various industries
• Basic concepts and terminologies

Module 2: Python Programming for Data Science

• Introduction to Python
• Basic data types and structures
• Control flow and functions

Module 3: Data Manipulation and Analysis with Pandas

• Introduction to Pandas
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)

Module 4: Introduction to NumPy and Matplotlib

• Basics of NumPy for numerical computing
• Data visualization using Matplotlib

Module 5: Introduction to Statistics

• Descriptive statistics
• Inferential statistics
• Probability distributions

Module 6: Linear Regression

• Understanding the concept of regression
• Simple linear regression
• Multiple linear regression
• Model evaluation metrics

Module 7: Decision Trees and Random Forest

• Introduction to decision trees
• Ensemble learning and random forests
• Hyperparameter tuning for better performance

Module 8: Classification Algorithms

• Logistic Regression
• Support Vector Machines (SVM)
• Model evaluation for classification

Module 9: Clustering with K-Means

• Unsupervised learning and clustering
• K-Means algorithm
• Evaluating clustering results

Module 10: Time Series Analysis

• Basics of time series data
• Time series visualization
• Forecasting using time series models

Module 11: Feature Engineering and Dimensionality Reduction

• Importance of feature engineering
• Techniques for dimensionality reduction
• Improving model performance through feature engineering

Module 12: Introduction to XGBoost

• Understanding gradient boosting
• XGBoost algorithm
• Tuning hyperparameters for XGBoost

Module 13: Advanced Machine Learning Techniques

• Introduction to neural networks
• Deep learning basics
• Transfer learning and fine-tuning

Module 14: Model Deployment and Productionization

• Deploying models using Flask or FastAPI
• Containerization with Docker
• Introduction to cloud services for model deployment

Module 15: Real-world Data Science Projects

• Working on real-world datasets
• End-to-end project development
• Best practices and project documentation

Module 16: Ethical Considerations in Data Science

• Privacy and security
• Bias and fairness in machine learning
• Responsible AI practices