Course Outline

Module 1: Introduction to Data Munging

• Overview of data munging and preprocessing
• Importance of data munging in machine learning
• Setting up the data munging environment
• Case Study: Data Munging in a Real-World Machine Learning Project

Module 2: Data Cleaning Techniques

• Identifying and handling missing data
• Detecting and treating outliers
• Data normalization and scaling
• Case Study: Cleaning a Messy Dataset for Analysis

Module 3: Data Transformation and Encoding

• Data transformation techniques
• Encoding categorical variables
• Creating dummy variables
• Case Study: Transforming Data for a Predictive Model

Module 4: Feature Engineering and Selection

• Introduction to feature engineering
• Creating new features from existing data
• Feature selection techniques
• Case Study: Enhancing Model Performance through Feature Engineering

Module 5: Handling Time Series Data

• Time series data preprocessing
• Handling date and time features
• Resampling and aggregating time series data
• Case Study: Forecasting Sales with Time Series Analysis

Module 6: Data Augmentation Techniques

• Data augmentation for imbalanced datasets
• Synthetic data generation
• Applying data augmentation in machine learning
• Case Study: Improving Model Robustness with Data Augmentation

Module 7: Integrating Data Munging with Machine Learning

• Building machine learning pipelines
• Integrating data preprocessing with model training
• Using libraries like scikit-learn and pandas
• Case Study: End-to-End Machine Learning Pipeline Implementation

Module 8: Performance Optimization

• Optimizing data preprocessing workflows
• Profiling and debugging data munging code
• Parallel processing and performance tuning
• Case Study: Performance Tuning in a Data Munging Workflow

Module 9: Practical Data Munging Projects

• Real-world data munging projects
• Applying learned techniques to projects
• Project presentations and feedback
• Case Study: Presentation of Project Outcomes and Learnings

Module 10: Troubleshooting and Best Practices

• Common data munging issues and solutions
• Best practices for data preprocessing
• Future trends in data munging for machine learning
• Case Study: Addressing Data Munging Challenges in Industry Settings