Course Outline

Module 1: Advanced pandas Techniques

• Advanced DataFrame operations
• MultiIndex and hierarchical indexing
• Working with time series data
• Case Study: Application of hierarchical indexing to analyze multi-level sales data.

Module 2: Advanced NumPy Techniques

• Advanced array operations
• Broadcasting and vectorization
• Handling large arrays efficiently
• Case Study: Optimizing financial calculations with large NumPy arrays.

Module 3: Data Cleaning and Preprocessing

• Advanced data cleaning techniques
• Handling missing data
• Data normalization and scaling
• Case Study: Cleaning and preparing survey data for analysis.

Module 4: Data Transformation and Reshaping

• Complex data transformation techniques
• Reshaping and pivoting data
• Working with categorical data
• Case Study: Transforming customer data for market segmentation analysis.

Module 5: Performance Optimization

• Optimizing data processing workflows
• Profiling and debugging code
• Parallel processing with Dask
• Case Study: Speeding up ETL processes in a large-scale data pipeline.

Module 6: Working with Unstructured Data

• Handling text data
• Working with JSON and XML data
• Natural Language Processing (NLP) basics
• Case Study: Extracting insights from customer reviews using NLP.

Module 7: Custom Data Functions and Pipelines

• Developing custom data transformation functions
• Creating reusable data processing pipelines
• Using pipeline libraries like sklearn-pipeline
• Case Study: Automating data transformation workflows for an e-commerce platform.

Module 8: Integrating Python with Other Tools

• Integrating Python with SQL databases
• Using Python with big data tools like Spark
• Interfacing with APIs and web services
• Case Study: Extracting and processing data from a web API to enrich a business dataset.

Module 9: Practical Data Projects

• Real-world data manipulation projects
• Applying advanced techniques to projects
• Project presentations and feedback
• Case Study: Building a comprehensive data dashboard for financial reporting.

Module 10: Troubleshooting and Best Practices

• Common issues and solutions
• Best practices for advanced data manipulation
• Future trends in data manipulation with Python
• Case Study: Implementing best practices to resolve bottlenecks in a production environment.