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Duration 14 hours
Course Outline
Python Foundations for Data Workflows
- Setting up the development environment and installing Python
- Core language concepts: variables, data types, and control structures
- Developing and executing basic Python scripts
Managing Files: CSV and Excel
- Loading and saving CSV files via the csv module and Pandas
- Handling Excel files using openpyxl/xlrd alongside Pandas
- Practical exercises: automating file format conversions
Getting Started with Pandas
- Essential DataFrame operations: creation, indexing, selection, and filtering
- Performing aggregation and grouping tasks
- Standard cleaning procedures: addressing missing values, duplicates, and type conversions
Getting Started with Polars
- Understanding Polars concepts and performance advantages relative to Pandas
- Executing basic DataFrame operations within Polars
- Scenario-based examples: determining when Polars is preferable to Pandas
Advanced Data Transformation (Intermediate Level)
- Executing complex joins, window functions, and pivot operations in Pandas
- Implementing efficient data processing patterns with Polars
- Chaining operations and optimizing memory utilization
Automating Processes with Python
- Scripting to automate repetitive data tasks and ETL steps
- Scheduling scripts using operating system schedulers or task schedulers
- Implementing logging, error handling, and notification systems
Script Packaging and Best Practices
- Generating executables using PyInstaller or comparable tools
- Structuring projects, managing virtual environments, and handling dependencies
- Basics of version control and documenting workflows
Practical Mini-Project
- End-to-end workflow: ingesting raw files, cleaning and transforming data, and generating outputs
- Automating the entire workflow and packaging it as a runnable script or executable
- Reviewing work and implementing improvements based on peer feedback
Wrap-Up and Future Directions
Requirements
- Fundamental understanding of programming concepts or a strong desire to learn
- Confidence in using command-line interfaces or terminals for installing packages
- Experience handling spreadsheet data (CSV/Excel)
Target Audience
- Data analysts and operations professionals looking to automate data tasks
- Engineers in analytical roles seeking lightweight ETL scripting solutions
- Professionals interested in implementing practical, Python-driven data workflows
Testimonials (2)
everything was perfect
Florin Vrincianu
Course - Python Programming Fundamentals
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
