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Duration 14 hours
Course Outline
Foundations of Python for Data Workflows
- Installing Python and configuring the development environment.
- Core language elements: variables, data types, and control structures.
- Developing and executing basic Python scripts.
File Management: CSV and Excel
- Reading and writing CSV files using the csv module and Pandas.
- Handling Excel files with openpyxl/xlrd and Pandas.
- Practical exercises focused on automating file conversions.
Getting Started with Pandas
- Essential DataFrame concepts: creation, indexing, selection, and filtering.
- Performing aggregation and grouping operations.
- Standard cleaning techniques: addressing missing values, duplicates, and type conversions.
Introduction to Polars
- Understanding Polars concepts and its performance advantages over Pandas.
- Executing basic DataFrame operations within the Polars framework.
- Use-case scenarios: determining when to prefer Polars over Pandas.
Advanced Data Transformation (Intermediate)
- Complex operations in Pandas, including joins, window functions, and pivots.
- Implementing efficient data processing patterns with Polars.
- Optimizing memory usage through chained operations.
Automating Processes with Python
- Scripting repetitive data tasks and ETL steps for automation.
- Scheduling scripts using OS schedulers or task schedulers.
- Implementing logging, error handling, and notification systems.
Script Packaging and Best Practices
- Creating standalone executables using PyInstaller or similar tools.
- Managing project structure, virtual environments, and dependencies.
- Basics of version control and documenting data 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.
- Peer review and iterative improvements based on feedback.
Wrap-Up and Future Directions
Requirements
- A foundational understanding of programming concepts or a strong willingness to learn.
- Confidence in using the command line or terminal for installing packages.
- Proficiency in working with spreadsheets (CSV/Excel).
Audience
- Data analysts and operations staff aiming to automate data-related tasks.
- Analytical engineers seeking lightweight ETL scripting solutions.
- Professionals interested in implementing practical Python-based 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.
