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Course Outline
Python Fundamentals for Data Tasks
- Installing Python and configuring the development environment.
- Core language concepts: variables, data types, and control structures.
- Writing and executing simple Python scripts.
File Handling: CSV and Excel
- Reading and writing CSV files using the csv module and Pandas.
- Managing Excel files with openpyxl/xlrd and Pandas.
- Practical exercises: automating file conversion processes.
Introduction to Pandas
- DataFrame essentials: creation, indexing, selection, and filtering.
- Executing aggregation and grouping operations.
- Common cleaning tasks: handling missing values, duplicates, and type conversions.
Introduction to Polars
- Understanding Polars concepts and performance characteristics relative to Pandas.
- Performing basic DataFrame operations in Polars.
- Use-case example: determining when to select Polars over Pandas.
Advanced Data Transformation (Intermediate)
- Complex joins, window functions, and pivot operations in Pandas.
- Implementing efficient data processing patterns with Polars.
- Chaining operations and optimizing memory usage.
Process Automation with Python
- Developing scripts to automate repetitive data tasks and ETL steps.
- Scheduling scripts using OS schedulers or task schedulers.
- Implementing logging, error handling, and notifications.
Packaging Scripts and Best Practices
- Creating executables with PyInstaller or similar tools.
- Structuring projects, managing virtual environments, and handling dependencies.
- Basics of version control and documenting workflows.
Hands-on Mini-Project
- End-to-end task: reading raw files, cleaning and transforming data, and generating outputs.
- Automating the workflow and packaging it as a runnable script or executable.
- Reviewing and refining work based on peer feedback.
Summary and Next Steps
Requirements
- Fundamental knowledge of programming concepts or a strong willingness to learn.
- Proficiency in using the command line or terminal for package installation.
- Practical experience working with spreadsheets (CSV/Excel).
Target Audience
- Data analysts and operations staff seeking to automate data tasks.
- Analytical engineers looking for lightweight ETL scripting solutions.
- Professionals interested in practical, Python-based data workflows.
14 Hours
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.