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Course Outline
Python Fundamentals for Data Tasks
- Installing Python and setting up the development environment.
- Language fundamentals: variables, data types, control structures.
- Writing and running simple Python scripts.
File Handling: CSV and Excel
- Reading and writing CSV files using the csv module and Pandas.
- Working with Excel files using openpyxl/xlrd and Pandas.
- Practical exercises: automating file conversions.
Introduction to Pandas
- DataFrame basics: creation, indexing, selection, and filtering.
- Aggregation and grouping operations.
- Common cleaning operations: missing values, duplicates, and type conversions.
Introduction to Polars
- Polars concepts and performance characteristics compared to Pandas.
- Basic DataFrame operations in Polars.
- Use-case example: when to choose Polars over Pandas.
Advanced Data Transformation (Intermediate)
- Complex joins, window functions, and pivot operations in Pandas.
- Efficient data processing patterns with Polars.
- Chaining operations and optimizing memory usage.
Process Automation with Python
- Writing scripts to automate repetitive data tasks and ETL steps.
- Scheduling scripts with OS schedulers or task schedulers.
- Logging, error handling, and notifications.
Packaging Scripts and Best Practices
- Creating executables with PyInstaller or similar tools.
- Project structuring, virtual environments, and dependency management.
- Version control basics and documenting workflows.
Hands-on Mini-Project
- End-to-end task: read raw files, clean and transform data, produce outputs.
- Automate the workflow and package as a runnable script or executable.
- Review and improvements based on peer feedback.
Summary and Next Steps
Requirements
- Basic familiarity with programming concepts or a willingness to learn.
- Comfort with using the command line or terminal for package installation.
- Experience working with spreadsheets (CSV/Excel).
Audience
- Data analysts and operations staff automating data tasks.
- Analytical engineers looking for lightweight ETL scripting solutions.
- Professionals interested in practical Python-based data workflows.
14 Hours
Testimonials (2)
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.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Examples/exercices perfectly adapted to our domain
