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Duration 14 hours
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 conversions
Introduction to Pandas
- Essentials of DataFrames: creation, indexing, selection, and filtering
- Aggregation and grouping operations
- Standard cleaning operations: handling missing values, duplicates, and type conversions
Introduction to Polars
- Polars concepts and performance characteristics relative to Pandas
- Fundamental 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
- 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 comparable tools
- Project structuring, virtual environments, and dependency management
- Version control basics 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 improving based on peer feedback
Summary and Next Steps
Requirements
- Basic familiarity with programming concepts or a strong willingness to learn
- Confidence in using command-line or terminal environments for package installation
- Experience working with spreadsheets (CSV/Excel)
Audience
- Data analysts and operations staff looking to automate data tasks
- Analytical engineers seeking lightweight ETL scripting solutions
- Professionals interested in practical Python-based data workflows
Testimonials (3)
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
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Interesting knowledge
