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 willingness to learn
- Comfort using command-line or terminal for package installation
- Experience working with spreadsheets (CSV/Excel)
Audience
- Data analysts and operations staff automating data tasks
- Analytical engineers seeking lightweight ETL scripting
- Professionals interested in practical Python-based data workflows
Testimonials (5)
The exercises we saw in the course were quite useful and applicable to my activities at work. My questions were answered and the examples shared are quite useful.
jocelin salas - BANXICO
Course - Test Automation with Selenium and Python
Machine Translated
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - TCMT
Course - Machine Learning with Python – 2 Days
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
The explaination