Course Outline
Core Python Skills for Data Processing
- Python installation and development environment setup
- Language essentials: variables, data types, and control flow
- Developing and executing basic Python scripts
File Management: CSV and Excel
- Ingesting and exporting CSV files via the csv module and Pandas
- Handling Excel files with openpyxl/xlrd and Pandas
- Practical drills: automating file format conversions
Fundamentals of Pandas
- DataFrame essentials: creation, indexing, selection, and filtering
- Executing aggregation and grouping tasks
- Standard cleaning operations: handling missing values, duplicates, and type casting
Introduction to Polars
- Polars concepts and performance benchmarks against Pandas
- Essential DataFrame operations within Polars
- Scenario analysis: determining when to prefer Polars over Pandas
Advanced Data Transformation (Intermediate Level)
- Complex joins, window functions, and pivot tables in Pandas
- Optimized data processing patterns using Polars
- Operation chaining and memory usage optimization
Python-Driven Process Automation
- Scripting repetitive data tasks and ETL steps for automation
- Scheduling scripts via OS or task schedulers
- Implementing logging, error handling, and notification systems
Script Packaging and Best Practices
- Generating executables using PyInstaller or comparable tools
- Project structure, virtual environments, and dependency management
- Version control fundamentals and workflow documentation
Practical Mini-Project
- End-to-end workflow: ingest raw files, clean and transform data, and generate outputs
- Automating the workflow and packaging it as a runnable script or executable
- Review and refinement based on peer feedback
Wrap-up and Future Pathways
Requirements
- Basic understanding of programming principles or a readiness to learn
- Confidence in using command-line or terminal interfaces for package installation
- Experience handling spreadsheet formats (CSV/Excel)
Target Audience
- Data analysts and operations teams seeking to automate data processes
- Analytics engineers looking for lightweight ETL scripting solutions
- Professionals aiming to adopt 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
