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Duration 14 hours
Course Outline
Python Essentials for Data Operations
- Installing Python and configuring the development environment.
- Core language concepts: variables, data types, and control structures.
- Creating and executing simple Python scripts.
File Management: CSV and Excel
- Reading and writing CSV files using the csv module and Pandas.
- Processing Excel files with openpyxl/xlrd and Pandas.
- Practical exercises focused on automating file conversions.
Getting Started with Pandas
- DataFrame fundamentals: creation, indexing, selection, and filtering.
- Executing aggregation and grouping operations.
- Standard cleaning tasks: handling missing values, duplicates, and type conversions.
Introduction to Polars
- Polars concepts and performance metrics compared to Pandas.
- Basic DataFrame operations within Polars.
- Case studies: determining when to prefer Polars over Pandas.
Advanced Data Transformation (Intermediate Level)
- Complex joins, window functions, and pivot operations in Pandas.
- Optimized data processing patterns using Polars.
- Operation chaining and memory usage optimization.
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 notification systems.
Script Packaging and Best Practices
- Generating executables with PyInstaller or comparable tools.
- Project structuring, virtual environments, and dependency management.
- Basics of version control and workflow documentation.
Practical Mini-Project
- End-to-end task: 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 Directions
Requirements
- Foundational knowledge of programming concepts or a strong commitment to learning.
- Proficiency in using command-line or terminal interfaces for package installation.
- Familiarity with spreadsheet tools (CSV/Excel).
Target Audience
- Data analysts and operations personnel seeking to automate data tasks.
- Analytical engineers looking for lightweight ETL scripting solutions.
- Professionals interested in practical, Python-based data workflows.
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.
