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Duration 14 hours
Course Outline
Python Essentials for Data Workflows
- Installing Python and configuring the development environment.
- Core language concepts: variables, data types, and control flow structures.
- Authoring and executing basic Python scripts.
File Management: CSV and Excel
- Reading and writing CSV files utilizing the csv module and Pandas.
- Processing Excel files with openpyxl, xlrd, and Pandas.
- Practical exercises focused on automating file format conversions.
Getting Started with Pandas
- DataFrame fundamentals: creation, indexing, selection, and filtering techniques.
- Performing aggregation and grouping operations.
- Addressing common cleaning challenges: missing values, duplicates, and data type conversion.
Introduction to Polars
- Core Polars concepts and a performance comparison against Pandas.
- Executing basic DataFrame operations within the Polars ecosystem.
- Case study analysis: determining when to prioritize Polars over Pandas.
Advanced Data Transformation (Intermediate Level)
- Implementing complex joins, window functions, and pivot tables in Pandas.
- Adopting efficient data processing patterns using Polars.
- Optimizing memory usage through operation chaining and refinement.
Workflow Automation via Python
- Developing scripts to automate repetitive data tasks and ETL procedures.
- Scheduling scripts using OS or task schedulers.
- Implementing logging, error handling, and notification systems.
Script Packaging and Professional Best Practices
- Generating standalone executables with PyInstaller or equivalent tools.
- Structuring projects, managing virtual environments, and controlling dependencies.
- Foundations of version control and documenting technical workflows.
Capstone Mini-Project
- End-to-end exercise: ingesting raw files, cleaning and transforming data, and generating final outputs.
- Automating the entire workflow and packaging it as a runnable script or executable.
- Evaluating results and applying improvements based on peer review.
Conclusion and Future Roadmap
Requirements
- A foundational understanding of programming concepts or a strong motivation to acquire new skills.
- Proficiency in using the command line or terminal for package management.
- Prior experience working with spreadsheet formats such as CSV or Excel.
Target Audience
- Data analysts and operations personnel looking to automate their data workflows.
- Engineering professionals seeking lightweight ETL scripting capabilities.
- Professionals aiming to implement practical, Python-driven data pipelines.
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.
