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Duration 14 hours
Course Outline
Python Foundations for Data Workflows
- Installation of Python and configuration of the development environment.
- Core language elements: variables, data types, and control structures.
- Development and execution of basic Python scripts.
Managing Files: CSV and Excel
- Ingesting and exporting CSV files using the csv module and Pandas.
- Processing Excel files utilizing openpyxl/xlrd and Pandas.
- Practical application: automating file format conversions.
Familiarizing with Pandas
- Essentials of DataFrames: creation, indexing, selection, and filtering.
- Executing aggregation and grouping operations.
- Standard cleaning tasks: addressing missing values, duplicates, and data type conversions.
Familiarizing with Polars
- Key concepts and performance metrics of Polars in comparison to Pandas.
- Executing fundamental DataFrame operations within Polars.
- Scenario-based analysis: determining when to prefer Polars over Pandas.
Sophisticated Data Transformation (Intermediate Level)
- Executing complex joins, window functions, and pivot operations in Pandas.
- Implementing efficient data processing patterns with Polars.
- Chaining operations and strategies for optimizing memory consumption.
Automating Processes with Python
- Scripting to automate repetitive data tasks and ETL pipeline steps.
- Configuring script execution via OS or task schedulers.
- Implementing logging, error handling mechanisms, and notification systems.
Script Packaging and Industry Best Practices
- Generating executables using PyInstaller or comparable tools.
- Organizing project structure, managing virtual environments, and handling dependencies.
- Basics of version control and documenting data workflows.
Practical Mini-Project
- Comprehensive task: ingesting raw files, cleansing and transforming data, and generating final outputs.
- Automating the entire workflow and packaging it as a runnable script or executable.
- Evaluating and refining solutions based on peer feedback.
Recap and Future Directions
Requirements
- A foundational understanding of programming concepts or a strong motivation to learn.
- Proficiency in using the command-line interface or terminal for installing packages.
- Practical experience managing spreadsheet formats such as CSV and Excel.
Target Audience
- Data analysts and operational staff looking to automate data-centric tasks.
- Analytical engineers interested in lightweight ETL scripting solutions.
- Professionals seeking to integrate practical, Python-based data workflows into their routine.
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.
