Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Python Essentials for Data Workflows
- Installation of Python and configuration of the development environment.
- Core language syntax: variables, data types, and control structures.
- Development and execution of basic Python scripts.
Managing Files: CSV and Excel
- Importing and exporting CSV data using the csv module and Pandas.
- Interacting with Excel workbooks via openpyxl/xlrd and Pandas.
- Practical drills focused on automating file format conversions.
Fundamentals of Pandas
- Core DataFrame mechanics: creation, indexing, selection, and filtering.
- Executing aggregation and grouping operations.
- Standard cleaning techniques: handling missing values, duplicates, and type casting.
Introduction to Polars
- Understanding Polars architecture and its performance advantages over Pandas.
- Executing basic DataFrame operations within the Polars framework.
- Scenario analysis: determining when Polars is the superior choice to Pandas.
Advanced Data Transformation (Intermediate Level)
- Implementing complex joins, window functions, and pivot tables in Pandas.
- Adopting efficient data processing patterns using Polars.
- Chaining operations to optimize memory consumption.
Workflow Automation with Python
- Scripting repetitive data tasks and ETL steps for automation.
- Configuring script execution via OS or task schedulers.
- Implementing logging, error handling, and notification systems.
Script Packaging and Best Practices
- Generating executable files using PyInstaller or comparable tools.
- Managing project structure, virtual environments, and dependencies.
- Basic version control principles and documentation of workflows.
Practical Mini-Project
- End-to-end exercise: ingesting raw data, cleaning and transforming it, and generating outputs.
- Automating the complete workflow and packaging it as a runnable script or executable.
- Peer review and iterative improvements based on feedback.
Conclusion and Path Forward
Requirements
- Familiarity with fundamental programming concepts or a strong motivation to learn.
- Proficiency in using command-line or terminal interfaces for package installation.
- Previous experience working with spreadsheet formats (CSV/Excel).
Target Audience
- Data analysts and operations staff looking to automate data-centric tasks.
- Analytical engineers seeking lightweight ETL scripting solutions.
- Professionals eager to adopt 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.
