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 Fundamentals for Data Tasks
- Installing Python and configuring the development environment
- Core language concepts: variables, data types, and control structures
- Developing and executing basic Python scripts
File Handling: CSV and Excel
- Reading and writing CSV files via the csv module and Pandas
- Managing Excel files using openpyxl/xlrd and Pandas
- Practical exercises: automating file conversion processes
Introduction to Pandas
- DataFrame fundamentals: creation, indexing, selection, and filtering
- Performing aggregation and grouping operations
- Standard cleaning tasks: addressing missing values, duplicates, and type conversions
Introduction to Polars
- Understanding Polars concepts and comparing performance characteristics with Pandas
- Executing basic DataFrame operations in Polars
- Case study: determining when to prefer Polars over Pandas
Advanced Data Transformation (Intermediate)
- Implementing complex joins, window functions, and pivot operations in Pandas
- Adopting efficient data processing patterns with Polars
- Chaining operations and optimizing memory utilization
Process Automation with Python
- Creating scripts to automate repetitive data tasks and ETL steps
- Scheduling scripts using OS or task schedulers
- Implementing logging, error handling, and notification systems
Packaging Scripts and Best Practices
- Building executables using PyInstaller or similar tools
- Organizing projects, managing virtual environments, and handling dependencies
- Basics of version control and documenting workflows
Hands-on Mini-Project
- End-to-end workflow: ingest raw files, clean and transform data, and generate outputs
- Automate the workflow and package it as a runnable script or executable
- Conduct reviews and apply improvements based on peer feedback
Summary and Next Steps
Requirements
- Basic knowledge of programming concepts or a strong motivation to learn
- Familiarity with using command-line or terminal interfaces for installing packages
- Experience handling spreadsheets (CSV/Excel)
Target Audience
- Data analysts and operations staff looking to automate data tasks
- Analytics engineers interested in lightweight ETL scripting
- Professionals seeking 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.
