Getting Started with Python for Data Science

This course introduces Python programming through the lens of data science. You will explore and summarize real datasets, sort and filter rows to find relevant information, and transform raw data into analytics-ready formats. The modules walk you through exploring data with Python, sorting and filtering rows, cleaning and transforming columns, and taking next steps in your learning path.

Codecademy designed this course for absolute beginners. No prior programming experience is assumed. You write code directly in the browser and receive immediate feedback. The focus stays on practical tasks: loading data, answering questions with filters, and reshaping tables for analysis. By the end, you can clean a dataset and prepare it for a basic report or dashboard.

The course takes about seven hours to complete. It is self-paced, so you can spread those hours across evenings or weekends. A free certificate is available upon finishing all modules.

This course does not cover machine learning models, statistical testing, or advanced visualisation libraries. Learners who want to build predictive models or work with large-scale databases should look elsewhere. The freeCodeCamp Data Science with Python course offers 300 hours of deeper coverage, including pandas and matplotlib, at no cost. Those seeking a university-backed credential may prefer the Google Data Analytics Professional Certificate on Coursera, which runs for six months and carries a government-verified certificate.

If you are comfortable with the basics and want to move into machine learning, the Udemy Machine Learning A-Z: AI, Python & R course provides 43 hours of model-building practice for ₹599. All three alternatives are linked above for comparison.

This course is a solid entry point if you want to start writing Python code on real datasets without paying upfront. The hands-on format and immediate feedback make it easy to stay motivated through the first week.