Quick Answer
For data science specifically, start with freeCodeCamp's Scientific Computing with Python, then Coursera's Google Data Analytics certificate (free to audit), then MIT's Introduction to Deep Learning. Each one builds on the last, so follow them in order. All three are free to access, and together they take you from Python basics to a working machine learning model.
The best free Python courses for data science are Kaggle's Python and Data Science tracks, fast.ai's Practical Deep Learning, and Google's Python Class. Start with Kaggle: it teaches Python inside the context of real datasets, which is exactly how data scientists use the language on the job. If you are weighing it against the deep learning route, Kaggle and fast.ai head to head shows how differently the two pitch at a beginner.
Python is used by 65.8% of data professionals, making it the most widely used language in data science (source: Stack Overflow Developer Survey 2024, survey.stackoverflow.co/2024).
What you actually need to learn for data science
Many data science courses teach you to run code without understanding it. The skill that matters is knowing what to do when your code doesn't work, which requires understanding Python fundamentals, not just copy-pasting pandas commands. A good data science learning path has three distinct phases: Python fundamentals (variables, functions, control flow, data structures), data manipulation and analysis (pandas, numpy, data cleaning, visualization with matplotlib/seaborn), and machine learning (scikit-learn, model evaluation, feature engineering). Don't rush the fundamentals. Shaky Python foundations make everything else harder.
#1: freeCodeCamp Scientific Computing with Python
This is the best free starting point for Python in a data science context. It covers Python fundamentals with a scientific computing angle: data structures, algorithms, and file handling. It's browser-based (no setup required), self-paced, and includes a verifiable certificate. The curriculum has been significantly updated in recent years and is now genuinely strong. Complete this before moving to any data-specific content: the Python foundations it builds will make everything else easier.
#2: Google Data Analytics Certificate on Coursera (free to audit)
This is a professional-grade introduction to data analytics. While it's not Python-first (it introduces R alongside Python), it's backed by Google and covers the complete data analyst workflow: data collection, cleaning, analysis, visualization, and communication. It's free to audit (without a certificate). If you want the certificate, Coursera charges a fee, but auditing gives you all the course content. This is one of the most recognized entry-level data credentials available and is regularly cited by hiring managers in data roles.
#3: freeCodeCamp Data Analysis with Python
After you have Python fundamentals, this course is the bridge to real data work. It covers numpy, pandas, matplotlib, and scipy: the core libraries for data analysis. The projects are practical and include working with real datasets. Combined with the Scientific Computing course, this gives you the technical vocabulary to handle most entry-level data analysis tasks.
#4: MIT Introduction to Deep Learning (free)
Once you're comfortable with Python and basic machine learning, MIT's deep learning course is an excellent free resource for going further. It covers neural networks, convolutional networks, sequence models, and reinforcement learning with a mix of theory and TensorFlow implementation. It's not beginner-friendly: complete the previous three courses first, but it's free, thorough, and from one of the world's best technical universities.
What's missing from all of these?
None of these courses teach you how to work with SQL, which is non-negotiable for data science roles. Almost all data science work starts in a database, not a CSV file. Add CS50 SQL or Khan Academy's SQL course to your learning plan. SQL is learnable in 2–4 weeks and will immediately make you more hireable as a data professional.
FAQ
Frequently Asked Questions
Do I need to know Python before taking a data science course?
No. Kaggle's Python course starts from scratch and takes most people about 5 hours to complete. You can go directly from Kaggle Python into Kaggle's Data Science and Machine Learning tracks.
Is Kaggle enough to learn Python for data science?
Kaggle covers the essentials: pandas, NumPy, data visualization, and basic machine learning with scikit-learn. For most entry-level data roles, this is a solid foundation. Supplement with real dataset projects to build your portfolio.
What Python libraries do data scientists actually use?
pandas (data manipulation), NumPy (numerical computing), matplotlib and seaborn (visualization), scikit-learn (machine learning), and Jupyter notebooks for interactive analysis. All of these are covered in free Kaggle and fast.ai courses.
How long does it take to learn Python for data science?
Most people reach a working level in 2-4 months of part-time study. Getting good at working with real datasets takes 6-12 months of practice.