You can learn data science completely free, but “free course with certificate” usually means one of two things: the certificate is genuinely free (Kaggle, freeCodeCamp, Great Learning), or the lessons are free and the certificate costs money (most Coursera and edX courses). This list separates the two so you know what you are signing up for.
Every link below was checked on September 25, 2026. Interested in AI specifically? See the best free AI courses online and Anthropic’s 22 free Claude courses.
Courses With a Genuinely Free Certificate
| Course | Provider | Covers |
|---|---|---|
| Python Certification | freeCodeCamp | Python from scratch through data structures and algorithms, with projects |
| Relational Databases Certification | freeCodeCamp | SQL, PostgreSQL and Bash, built in a real terminal |
| Data Cleaning | Kaggle Learn | Missing values, scaling, parsing dates, encodings |
| Intro to Deep Learning | Kaggle Learn | Neural networks with Keras, hands-on in notebooks |
| Data Science with Python | Great Learning Academy | NumPy, pandas and visualization basics |
| Business Analytics with Excel | Simplilearn SkillUp | Excel for data analysis |
| Machine Learning Algorithms | Simplilearn SkillUp | Core ML algorithms, beginner level |
Kaggle Learn is the best place to start if you want to write real code quickly. Every course runs in a browser notebook with Kaggle’s free compute, and each gives a certificate on completion. Kaggle also has free courses on Python, pandas, data visualization, intro and intermediate machine learning, and SQL.
Free to Learn, Paid Certificate
These are the big-name university and industry courses. On edX, you can audit HarvardX courses for free and pay for a verified certificate. On Coursera, free access varies by course (audit or preview mode), certificates require a paid enrollment or Coursera Plus, and financial aid is available on many courses.
Python and programming
| Course | Provider |
|---|---|
| CS50’s Introduction to Programming with Python | HarvardX (edX) |
| Python for Data Science, AI & Development | IBM (Coursera) |
| Data Analysis with Python | IBM (Coursera) |
SQL, Excel and BI tools
| Course | Provider |
|---|---|
| SQL for Data Science | UC Davis (Coursera) |
| Retrieve Data Using SQL | OpenClassrooms (free to follow) |
| Excel Basics for Data Analysis | IBM (Coursera) |
| Create Dashboards with Power BI | OpenClassrooms (free to follow) |
| Create Dashboards with Tableau | OpenClassrooms (free to follow) |
Statistics and math
| Course | Provider |
|---|---|
| Data Science Math Skills | Duke University (Coursera) |
| Data Science: Inference and Modeling | HarvardX (edX) |
| MATLAB Academy | MathWorks (many free self-paced courses) |
The HarvardX Data Science series
Harvard’s series, taught in R, is one of the most complete free paths available. Take them in order:
| Course | Focus |
|---|---|
| Data Science: Visualization | ggplot2 and principles of good charts |
| Data Science: Wrangling | Importing, tidying and reshaping data |
| Data Science: Inference and Modeling | Statistical inference and modeling |
| Data Science: Building Machine Learning Models | Core machine learning |
| Data Science: Capstone | An end-to-end project |
Machine learning and AI
| Course | Provider |
|---|---|
| Machine Learning Specialization | Stanford Online and DeepLearning.AI, taught by Andrew Ng (Coursera) |
| AI For Everyone | DeepLearning.AI, Andrew Ng (Coursera), no coding |
| Applied Data Science Specialization | IBM (Coursera) |
Full professional certificates
| Program | Provider | Good first course |
|---|---|---|
| IBM Data Science Professional Certificate | IBM (Coursera) | What is Data Science? |
| IBM Data Analyst Professional Certificate | IBM (Coursera) | Introduction to Data Analytics |
Also useful: IBM’s Tools for Data Science (Jupyter, RStudio, GitHub). DataCamp’s Preprocessing for Machine Learning in Python is good too, but only the first chapter is free; the rest needs a subscription.
A Free Learning Path, in Order
- Python: freeCodeCamp Python Certification or CS50P.
- SQL: freeCodeCamp Relational Databases or Kaggle’s Intro to SQL.
- Data handling: Kaggle Pandas and Data Cleaning.
- Statistics: Data Science Math Skills, then Harvard’s Inference and Modeling.
- Machine learning: Kaggle Intro to Machine Learning, then Andrew Ng’s Machine Learning Specialization.
- A project: pick a Kaggle dataset, analyze it end to end, and publish the notebook. This matters more to employers than any certificate.
If you want to automate your analysis with code, our guide to automating Excel with Python and openpyxl is a practical next step.
Frequently Asked Questions
Yes to audit and learn. Some platforms charge only if you want a graded certificate.
Basic statistics and comfort with algebra are enough to begin. Deeper linear algebra and calculus help later for machine learning.
Start with a Python-for-data-science course, then SQL, then statistics, before moving on to machine learning.
It builds the skills. A portfolio of real projects plus a certificate or degree is usually what gets interviews.