Free alternative to DataCamp: the top free data science courses
DataCamp teaches data science in your browser: interactive Python and SQL lessons, strong coverage of pandas, numpy, and scikit-learn, and a gamified track that a lot of career-switchers like. The catch is the price. It runs about $25 a month, or roughly $150 a year, which is a real barrier when you are just starting out and not yet sure data science is for you. Here is the honest version: the free options below cover the same pandas, scikit-learn, and SQL ground that DataCamp's core tracks do. DataCamp wins on polish. The gamified progression, the instant feedback inside the exercises, and the zero setup are genuinely nice when you are new and want momentum. The free options win on depth, community, and real projects with messy data. If you are starting from zero, Kaggle Learn gets you to the same pandas and scikit-learn foundations in about 20 hours, for free.
DataCamp costs $25/month or ~$150/year. Here is what to use instead.
The free courses that replace DataCamp
Every course below is free to access or free to audit. Start with one, finish it, then move on.
Python (Kaggle Learn)
Kaggle Learn's 7-hour Python micro-course covering syntax, functions, booleans and conditionals, lists, loops, strings, dictionaries, and working with external libraries. Notebook-based with auto-graded exercises.
Pandas (Kaggle Learn)
Kaggle Learn's 4-hour Pandas course. Covers DataFrames and Series, indexing, summarising data, grouping, sorting, data types, missing values, renaming, and combining DataFrames.
Kaggle Learn's micro-course on machine learning fundamentals using scikit-learn. Covers decision trees, model validation, underfitting and overfitting, and random forests. Three hours, all in browser-based notebooks.
Intro to SQL (Kaggle Learn)
Kaggle Learn's 3-hour SQL micro-course using BigQuery. Covers SELECT, WHERE, GROUP BY, ORDER BY, JOINs, and writing efficient queries over large datasets. Notebook-based and free.
Python for Everybody Specialization
Dr. Chuck's Python for Everybody course from University of Michigan. Covers Python basics, data structures, web data access, databases, and capstone. Free to audit; certificate for purchase.
Google's data analytics certificate. Covers data cleaning, analysis, visualisation with Tableau, SQL queries, and R programming. Free to audit; certificate costs money.
Data Analysis with Python
Learn data analysis using NumPy, Pandas, Matplotlib, and Seaborn. Build real data analysis projects using real-world datasets. Earn a free verified certificate after completing 5 projects.
The free stack that covers DataCamp's core
Two free resources cover most of what DataCamp's beginner tracks teach. Kaggle Learn is the closest match: short interactive courses on Python, pandas, data visualization, machine learning, and SQL, all running in your browser with real datasets from the first lesson. There is no account wall on the free content, and the machine learning course walks you through scikit-learn from scratch in about four hours, which is DataCamp's core ML track for free. It is less gamified than DataCamp but more grounded in the actual messy data work. Coursera fills in the structured, certificate-style path. You can audit most courses at no cost, so the IBM Data Science Professional Certificate and the Google Data Analytics Certificate are free to watch and work through; you only pay if you want the graded certificate. The IBM path alone covers Python, pandas, SQL, data visualization, and machine learning in a sequence that mirrors DataCamp's career tracks. Audit access is not advertised loudly, but it is real.
Where to go deeper on machine learning and AI
Once you have the basics, three free resources go further than DataCamp does. fast.ai's Practical Deep Learning for Coders starts with a working model in the first lesson and fills in the theory as you need it, covering neural networks, computer vision, NLP, and tabular data in PyTorch. It assumes you can already write Python, so it is not a first step, but at that level it beats anything DataCamp offers. Hugging Face publishes a free NLP course and a diffusion models course that go deeper on language models and image generation than DataCamp's AI track touches. And DeepLearning.AI runs short courses, most under two hours, on LLM APIs, RAG, agents, and fine-tuning; they are written for developers building systems, not analysts running notebooks. Take Kaggle's machine learning course first if you need the prerequisites.
Matching DataCamp for SQL, Python, and R
DataCamp's strength is that it bundles Python, SQL, and R in one place. You can rebuild that for free. For SQL, Kaggle Learn's SQL course covers the same SELECT, JOIN, GROUP BY, and window function ground as DataCamp's SQL track. For Python, start with Kaggle's Python course (about five hours), then its pandas and machine learning courses, and you have the core of DataCamp's Python Data Scientist track. If you want more Python depth first, freeCodeCamp's Scientific Computing with Python certification is free. For R, the Johns Hopkins Data Science Specialization on Coursera is free to audit and is more thorough than DataCamp's R content. The pieces are all here; you just assemble them yourself instead of following one paid track.
The honest catch
DataCamp's paid product buys you one thing the free stack does not: a single, gamified path with instant feedback baked into every exercise, so you never have to decide what to learn next or set anything up. Assembling Kaggle, Coursera audit tracks, and fast.ai into a coherent plan takes more self-direction, and no one nudges you to keep your streak. If that structure is the difference between finishing and quitting, $150 a year can be money well spent.
Our verdict
For learning data science from scratch, skip the DataCamp subscription and start with Kaggle Learn's Python, pandas, and machine learning courses, then add Coursera's free-to-audit IBM or Google certificates for structure. Pay for DataCamp only if you know the gamified feedback loop is what keeps you going.
Where these courses come from
Keep going
When you are ready to invest
Free will take you a long way. If you later decide paid structure or career support is worth it, we keep an honest shortlist of the ones worth the money.
See the paid options we recommend →DataCamp: your questions, answered
Is DataCamp worth paying for?
For some learners, yes. DataCamp's gamified progression and instant browser feedback work well if you need that structure to keep going. But the free alternatives cover the same pandas, scikit-learn, and SQL ground at no cost. It mostly comes down to whether the feedback loop of DataCamp's interactive exercises is worth about $150 a year to you, versus self-directing through Kaggle or Coursera.
Does Kaggle Learn give a certificate?
Yes, each Kaggle Learn course awards a completion certificate. It is not as recognized as the IBM or Google certificates from Coursera (which cost money to certify), but it is free and shows on your Kaggle profile. For a portfolio, a project notebook on Kaggle with real data is usually more convincing than any certificate anyway.
Which free option is closest to DataCamp for SQL?
Kaggle Learn's SQL course covers the same SELECT, JOIN, GROUP BY, and window function ground as DataCamp's SQL track, for free. If you want a certificate path, the Google Data Analytics Certificate on Coursera (free to audit) uses SQL throughout.
Can I learn Python for data science for free?
Yes. Kaggle Learn's Python course is a clean starting point, about five hours. Follow it with Kaggle's pandas and machine learning courses and you have covered the core of DataCamp's Python Data Scientist track for free. If you want more Python depth first, freeCodeCamp's Scientific Computing with Python certification is also free.
More free alternatives