Free alternative
DataCamp costs $25/month or ~$150/year. Here is what to use instead.
Every course below is free to access or free to audit. Start with one, finish it, then move on.
PythonBeginner
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.
PythonBeginner
Kaggle Learn's 4-hour Pandas course. Covers DataFrames and Series, indexing, summarising data, grouping, sorting, data types, missing values, renaming, and combining DataFrames.
Machine LearningBeginner
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.
SQLBeginner
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.
PythonBeginner
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.
SQLBeginner
Google's data analytics certificate. Covers data cleaning, analysis, visualisation with Tableau, SQL queries, and R programming. Free to audit; certificate costs money.
PythonIntermediate
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.
RIntermediate
Johns Hopkins's flagship 10-course Data Science Specialization on Coursera, taught entirely in R. Covers the R toolchain, statistical inference, regression, machine learning, and a capstone project. Free to audit; certificate costs money.
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.
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.
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.
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.
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.
A step-by-step order to work through these courses, not just a list.
Every skill to pick up, in order, with a free course for each phase.
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 →More free alternatives