Last updated: June 2026. Reviewed by Josh Hutcheson, OnlineCourseing editor. We take and pay for the tracks we review.
QUICK VERDICT — 4.3 / 5
Bottom line: DataCamp’s Data Scientist with Python career track is one of the best ways to learn data science fundamentals by actually writing code — 23 courses and roughly 90 hours that take you from Python basics to machine learning. It won’t make you a production-ready data scientist on its own, but as a structured foundation it’s hard to beat for the price.
- Best for: Beginners and career-changers who learn by doing
- Pricing: ~$14/month billed annually (first chapter of every course free)
- Skip if: You need deep, production-grade ML or deployment skills
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DataCamp’s Data Scientist with Python track is its flagship learning path: a structured sequence that moves from Python basics through data manipulation, statistics, machine learning, and a first taste of deep learning. The promise is that you can build genuine data science skills entirely in the browser, one short coding exercise at a time. After working through it, here’s our honest assessment of where it delivers and where it falls short.
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Data Scientist with Python: track at a glance
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| Feature | Details |
|---|---|
| Courses | ~23 courses in the track |
| Length | ~90 hours of content |
| Languages | Python (primary), SQL |
| Topics | Python, pandas, visualization, statistics, machine learning, intro deep learning |
| Format | Interactive in-browser coding + guided projects |
| Certificate | DataCamp track completion statement |
| Price | ~$14/month billed annually; first chapter of each course free |
What the track covers
Python and data manipulation. The track opens with Python fundamentals and quickly moves into pandas and NumPy for cleaning and reshaping data. If you already code, DataCamp’s skill assessments let you test out and skip ahead rather than sitting through the basics.
Visualization and statistics. You cover matplotlib, seaborn, and plotly for charts, then move into statistical thinking, hypothesis testing, and experimental design — the part that genuinely separates a data scientist from a data analyst, and a section DataCamp handles well.
Machine learning and deep learning. The bulk of the back half is supervised and unsupervised learning with scikit-learn — regression, decision trees, random forests, clustering, model evaluation, and feature engineering — closing with an introduction to neural networks in TensorFlow/Keras and the basics of NLP.
What it does well
- Genuinely interactive: every lesson is hands-on coding in the browser — far stickier than video-only platforms, and you retain more.
- Logical progression: each course builds on the last in a deliberate sequence, so you’re never lost about what to learn next.
- Bite-sized: short 2–4 minute explanations paired with exercises make it easy to keep a daily habit.
- Skill assessments: test out of material you already know instead of grinding through it.
Where it falls short
No platform is perfect, and being honest about the gaps is the point of a review. Three matter here:
- Advanced topics stay shallow: the deep learning and NLP coverage is introductory, not production-grade. You’ll know the vocabulary, not the depth.
- Projects are guided: the in-platform projects hold your hand more than building something from a blank file would. You’ll still want a portfolio project of your own.
- No deployment or engineering: the track doesn’t touch MLOps, APIs, or putting a model into production — skills most data science jobs eventually expect.
Pricing and is it worth it?
DataCamp runs on a single subscription — around $14 a month when billed annually (roughly $168 a year), or a higher month-to-month rate — and that unlocks the entire course library, not just this track. The first chapter of every course is free, so you can sample the format before paying anything. Measured against a single bootcamp seat costing thousands, a year of DataCamp to build the fundamentals is inexpensive. The honest framing: it’s excellent value for what it is — structured fundamentals practice — and a poor fit if you expected it to replace a full degree or bootcamp.
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Who should take it — and who shouldn’t
Take it if you’re a beginner or career-changer who learns better by doing than by watching, you want a clear path rather than assembling one yourself, and you’re prepared to supplement it with your own projects. Look elsewhere if you already have the fundamentals and need advanced, production-level ML, or if you want mentor support and career services — for that, a more comprehensive program is the better spend. See our roundup of the best data science programs online for those options, and DataCamp vs Coursera if you’re weighing the two.
The verdict
DataCamp’s Data Scientist with Python track earns a 4.3 out of 5. It’s one of the most effective ways to build data science fundamentals through real practice, at a price that’s easy to justify. It stops short of making you job-ready on its own — you’ll need portfolio projects and some production skills to close that gap — but as the structured foundation everything else builds on, it’s a genuinely strong choice. Try the free first chapter before you commit; the format either clicks for you or it doesn’t, and you’ll know quickly.
Frequently asked questions
Is DataCamp good for learning data science?
Yes, for fundamentals. The Data Scientist with Python track is one of the most effective ways to learn the basics through interactive coding, covering Python, statistics, and machine learning across about 90 hours. It’s a foundation, not a complete replacement for a degree or bootcamp.
How long does the DataCamp Data Scientist track take?
About 90 hours across roughly 23 courses. At 5–10 hours a week, most learners finish in two to four months. DataCamp’s daily-practice streaks help maintain momentum.
How much does DataCamp cost?
A DataCamp subscription is roughly $14 a month billed annually (about $168 a year), or more on a monthly plan. It unlocks the full library, and the first chapter of every course is free to try.
Does the DataCamp certificate help you get a job?
It demonstrates completed coursework, which helps for entry-level roles, but employers weight portfolio projects and demonstrated skills more heavily. Treat the track as the learning, and your own projects as the proof.
DataCamp or Coursera for data science?
DataCamp is stronger for hands-on, interactive practice; Coursera offers university-backed certificates and more theory. If you learn by coding, start with DataCamp; if you want a credential with a university name, lean Coursera. Our DataCamp vs Coursera comparison goes deeper.