Last updated: September 2026. Reviewed by Josh Hutcheson, OnlineCourseing editor. Track syllabi, hours and prices checked against DataCamp’s own pages in September 2026.
QUICK VERDICT — 4.3 / 5
Bottom line: DataCamp now runs four data science career tracks, not one. The two Associate Data Scientist tracks (Python or R, about 90 hours each) are the foundation and the right starting point for almost everyone. The two newer Data Scientist tracks (about 26 to 27 hours) are a professional-level follow-on that adds SQL, Git, packaging and ML preprocessing. Together they are one of the most practical ways to build data science fundamentals by writing code, at a price that is easy to justify.
- Start with: Associate Data Scientist in Python (23 courses, about 90 hours)
- Then: Data Scientist in Python (9 courses, about 26 hours) if you want the SQL, Git and certification layer
- Pricing: about $14/month billed annually on the standing offer ($28 list), $35 month-to-month; first chapter of every course free
- Skip if: you need deep learning, MLOps or deployment. None of the four tracks teaches production ML
Start the Associate Data Scientist Track Free →
When most people search for a DataCamp data science review, they mean one thing: the career track DataCamp used to call Data Scientist with Python. That track still exists, renamed Associate Data Scientist in Python, but it now sits inside a family of four. DataCamp has added a shorter, professional-level Data Scientist in Python track that picks up where the associate track stops, and it runs the same two-level structure in R.
This review covers the whole family: what each track teaches, how the associate and professional levels fit together, whether to learn in Python or R, how DataCamp’s data scientist certification works, and whether the subscription is worth paying for. If you have already settled on the Python associate track and want a course-by-course walkthrough, our Associate Data Scientist in Python review goes deeper on that one track.
Disclosure: the DataCamp links on this page are affiliate links. If you subscribe through one we may earn a commission at no extra cost to you. It doesn’t affect our rating.
DataCamp’s four data science tracks at a glance
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DataCamp organizes data science into two levels per language. The associate track is the foundation. The professional track lists the associate track as its prerequisite, and its course numbering even continues from where the associate track ends. Here is how the four compare, using the course counts and hours DataCamp publishes on each track page.
| Track | Level | Courses | Hours | Prerequisite | Prepares for |
|---|---|---|---|---|---|
| Associate Data Scientist in Python | Foundation | 23 | ~90 | None | Associate Data Scientist certification |
| Data Scientist in Python | Professional | 9 | ~26 | Associate Data Scientist | Data Scientist certification |
| Associate Data Scientist in R | Foundation | 22 | ~88 | None | Associate Data Scientist certification |
| Data Scientist in R | Professional | 9 | ~27 | Associate Data Scientist (R) | Data Scientist certification |
The practical reading of that table: the associate tracks are where the learning happens, at roughly 90 hours each. The professional tracks are short by comparison and assume you already have the associate material. Treat them as a second step, not an alternative first step.
Associate Data Scientist in Python: the flagship track
This is the track most reviews, including the original version of this one, are about. It has no prerequisites and runs 23 courses and 11 guided projects across about 90 hours. DataCamp renamed it from Data Scientist with Python, so older reviews and forum threads use the old name for the same path.
What it covers
- Python foundations: Introduction to Python, Intermediate Python, Introduction to Functions in Python, Python Toolbox and Writing Functions in Python.
- Working with data in pandas: Data Manipulation with pandas, Joining Data with pandas, Introduction to Importing Data in Python, Cleaning Data in Python, Working with Dates and Times in Python and Working with Categorical Data in Python.
- Visualization and exploration: Introduction to Data Visualization with Matplotlib, Introduction to Data Visualization with Seaborn, Exploratory Data Analysis in Python and Data Communication Concepts.
- Statistics: Introduction to Statistics in Python, Introduction to Regression with statsmodels in Python, Sampling in Python, Hypothesis Testing in Python and Experimental Design in Python.
- Machine learning: Supervised Learning with scikit-learn, Unsupervised Learning in Python and Machine Learning with Tree-Based Models in Python.
The projects sit between the courses and use real datasets: Netflix movies, NYC public school test scores, Nobel Prize winners, crime in Los Angeles, Airbnb listings, car insurance claims, men’s and women’s soccer results, agricultural yield prediction, clustering Antarctic penguin species and predicting movie rental durations.
One correction worth making plainly, because earlier versions of this page got it wrong: the current associate track does not include deep learning, TensorFlow or NLP. It stops at classical machine learning with scikit-learn. That is the right scope for a foundation, but if deep learning is your goal you will need a separate track afterwards.
For a course-by-course walkthrough, including the project experience and how long each phase realistically takes, read our dedicated Associate Data Scientist in Python review.
See the Associate Data Scientist in Python Track →
The new Data Scientist in Python track: what it adds
Data Scientist in Python is the professional-level follow-on, and it is the newest of the four. DataCamp lists the Associate Data Scientist track as its prerequisite and estimates about 26 hours for its 9 courses. Where the associate track teaches you to analyze and model data, this one fills in the working skills a data science job expects around that analysis.
- Data engineering basics: Intermediate Importing Data in Python (web and API data) and Preprocessing for Machine Learning in Python.
- Writing reusable code: Developing Python Packages.
- Applying ML to business problems: Machine Learning for Business.
- SQL: Introduction to SQL, Intermediate SQL and Joining Data in SQL.
- Version control: Introduction to Git and Intermediate Git.
Two projects round it out: analyzing students’ mental health data and a SQL project on the golden era of video games.
Is it worth adding? For most learners, yes, and mostly for the SQL and Git. Nearly every data science job posting expects both, and the associate track barely touches them. At about 26 hours it is a small extra commitment on a subscription you are already paying for. What it does not add is depth in modeling: there is no deep learning here either. Its other job is certification prep, covered below.
See the Data Scientist in Python Track →
The R tracks: Associate Data Scientist in R and Data Scientist in R
The R family mirrors the Python one. Associate Data Scientist in R runs 22 courses over about 88 hours with no prerequisites: R and the tidyverse, dplyr for data manipulation, ggplot2 for visualization, importing and cleaning data, writing functions, exploratory analysis, regression, sampling, hypothesis testing, experimental design, and supervised and unsupervised learning.
Data Scientist in R is the 9-course, roughly 27-hour follow-on: Intermediate Importing Data in R, Developing R Packages, Machine Learning for Business, Feature Engineering in R, the same three SQL courses and the same two Git courses as the Python version.
See the Associate Data Scientist in R Track →
Python or R: which DataCamp track should you choose?
Both languages are taught to the same standard, and the associate and professional structure is identical, so the choice comes down to where you want to work.
- Choose Python if you are aiming at general data science, machine learning or tech-industry roles, or you may later move toward data engineering or AI work. Python is the more common language in job listings for those roles, and it is the path we recommend by default.
- Choose R if you work, or want to work, in academic research, biostatistics, public health, or a team that already uses R. R’s statistical tooling and ggplot2 visualizations remain excellent, and the R associate track is a slightly shorter 88 hours.
- Not sure? Start with Python. Learning a second language later is much easier than switching tracks halfway, and DataCamp’s subscription covers both if you want to sample R once you have the fundamentals.
DataCamp’s data scientist certification: how it works
Completing a track earns a statement of accomplishment. The certification is a separate, assessed credential, and the tracks are designed to prepare you for it: the associate tracks for the Associate Data Scientist certification, the professional tracks for the Data Scientist certification.
According to DataCamp’s certification page, the process has several parts. First come two timed exams that test technical proficiency, with two hours and two attempts for each. Then a practical exam in which you apply your skills to a real-world data project; associate-level exams are graded automatically, so results arrive shortly after you submit. The certification page also lists a recorded presentation, in which you communicate your findings to stakeholders. You can take the exams in Python or R, and SQL is part of what they assess.
On cost, DataCamp lists certification at $25 a month on its own, and it is included in a Premium subscription. If you are already subscribing for the tracks, the certification adds nothing to the bill.
How much is it worth? It is a useful signal for entry-level roles and career changers because it is assessed, not just a completion badge, and the practical exam produces work you can talk through in an interview. It is not a substitute for a degree or for a portfolio of your own projects, and most employers will weigh those more heavily.
See DataCamp’s Data Scientist Certification →
What DataCamp’s data science tracks do well
- You learn by writing code: every lesson pairs a short explanation with in-browser coding exercises, which keeps you practicing rather than watching.
- A clear sequence: each track is a deliberate path, so you never have to decide what to learn next, which is where most self-taught learners stall.
- Real datasets in the projects: the 11 associate projects use recognizable real-world data, which makes them better portfolio talking points than toy examples.
- A sensible two-level structure: the professional tracks add SQL and Git, the two skills beginners most often skip and employers most often expect.
- Test out of what you know: skill assessments let experienced learners skip material instead of grinding through it.
Where they fall short
Being honest about the gaps is the point of a review. Four matter here.
- No deep learning or NLP in the core tracks: all four stop at classical machine learning. DataCamp teaches deep learning in separate tracks, which you would add afterwards.
- Guided projects hold your hand: the in-platform projects are scaffolded. You will still want at least one project built from a blank file to show employers.
- No deployment or MLOps: none of the tracks covers putting a model into production, building APIs or monitoring models, which most data science jobs eventually expect.
- Short exercises can feel shallow: the bite-sized format is great for habit-building but light on the long, messy problems real work involves. Pair the tracks with your own analysis of a dataset you care about.
Pricing: is DataCamp worth it for data science?
DataCamp runs on a single subscription that covers the entire library, including all four tracks and the certification. When we checked in September 2026, Premium was listed at $14 a month billed annually on DataCamp’s standing offer (about $168 a year, against a $28 list price), or $35 a month on the monthly plan. The free Basic tier gives you the first chapter of every course, so you can try the format before paying. Our DataCamp pricing guide tracks the current plans and any discounts.
Measured against a data science bootcamp costing several thousand dollars, a year of DataCamp is inexpensive for what it covers: roughly 115 hours of structured Python training across the two levels, plus the certification. The honest framing is that it is excellent value as structured fundamentals practice, and a poor fit if you expected it to replace a degree, a bootcamp’s career services, or mentorship.
A realistic timeline: at 5 to 10 hours a week, most learners finish the associate track in two to four months and the professional track in another three to five weeks.
Check Current DataCamp Pricing →
Which DataCamp data science track should you take?
| If you are… | Take this |
|---|---|
| A complete beginner aiming for data science | Associate Data Scientist in Python, then Data Scientist in Python |
| Already comfortable with pandas and scikit-learn | Test out with skill assessments, then Data Scientist in Python for SQL and Git |
| Working in research, biostatistics or an R shop | Associate Data Scientist in R, then Data Scientist in R |
| Mainly after an assessed credential | Finish an associate track, then sit the certification (included in Premium) |
| Interested in data analysis rather than data science | DataCamp’s Data Analyst tracks; see our best DataCamp courses |
| Looking for deep learning or production ML | A different path; see our Machine Learning Scientist review and best data science courses |
The verdict
DataCamp’s data science tracks earn a 4.3 out of 5. The Associate Data Scientist in Python track remains one of the most effective ways to build data science fundamentals through real practice, and the new Data Scientist in Python track fixes its biggest gap by adding SQL and Git. The whole path is honest about being a foundation: it will not teach deep learning or production engineering, and you will need your own projects to prove your skills. As the structured base everything else builds on, it is a strong choice at a price that is easy to justify.
Our recommendation is to try the free first chapter of the associate track before committing. The format either clicks for you or it does not, and you will know quickly. If you are also weighing other platforms, see our full DataCamp review, DataCamp vs Coursera and DataCamp vs Dataquest.
Try the Associate Data Scientist Track Free →
Frequently asked questions
Is DataCamp good for learning data science?
Yes, for fundamentals. The Associate Data Scientist in Python track teaches Python, pandas, visualization, statistics and classical machine learning through interactive coding across about 90 hours. It is a strong foundation, but not a complete replacement for a degree or bootcamp, and it does not cover deep learning or deployment.
What is the difference between DataCamp’s Associate Data Scientist and Data Scientist tracks?
The Associate Data Scientist track is the foundation: 23 courses and about 90 hours, with no prerequisites. The Data Scientist track is a professional-level follow-on of 9 courses and about 26 hours that lists the associate track as its prerequisite and adds SQL, Git, Python packaging and machine learning preprocessing.
Is Data Scientist with Python the same as Associate Data Scientist in Python?
Yes. DataCamp renamed its Data Scientist with Python career track to Associate Data Scientist in Python. Older reviews and forum posts use the old name for the same 23-course track.
How long does the DataCamp data scientist track take?
DataCamp estimates about 90 hours for the Associate Data Scientist in Python track and about 26 hours for the Data Scientist in Python follow-on. At 5 to 10 hours a week, most learners finish the associate track in two to four months.
Should I learn data science on DataCamp with Python or R?
Choose Python for general data science, machine learning and tech-industry roles. Choose R for academic research, biostatistics, public health or teams that already use R. Both have identical associate and professional tracks, and the subscription covers both.
How much does DataCamp cost?
When we checked in September 2026, DataCamp Premium was $14 a month billed annually on its standing offer (about $168 a year, against a $28 list price), or $35 a month month-to-month. It covers all tracks and the certification, and the first chapter of every course is free.
Is the DataCamp data scientist certification worth it?
It is a useful credential for entry-level roles because it is assessed: two timed exams plus a practical exam on a real data project. It is included in Premium, so it adds no cost if you already subscribe. Employers still weigh portfolio projects and degrees more heavily.