Last updated: September 2026. Written by Josh Hutcheson, OnlineCourseing editor. Track syllabus, hours, certification details and prices checked against DataCamp’s own pages on 23 September 2026. See our review methodology.
DataCamp’s Associate Data Scientist in Python track (it was called Data Scientist with Python until DataCamp renamed it) is a 90-hour, beginner-friendly path from your first line of Python to supervised machine learning. We rate it 4.3 out of 5: it is one of the most efficient structured ways to learn data science by writing code, with gaps you need to know about before you pay.
QUICK VERDICT — 4.3 / 5
Bottom line: a well-sequenced, hands-on Python foundation for data science, and the official prep for DataCamp’s Associate Data Scientist certification. It will not make you job-ready alone: there is no SQL, Git or deep learning in it.
- Length: 23 courses, 11 projects and 3 skill assessments, about 90 hours
- Level: beginner, no prerequisites
- Best for: career changers and analysts who want structured Python practice
- Skip it if: you need mentoring, career services or an accredited credential
- Cost: included in DataCamp Premium; the first chapter of every course is free
Try the track free on DataCamp →
The track at a glance
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All figures below come from DataCamp’s track page, checked on 23 September 2026.
| Detail | Associate Data Scientist in Python |
|---|---|
| Former name | Data Scientist with Python |
| Type | Career track (job-oriented, as opposed to a narrower skill track) |
| Content | 23 courses, 11 guided projects, 3 skill assessments (37 items) |
| Estimated time | About 90 hours |
| Prerequisites | None |
| Main libraries | pandas, Matplotlib, Seaborn, statsmodels, scikit-learn |
| Learner rating | 4.7 from 93 reviews on DataCamp; 640,000+ learners |
| Last updated by DataCamp | September 2026 |
| Prepares for | Associate Data Scientist certification (Python) |
| Price | Included in DataCamp Premium |
Data Scientist with Python vs Associate Data Scientist in Python: what changed
If you are searching for the Data Scientist with Python track, this is it. DataCamp renamed it Associate Data Scientist in Python and kept the same place in its catalog: the beginner career track for Python data science. Older reviews, Reddit threads and GitHub repos still use the old name.
The content has moved on since those older reviews were written. Our original 2021 version of this page described an 88-hour track with 6 projects. The current version is about 90 hours with 11 projects and 3 skill assessments, and DataCamp lists it as updated in September 2026.
The name change caused one real source of confusion. DataCamp later launched a different track called Data Scientist in Python (no “Associate”). That one is a short, 9-course professional follow-on of about 26 hours, and it lists the associate track as its prerequisite. Our DataCamp data science review compares all four of DataCamp’s data science tracks side by side. This page goes deep on the associate Python track only.
What you will learn: the full curriculum
The 23 courses fall into five stages. The table groups them in the order you meet them, with the stage’s job in the track.
| Stage | Courses | What it gives you |
|---|---|---|
| 1. Python foundations | Introduction to Python; Intermediate Python; Introduction to Functions in Python; Python Toolbox; Writing Functions in Python | Variables, lists, dictionaries, loops, functions, iterators and list comprehensions. Enough Python to write your own analysis code. |
| 2. Data wrangling with 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; Working with Categorical Data in Python | Importing from CSV, Excel, SQL databases and the web, then cleaning, joining and reshaping. This is where most real data work happens. |
| 3. Visualization and communication | Introduction to Data Visualization with Matplotlib; Introduction to Data Visualization with Seaborn; Data Communication Concepts | Building and customizing charts, and presenting results to people who do not read code. |
| 4. Statistics and experimentation | Introduction to Statistics in Python; Exploratory Data Analysis in Python; Sampling in Python; Hypothesis Testing in Python; Experimental Design in Python; Introduction to Regression with statsmodels in Python | Summary statistics, distributions, sampling methods, t-tests, chi-square tests, A/B-style experiment design and linear and logistic regression. |
| 5. Machine learning | Supervised Learning with scikit-learn; Unsupervised Learning in Python; Machine Learning with Tree-Based Models in Python | Classification, regression, clustering, dimensionality reduction, decision trees and ensembles such as random forests and gradient boosting. |
Spread through the courses are 11 guided projects that apply each stage to a real dataset: Investigating Netflix Movies, Exploring NYC Public School Test Result Scores, Visualizing the History of Nobel Prize Winners, Analyzing Crime in Los Angeles, Customer Analytics: Preparing Data for Modeling, Exploring Airbnb Market Trends, Modeling Car Insurance Claim Outcomes, Hypothesis Testing with Men’s and Women’s Soccer Matches, Predictive Modeling for Agriculture, Clustering Antarctic Penguin Species and Predicting Movie Rental Durations.
There are also 3 skill assessments (Data Manipulation with Python, Importing & Cleaning Data with Python, and Python Programming). They are short timed tests that show where you are weak. They are useful practice for the certification exams, which use a similar timed format.
How the learning works
Every DataCamp course follows the same loop: a video of a few minutes, then several exercises you complete in a code editor in your browser, with instant feedback. You never install Python. That is the track’s biggest strength for beginners, and its biggest limitation later on.
- Why it works: the gap between watching and doing is a few seconds, so you practise every idea straight away. Short exercises also make it easy to study in 20-minute sessions.
- Where it falls short: exercises are pre-scaffolded. The data is loaded and half the code is often written for you. Real work starts from an empty file, a messy folder and your own environment.
- Projects close part of that gap: they are longer, less guided tasks in DataCamp’s notebook tool. They are still scaffolded, so you should plan at least one project of your own as well.
The instructors are working data scientists and academics. The track page lists Hugo Bowne-Anderson, Benjamin Wilson and Elie Kawerk among them. Because every course uses the same format, the experience is consistent from course to course.
How long does the track take?
DataCamp estimates about 90 hours. How that translates to calendar time depends on your weekly commitment:
| Hours per week | Approximate time to finish |
|---|---|
| 3 hours | About 7 months |
| 5 hours | About 4 to 5 months |
| 10 hours | About 2 months |
| 20 hours (full-time study) | About 4 to 5 weeks |
Two things usually stretch the estimate. Projects take longer than their labels suggest, because you debug your own code. And the statistics stage (stage 4) is the hardest part for people without a maths background. Budget extra time there instead of rushing to the machine learning courses.
The Associate Data Scientist certification
Finishing the track earns a statement of accomplishment, which is a completion record. The certification is a separate, assessed credential, and this track is DataCamp’s official preparation for it. According to DataCamp’s certification page, the associate level works like this:
| Part | Format | What it tests |
|---|---|---|
| Timed Exam 1 (DS101) | 2 hours, 2 attempts | Exploratory analysis, visualization, sampling and statistical testing |
| Timed Exam 2 (DS102) | 2 hours, 2 attempts | Importing, joining and cleaning data; transforming data for modeling; supervised and unsupervised models; writing functions and loops |
| Practical Exam (DS501P) | Project, auto-graded | Validating data, calculating metrics and fitting models for a business problem |
You have 30 days from registration to finish. Associate exams are graded automatically, so results arrive shortly after you submit. You can take the exams in Python or R, and the certification is included with a Premium subscription. The two timed exams map directly onto the track: DS101 covers stages 3 and 4, and DS102 covers stages 1, 2 and 5.
What is it worth? It is assessed, which makes it a stronger signal than a completion badge, and the practical exam gives you a worked project you can discuss in an interview. It is not accredited and will not substitute for a degree. The next level, the Data Scientist certification, adds SQL (exam DS201) and a human-graded, recorded presentation. For a wider look, read our verdict on whether DataCamp is worth it.
See the certification requirements →
How much does it cost?
The track is not sold separately. It comes with a DataCamp Premium subscription, which unlocks the whole catalog and the certifications. Prices from DataCamp’s pricing page on 23 September 2026:
| Plan | Price | What you get |
|---|---|---|
| Basic | Free | The first chapter of every course, so you can try the first stage of this track |
| Premium, monthly | $35 a month | Every course, track, project and certification |
| Premium, annual | $28 a month list ($336 a year) | Same as monthly; cheapest way to finish a 90-hour track |
PRICE CHECK — 23 SEPTEMBER 2026
DataCamp was running a 50%-off sale that cut the annual plan to $14 a month ($168 a year). Its site said the sale was ending within two days. DataCamp has run discounts like this before, so if you are not in a hurry, check for one before you pay list price. Our DataCamp pricing guide tracks the current plans.
The value calculation is simple. At the annual list price, finishing the track in four to five months costs a fraction of a data science bootcamp, which typically runs to thousands of dollars. If you study 5 hours a week, five months on the monthly plan ($175) costs about half the annual list price, so at list price the annual plan only pays off if you keep learning after this track. During a sale like the September one, a full year ($168) costs less than five monthly payments.
Check current DataCamp prices →
Strengths
- Sequencing: each course builds on the last, which removes the “what should I learn next?” problem that stalls self-taught learners.
- Practice density: you write code in every lesson, not just watch it. For fundamentals, that beats most video-first courses.
- A current, industry-standard stack: pandas, Matplotlib, Seaborn, statsmodels and scikit-learn are the libraries data scientists use daily.
- Statistics taken seriously: sampling, hypothesis testing and experimental design get six courses. Many beginner programs skip straight to machine learning.
- A clear route to a credential: the track maps exam by exam onto the Associate Data Scientist certification, at no extra cost on Premium.
Weaknesses
- No SQL or Git: nearly every data science job posting asks for both. They are in the follow-on Data Scientist in Python track, not this one.
- No deep learning or deployment: the track stops at classical machine learning. There is nothing on neural networks, putting a model into production or monitoring it.
- Hand-holding: exercises and projects are scaffolded. You can finish the track without ever setting up Python on your own machine, which hurts later.
- No mentors or career services: help comes from hints and community forums. There is no code review from a person and no job support.
- Rolling updates: DataCamp updates courses individually, so check the last-updated date on each course if a specific library version matters to you.
Who should take it, and who should not
| Take it if you… | Look elsewhere if you… |
|---|---|
| Are new to programming and want a structured start in Python for data | Need an accredited qualification or university credit |
| Work with Excel or BI tools and want to move into Python analysis | Want a mentor, code reviews and career coaching |
| Learn best in short, frequent sessions | Already know pandas and scikit-learn well |
| Want to sit DataCamp’s Associate Data Scientist certification | Mainly want deep learning or AI engineering skills |
Already comfortable with Python? Take the three skill assessments first. If you score well, skip ahead to the statistics and machine learning stages, or go straight to the follow-on track.
What to take after this track
The associate track is a foundation. These are the logical next steps inside DataCamp, all included in the same subscription:
- Data Scientist in Python (9 courses, about 26 hours): adds SQL, Git, Python packaging and machine learning preprocessing. For job-seekers it is the most important add-on, mainly for the SQL and Git.
- Machine Learning Scientist in Python (21 courses, about 85 hours): goes much deeper into modeling. Read our Machine Learning Scientist track review first.
- A project of your own: pick a public dataset you care about, analyze it from a blank notebook on your own machine and publish it on GitHub. This is the piece employers ask about.
Alternatives to the DataCamp track
The associate track is the best fit for learners who value practice density and price. Here is how it compares with the main alternatives.
| Option | Format | Best for |
|---|---|---|
| DataCamp Associate Data Scientist in Python | 23 short interactive courses + 11 projects, ~90 hours | Hands-on fundamentals at low cost |
| IBM Data Science Professional Certificate (Coursera) | 12-course series; Coursera estimates about 4 months at 10 hours a week; 960,000+ enrolled | A widely recognized brand-name certificate and more SQL coverage |
| Dataquest | Text-first, project-heavy paths | Learners who prefer reading to video |
| Udacity Nanodegrees | Project programs with human reviews | People who want feedback on their projects from reviewers |
| Bootcamps (Springboard, BloomTech) | Mentored, cohort-based programs | Career changers who need coaching and job support |
For a direct platform-level comparison, see DataCamp vs Coursera and Codecademy vs DataCamp. If you would rather compare individual programs across every platform, our best data science courses and best Python courses guides rank them.
Is the DataCamp Associate Data Scientist track worth it?
For the right learner, yes. The job market still supports the investment: the U.S. Bureau of Labor Statistics puts the 2025 median pay for data scientists at $120,230 and projects 35% employment growth from 2025 to 2035, with 275,600 jobs in 2025 (BLS Occupational Outlook Handbook).
The honest caveat is that entry into data science is competitive, and a DataCamp track alone rarely gets anyone hired. It is worth paying for as the first step: the fastest structured way we know to go from zero to confident with pandas, statistics and scikit-learn. Pair it with the follow-on track for SQL and Git, the certification if you want an assessed credential, and one or two self-directed projects.
OUR VERDICT
Take the Associate Data Scientist in Python track if you want a disciplined, practice-heavy Python foundation for data science and plan to build on it. Try the free first chapter of Introduction to Python first. If the short-exercise format suits you, the rest of the track will too.
Start Associate Data Scientist in Python →
Frequently asked questions
Is Data Scientist with Python the same as Associate Data Scientist in Python?
Yes. DataCamp renamed the Data Scientist with Python career track to Associate Data Scientist in Python. It is the same beginner path, now 23 courses and about 90 hours. It is not the same as the newer Data Scientist in Python track, which is a 9-course professional follow-on that lists the associate track as its prerequisite.
How long does the DataCamp Associate Data Scientist in Python track take?
DataCamp estimates about 90 hours. At 5 hours a week that is roughly four to five months; at 10 hours a week, about two months. Most of the variation comes from the 11 projects, which take longer than the courses.
Is the DataCamp Associate Data Scientist track good for beginners?
Yes. It has no prerequisites and starts with Introduction to Python. It suits beginners who learn well from short videos and guided exercises. It is less suited to people who want to set up their own coding environment from day one.
Does completing the track give you a certification?
No. Finishing the track earns a statement of accomplishment. The Associate Data Scientist certification is a separate assessment: two timed exams (DS101 and DS102) and a practical exam (DS501P), completed within 30 days. It is included with DataCamp Premium.
How much does the Associate Data Scientist in Python track cost?
The track is included with DataCamp Premium. When we checked on 23 September 2026, Premium cost $35 a month on the monthly plan, or a $28-a-month list price billed annually, cut to $14 a month in a 50%-off sale that DataCamp said was ending within two days. The first chapter of every course is free.
Does the track teach SQL?
No. The associate track is Python only. SQL is taught in the follow-on Data Scientist in Python track, and it is tested in the professional-level Data Scientist certification (exam DS201), not the associate one.
Can you get a data science job with just this DataCamp track?
Rarely on its own. It builds solid fundamentals, but employers also look for SQL, Git, a portfolio of self-directed projects and often a degree. Treat the track as the foundation, then add the follow-on track and your own projects.
Related guides
- DataCamp data science review: all four tracks compared
- Is DataCamp worth it?
- DataCamp pricing
- Best DataCamp courses
- DataCamp Machine Learning Scientist track review
- IBM Data Science Professional Certificate review