DataCamp Machine Learning Scientist with Python Review

Overview

Machine Learning Scientist in Python is a DataCamp career track: a fixed sequence of short, interactive courses that you complete in the browser, with no software to install. DataCamp renamed it from “Machine Learning Scientist with Python”, which is the name most people still search for. When we read the track page on 24 September 2026 it listed 21 courses and 25 items in all (the courses plus hands-on projects), an estimated 85 hours of content, and a note that the track was updated in September 2026.

Each course follows the same pattern: a short video explains a technique, then you write code against a real dataset in an exercise window that checks your answer. The track assumes you already know how to load and clean data in Python; it teaches you how to build, validate and tune models on top of that, and then how to apply them to text, images and data too large for one machine.

If you have never written Python, start one step earlier. Our review of DataCamp’s Associate Data Scientist in Python track covers the track that teaches the pandas and statistics groundwork this one builds on.

Who this track is for

• Data analysts moving into modeling. If you already use pandas at work and want to build predictive models, this is the most direct route on DataCamp.
• Graduates of DataCamp’s data scientist track. It picks up where the associate track stops, with no overlap worth mentioning.
• Self-taught learners who want structure. The fixed order stops you skipping validation and tuning, the two topics self-directed learners most often miss.
• Not for complete beginners, and not for anyone who needs an accredited credential for a visa, a degree or an employer’s tuition policy.

Syllabus: the 21 courses, grouped by stage

DataCamp lists the courses as one long sequence. We have grouped them into six stages so you can see how the track builds. Course names are DataCamp’s own, as listed on 24 September 2026.

The projects sit between the courses. Two we saw on the track page were “Predictive Modeling for Agriculture” and “Clustering Antarctic Penguin Species”: guided but less scaffolded than the exercises, and the closest thing in the track to real work. Do them; they are what you can talk about in an interview.

The weakest stretch is stage 6. Two PySpark courses are enough to understand how Spark works, not to run a production pipeline, and the Kaggle course is more about workflow than any single technique. Stages 1 and 3 are the strongest: model validation and hyperparameter tuning each get a full course of their own, where many single machine learning courses squeeze them into a chapter.

What changed since the track launched

If you have read an older review of this track, including earlier versions of this one, several details have moved:

• Fewer courses, fewer hours. The track has gone from 23 courses and about 93 hours to 21 courses and 85 hours.
• PyTorch, not Keras. The deep learning stage now uses two PyTorch courses; the older version taught deep learning with Keras and TensorFlow.
• A new name. “with Python” became “in Python”, in line with DataCamp’s other tracks, though the page address still uses “with-python”.
• Simpler pricing. DataCamp dropped its cheaper Standard plan, so the track now needs Premium (see below).

Prerequisites

DataCamp’s courses start from the assumption that you can already work in Python. In practice you should be comfortable with:

• Python basics: variables, lists and dictionaries, loops, and writing functions.
• pandas and NumPy: loading a CSV, filtering and grouping a DataFrame, and handling missing values.
• Introductory statistics: mean and variance, distributions, and what a correlation does and does not tell you.

You do not need calculus or linear algebra to finish the track; the courses explain models at a practical level. You will need them later if you want to understand why a model behaves as it does, which is the gap between completing this track and working as a machine learning engineer.

How long it takes

DataCamp estimates 85 hours for the whole track. That is the time in the courses; add some for the projects and for re-doing exercises you get wrong. At five hours a week, 85 hours is about 17 weeks. At ten hours a week, it is about nine weeks. The track is self-paced with no deadlines, so the real risk is stalling rather than running out of time.

At five hours a week or fewer, the annual plan is clearly cheaper than paying month by month.

Pricing and duration

The track is not sold on its own; it comes with a DataCamp subscription. These prices were read on DataCamp’s own pricing page on 24 September 2026.

DataCamp publishes no end date for the $14 special price, so check the page before you buy. On the monthly plan, finishing the track in two months at ten hours a week costs $70; stretching it over four months costs $140, close to a full year of the annual plan. Our DataCamp pricing guide breaks down every plan, and teams can ask DataCamp for a business demo.

Is the certificate worth anything?

Finishing the track earns a Statement of Accomplishment: a shareable record, which you can add to LinkedIn, that you completed it. It is not accredited and is not an exam, so on its own it carries little weight with employers. DataCamp also runs separate certifications, such as its Data Scientist certification, which are distinct from completing a track and are included in Premium.

What gets you hired is evidence you can do the work. Take the track’s projects further, publish two or three of your own on GitHub with a clear write-up, and use the Statement of Accomplishment as supporting detail, not the headline.

Pros and cons

Pros

• Covers the full classical machine learning workflow, including the validation and tuning steps most courses rush.
• Current: the track page shows a September 2026 update, and deep learning is now taught in PyTorch.
• Nothing to install; every exercise runs in the browser, which removes the most common reason beginners give up.
• Short lessons suit an hour after work, and the free plan lets you try the first chapter of every course.
• Good value on the annual plan: $168 covers this track and the rest of the library.

Cons

• The exercises are heavily scaffolded; you fill in blanks more than you build from scratch, so the projects matter.
• The certificate is a Statement of Accomplishment, not an accredited credential.
• Light on the maths behind the models and on deploying a model into production.
• The PySpark and Kaggle courses are introductions, not depth.

Alternatives to consider

• Need the groundwork first? DataCamp’s Associate Data Scientist in Python is the natural track to take before this one.
• Want a university name on the certificate? Compare this track with Coursera’s machine learning programs in our DataCamp vs Coursera comparison.
• Comparing across platforms? Our best machine learning courses roundup ranks options from Coursera, Udacity, DataCamp and others.
• Looking at DataCamp’s other machine learning options? Our DataCamp machine learning review looks at its machine learning catalogue as a whole.
• Unsure about DataCamp itself? Read is DataCamp worth it for the platform-level verdict.

Conclusion

Machine Learning Scientist in Python is the most complete machine learning route on DataCamp, and the September 2026 version is more current than it was: PyTorch for deep learning, 21 tighter courses and 85 hours. For an analyst who already writes Python, it is a good-value, well-ordered way to learn to build and evaluate models. Treat it as training, not a credential: do every project, build a few of your own, and it will take you a long way toward a first machine learning role.

Start the Machine Learning Scientist in Python track on DataCamp.

Frequently asked questions

How long does the DataCamp Machine Learning Scientist track take?

DataCamp estimates 85 hours across its 21 courses. At five hours a week that is about 17 weeks; at ten hours a week, about nine weeks. It is self-paced, with no deadlines.

Is “Machine Learning Scientist with Python” the same as “Machine Learning Scientist in Python”?

Yes. DataCamp renamed the track from “with Python” to “in Python”; the content is the same track, and its web address still uses the old name.

How much does the track cost?

It is included in DataCamp Premium, which was $14 a month billed annually ($168 a year) or $35 month to month when we checked on 24 September 2026. The free Basic plan gives you only the first chapter of each course.

Do you get a certificate?

You get a Statement of Accomplishment when you finish. It is a shareable record of completion, not an accredited certificate. DataCamp’s separate certifications, such as Data Scientist, are distinct from completing a track.

Does the track teach deep learning?

Yes. Two courses cover deep learning with PyTorch, from building a first network to intermediate training techniques, and a third covers image processing.

Can this track get you a machine learning job?

It can give you the skills, but no course guarantees a job. Employers look for evidence, so complete the track’s projects and publish a few of your own alongside it.

Explore more courses

Browse our best machine learning courses and best Python courses roundups, or see all DataCamp plans and pricing.