Kirill Eremenko published his first data science course on Udemy in 2014, back when “data scientist” was a job title most hiring managers had never typed into a requisition. A decade later, the brand he built around those courses has grown into something closer to an ecosystem: a course library, a long-running interview podcast, a newsletter, and a community full of people who landed their first analytics job and stuck around.
That sprawl makes one question hard to answer. In a market crowded with DataCamp, Coursera, and a million free YouTube tutorials, is SuperDataScience still worth paying for? The answer depends entirely on what you’re trying to do. Here’s how the pieces fit together.
What SuperDataScience Actually Is
The name gets used for three different things, and people mix them up constantly.
- The course catalogue. The original business, and still the biggest. Machine Learning A-Z, Deep Learning A-Z, Python A-Z, SQL, Tableau and more, taught by Kirill and co-instructor Hadelin de Ponteves.
- The Super Data Science Podcast. Long-form interviews with practitioners, founders, and data leaders, running for hundreds of episodes.
- The platform itself. superdatascience.com hosts a blog, a newsletter, a jobs board, and a membership that bundles the courses together.
So when someone says they’re learning data science with SuperDataScience, they might mean a $20 Udemy purchase or a daily podcast habit. Both are legitimate entry points.
Inside the Course Catalogue
The A-Z series is the flagship. Machine Learning A-Z, the best known of the bunch, has racked up well over a million enrolments, and it earns them by being relentlessly practical. You build models in Python and R rather than reading proofs. Reusable code templates sit at the bottom of most sections, which is genuinely useful when you’re staring at a messy client dataset at 9pm.
What the A-Z courses do well
Coverage is the selling point. A single course walks through regression, classification, clustering, natural language processing, and dimensionality reduction, with the maths explained at a level that doesn’t assume a statistics degree. Kirill is an engaging presenter, and the production quality sits a notch above most of what you’ll find on the platform’s cheaper competitors.
Where they get thin
Depth is the trade-off. At roughly six or seven hours per algorithm family, you come away with a working intuition and not much more. The datasets are small and tidy, the kind you never meet in production. A retail churn model built on 10,000 clean rows teaches you the syntax of scikit-learn, not the reality of five inconsistent source systems.
Tool-specific courses
Beyond the A-Z line there’s a solid set of narrower courses covering SQL, Tableau, Power BI, Excel, TensorFlow, and statistics for business analysis. These tend to age better than the machine learning content, since SQL syntax and dashboard conventions change slowly.
The Podcast Is the Quiet Strength
If you only sample one thing from the SuperDataScience universe, make it the podcast. Kirill interviews guests ranging from Kaggle grandmasters to chief data officers at banks and pharmaceutical companies, usually for 40 to 60 minutes.
The episodes are less about library syntax and more about how a person actually built a career. Guests talk about failed projects, internal politics, and the unglamorous work of getting a model into production and keeping it there. That’s exactly the material course curricula skip. It costs nothing, and it works well on a commute.
What You’ll Pay
Pricing depends on where you buy, and the gap is wide enough to matter.
- Udemy purchases: list prices run into the low hundreds of dollars, but sales are frequent, so most people pay somewhere between $15 and $25 per course for lifetime access.
- The membership: the SuperDataScience site sells a subscription bundling the course library with the community and jobs board, billed monthly or annually.
If you’re price-sensitive, buy one Udemy course on sale, finish it, and only then decide whether a membership makes sense. Bundles look cheap until you’ve paid for forty hours of content you never open.
The Honest Limitations
Certificates from online platforms carry limited weight with hiring managers on their own. They’re evidence you put in effort, not proof you can do the job. A portfolio with two or three real projects pushed to GitHub will out-compete a stack of completion certificates almost every time.
Some of the machine learning material shows its age. Tools and deployment practices from the mid-2010s have moved on considerably, and a few lessons lean on libraries that have since changed shape. Treat the courses as a foundation in concepts, then update your tooling from current documentation.
The A-Z branding also overpromises. No single course takes a beginner to expert. What these do is move you from confused to functional, which is a genuinely valuable step and a much smaller one than the title implies.
Who Gets the Most Out of It
Three groups do well here. Career switchers from marketing, finance, or operations who need working familiarity with the whole pipeline rather than deep specialisation. Analysts who already live in Excel and want to add Python or SQL without enrolling in a master’s. And students testing whether the field holds their interest before committing three years and a tuition bill.
If you already write production code, or you’re targeting a research track, you’ll likely find the material too gentle.
A Sensible Way to Start
Buy one course, not five. Pick based on your destination: Python A-Z if you’re new to code, the SQL course if you’re comfortable with data but not databases, Tableau or Power BI if you’re chasing a reporting-heavy role.
Then set a schedule you can actually keep. Two hours on weekday evenings plus a longer block on Saturday gets you through a 20-hour course in about three weeks. Build something small with each new technique before moving to the next section, even if the output is ugly. The habit of finishing projects matters more than the certificate at the end.
Subscribe to the podcast while you work through the material. Hearing practitioners describe their day-to-day is a useful counterweight to the polished, sequential world of a curriculum. When you finish, ask yourself whether you could explain the model you built and why you chose it over the alternatives. If the answer is yes, the money was well spent. If not, rewatch the relevant sections before buying anything else.