Every course from every provider we track, with the same filters over all of them: level, length, price, certificate and who stands behind it.
362 courses for “Product Management 101: Everything You Need to Know (Full Course)”
53 hours
29 lessons, 3164 minutes in all.
5 hours
Five hours on MLOps: getting a model out of a notebook and keeping it running.
New courses, a couple of emails a month. One click unsubscribes.
12 hours
Twelve hours of Python, taught by building rather than by explaining.
12 hours
Twelve hours of Python for beginners, taken slowly enough to follow along.
4 hours
Four and a half hours of Python from nothing, the course most people start with.
10 hours
Ten hours of data mining: the techniques, the algorithms and what each is for.
12 minutes
Natural Language Processing (NLP)
Twelve minutes on what natural language processing is and how it came to work.
12 hours
Eleven hours of statistics and probability for data science, from first principles.
19 hours
Nineteen hours across SQL, Tableau, Power BI, Python, Excel and pandas — the analyst toolset in one course.
5 hours
Five hours on analysing data in Python, from loading a file to a finished chart.
4 hours
Four hours of data analysis in Python: NumPy, pandas, Matplotlib and Seaborn.
9 hours
Nine hours of React, taught step by step.
5 hours
Five hours of React for somebody who has never written any.
9 hours
Nine hours of React in one course, from the first component to a finished application.
23 hours
Generative AI for Data Science
Twenty-two hours on generative AI — the longest of these, and the most thorough.
12 hours
Generative AI for Data Science
Twelve hours across the generative AI stack, from the models to what is built with them.
10 hours
Ten hours of business analytics, from the statistics through to the dashboards.
24 hours
Twenty-four hours on the business analyst role itself — the process, not only the tooling.
10 hours
Ten hours of business analysis from the beginning, for somebody with no background in it.
12 hours
Eleven hours on the big data stack, from what the term means to the tools that do the work.
