Every course from every provider we track, with the same filters over all of them: level, length, price, certificate and who stands behind it.
313 courses for “Wordpress Tutorial for Beginners (2026) | Step-by-Step Full”
45 minutes
Forty-five minutes of scikit-learn, from fitting a model to scoring it.
10 hours
Ten hours of data mining: the techniques, the algorithms and what each is for.
New courses, a couple of emails a month. One click unsubscribes.
1 hour
Natural Language Processing (NLP)
An hour on the step every NLP pipeline starts with: turning text into something a model can read.
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.
2 hours
Two hours of R, taught as what it is: a language built for statistics.
2 hours
Two and a quarter hours of R for beginners, taught in Hindi.
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.
2 hours
Exploratory Data Analysis (EDA)
Two and a quarter hours of exploratory data analysis in Python, taught in Hindi.
4 hours
Three and a half hours of OpenCV in Python, from reading an image to detecting faces.
9 hours
Eight and a half hours on deep learning, from neural networks to the frameworks that train them.
