Every course from every provider we track, with the same filters over all of them: level, length, price, certificate and who stands behind it.
263 courses for “Complete Excel Tutorial for Data Analysis in 4 Hours (with FREE Files)”
9 hours
Nine hours of React in one course, from the first component to a finished application.
10 hours
Generative AI for Data Science
Nine and a half hours explaining generative AI for somebody starting from nothing.
New courses, a couple of emails a month. One click unsubscribes.
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.
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.
5 hours
Five hours of SQL for analytics in one sitting.
3 hours
Eleven lessons on MySQL, from the first SELECT to the joins and aggregates an analyst uses daily.
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.
24 hours
Twenty-four hours of business analytics: the statistics, the tools and the reporting.
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.
3 hours
Exploratory Data Analysis (EDA)
Three hours on looking at a dataset properly before modelling it, and on the features built from what you find.
4 hours
Three and a half hours of OpenCV in Python, from reading an image to detecting faces.
7 hours
Seven hours building neural networks in TensorFlow 2, for people who have not built one before.
9 hours
Eight and a half hours on deep learning, from neural networks to the frameworks that train them.
10 hours
Ten hours of deep learning from Edureka, from the neural network up.
1 hour
An hour on descriptive statistics: the measures that summarise a dataset before any model touches it.
