Every course from every provider we track, with the same filters over all of them: level, length, price, certificate and who stands behind it.
440 courses for “Marketing and Sales Full Course in Hindi Playlist | #marketingcourse #salescourse”
12 minutes
Natural Language Processing (NLP)
Twelve minutes on what natural language processing is and how it came to work.
8 minutes
What independence means in probability, and what changes when events depend on each other.
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17 minutes
Sample spaces, events and the basic rules of probability, in seventeen minutes.
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.
10 hours
Generative AI for Data Science
Nine and a half hours explaining generative AI for somebody starting from nothing.
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 and a quarter hours of R for beginners, taught in Hindi.
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.
