Kirsten Kehrer from
datamovesme.com does all kinds of super valuable stuff in SQL and end of 2019 she decided to share it with the world via a free SQL course.
Screenshot from youtube.com/watch?v=W5AiLYR02l8
The course is focused on data science and has 5 modules with video lectures:
Kirsten advises to take the course with dual monitors, as she also provides an
, where you can write your queries during the videos online SQL query builder environment .
Moreover, Kirsten also published the slides and the code to go with the course, so you can really learn along:
A nice touch is that Kirsten simulated some data for a fictitious e-commerce company, that really allows you to get a feel for the type of data you’d be working with in practice:
Screenshot from youtube.com/watch?v=itpvD0Eb_s4
There are multiple unread e-mails in my inbox.
Links to books.
Just sitting there. Waiting to be opened, read. For months already.
The sender, you ask? Me. Paul van der Laken.
A nuisance that guy, I tell you. He keeps sending me reminders, of stuff to do, books to read. Books he’s sure a more productive me would enjoy.
Now, I could wipe my inbox. Be done with it. But I don’t wan’t to lose this digital to-do list… Perhaps I should put them here instead. So you can help me read them!
Each of the below links represents a formidable book on programming! (I hear) And there are free versions! Have a quick peek. A peek won’t hurt you:
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Applied Predictive Modelling – by Max Kuhn & Kjell Johnson Feature Engineering and Selection: A Practical Approach for Predictive Models – by Max Kuhn & Kjell Johnson The Pragmatic Programmer – by Andrew Hunt & David Thomas Clean Code – by Robert Martin R for Data Science – by Hadley Wickham
Advanced R – by Hadley Wickham
R Markdown: The Definitive Guide – by Yihui Xie, J. J. Allaire, & Garrett Grolemund Bookdown: Authoring Books and Technical Documents with R Markdown – by Yihui Xie blogdown – by Yihui Xie The Hundred Page Machine Learning Book – by Andriy Burkov An Introduction to Statistical Learning – by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani
The Elements of Statistical Learning – by Trevor Hastie, Robert Tibshirani, & Jerone Friedman
Interpretable Machine Learning – by Christoph Molnar Deep Learning – by Ian Goodfellow, Yoshua Bengio, & Aaron Courville Deep Learning with Python – by Francois Chollet Pro Git – by Scott Chacon & Ben Straub
The books listed above have a publicly accessible version linked. Some are legitimate. Other links are somewhat shady.
If you feel like you learned something from reading one of the books (which you surely will), please buy a hardcopy version. Or an e-book. At the very least, reach out to the author and share what you appreciated in his/her work. It takes valuable time to write a book, and we should encourage and cherish those who take that time.
For more books on R programming, check out my
R resources overview.
For books on data analytics and (behavioural) psychology in (HR) management, check out
Books for the modern data-driven HR professional.