1 Why data science?
Data science is of of the most vibrating fields of research and industries at present. Its ubiquity and importance is likely on the rise. Due to its importance, it’s a great place for vivid minds to contribute and to develop. Joining this field allows to participate in of the leadings fields allows for personal growth and give access to a vibrant community. Of course, there are other highly exciting fields too!
2 Free resources overview
Here is a short, non-complete, subjective, opinionated curation of worthwhile free resources for those wanting to become a data practitioner, i.e. “data scientist”.
2.1 Machine learning conceps
- An Introduction to Statistical Learning, widely received and acknowledged book, free pdf version of book available
2.2 Math basics
- Mathematics for Machine Learning, concisse overview von math foundations of machine learning, free pdf version of book available
2.3 R basics
- R for data science, standard book on how to use R for data analysis and more advanced aspects such as modeling, free online book
2.4 Machine learning framework with R
- Tidy Modeling with R, the latest attempt to providing a comprehensive machine learning framework for R, built by RStudio
- Here’s the tidymodels website, providing some overview on the framework. However, the book may provide an even more comprohensive journey.
2.5 R online environment
- RStudio Cloud, offers free (up to some hours per month) access to a convenient R Studio online environment
2.6 Online course
- Stanford Machine Learning Coursera Course offers a good introduction by popular teachers free of charge
- Coursera Data Science for Executives Specialization is aimed partiucularly at executives and leaders. This course spans four (busy) weeks. Note that this course offers a free 7-day test phase, but is not free of charge afterwards albeit affordably (39$ for the course, at leaast last time I checked).
2.7 Beautiful intuition
Intuition on decision trees, visualized in a stunning, simply beuatiful way, part 1 of a 2 elements series
Model Tuning and the Bias-Variance Tradeoff, part 2 of a 2 elements series, vividly illustrates the pivotal concept of over- (and under-) fitting in machine learning
2.8 Blogs
- There are heaps of great blogs around, but I particularly like Julia Silge’s blog on machine learning using the Tidy Models framework.
2.9 Help
- The canocical site to receive help is, of course, StackOverflow.com. Make sure you understand how to post a reproducible, minimal example of your problem before you proceed, and you will be rewarded by a great community, all for free.
2.10 YouTube channels
Again, there are plenty channels around. Here’s one opinionated pick: Again, Julia Silge’s channel is a great place to start, and it coalesces nicely with her blog.
Just for the beauty of it, and, well the deep wisdom incorporated, plus the stunning beauty of its visualization, I feel it’s kind of mandatory to having visited 3blue1brown’s channel for an overview of math related to machine learning.