Coursera Statistics, Making Sense of Data: A applied Statistics course that teaches the complete pipeline of statistical analysis MIT: Statistical Thinking and Data Analysis: Introduction to probability, sampling, regression, common distributions, and inference. While R is the de facto standard for performing statistical analysis, it has quite a high learning curve and there are other areas of data science for which it is not well suited. To avoid learning a new language for a specific problem domain, we recommend trying to perform the exercises of these courses with Python and its numerous statistical libraries. You will find that much of the functionality of R can be replicated with NumPy, @SciPy, @Matplotlib, and @Python Data Analysis Library Books Well-written books can be a great reference (and supplement) to these courses, and also provide a more independent learning experience. These may be useful if you already have some knowledge of the subject or just need to fill in some gaps in your understanding: O'Reilly Think Stats: An Introduction to Probability and Statistics for Python programmers Introduction to Probability: Textbook for Berkeley's Stats 134 class, an introductory treatment of probability with complementary exercises.
So there you have it – 5 free eBooks (plus a bonus book) for your summer reading. It would be great if you would leave brief reviews of these books in the comments below – I'm sure all the authors would appreciate your comments and shares. Join the debate below and let me know your thoughts... About the Author Lee Baker is an award-winning software creator with a passion for turning data into a story. A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic '60s, it's amazing he turned out so normal! Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress - but 100 times the fun! He also wanted to be rich, famous and good looking.
Even though it's still hard to agree on a precise definition of data science or the role of a data scientist, the interest in the field keeps on rising: numerous blogs prescribe how to "really" learn data science, hot topics in forums such as Quora deal with discussions that relate to "becoming a data scientist". Naturally, these recommendations and discussions boil down to two essential questions: what is data science exactly and how can one learn it? Leaving the first question for what it is at the moment, DataCamp wanted to focus on the second one in this post. Because maybe right now, you don't have the need to hear yet another definition of what data science is and what it can mean to you. Maybe you want to learn about it and get your first job or to switch your career. You also don't want just another guide that lists 50 resources to check out. You want a list of resources you possibly haven't considered yet! With the popularity of the field comes a whole variety of recommendations from all sides: beginners as well as experts, all with different backgrounds, give their view on what it means to actually learn data science. In the end, considering all these resources and how they might fit your learning style is the key to learning data science. It's about puzzling together the existing resources and making them fit for you. That's why DataCamp presents to you the mystic square of data science learning resources: we already hand you some pieces of the puzzle that you can use to make your learning complete. The best thing about this mystic square is that it contains resources that you might not have considered. That means that the mystic square includes resources that are all complimentary to the ones that you have already encountered and registered to, as learning data science doesn't limit itself to just one resource. Even though the initial search interest for projects was already high to begin with, the demand for data science projects has been particularly high this year. Many users are looking to put their knowledge into practice or to advance their skills even further.