I already mentioned the Hastie & Tibshirani course on statistical learning as one of my personal highlights in data science last year. My second highlight is also an online course, also by leading experts on their field (this time: Big Data and data mining), also based on a (freely available) book and also by Stanford University professors: Jure Leskovec, Anand Rajamaran and Jeff Ullman’s course on “Mining Massive Datasets”.
If you’re interested in data science or data mining, chances are high that you have already been in touch with their book. It can safely be considered a standard work on the fascinating intersection of data mining algorithms, machine learning and Big Data. The 7 week course is the online version of the Stanford courses CS246 and the earlier version of CS345A.
The course is very dense and covers a lot of territory from the book, for example:
- How does Map Reduce work and why is it important?
- How can I retrieve frequently appearing combinations from very large sets of items such as shopping baskets?
- How to retain information about a datastream that does not fit in memory?
- What are the most common tasks in supervised machine learning and how to implement them?
- How do I program an intelligent system for recommending movies?
- How to compute optimal placements of online advertisements?
Some of the lectures are on a beginners to intermediate level, but some lectures cover very advanced topics. What I especially liked about this course is that a lot of the material covered really is state-of-the-art in data mining. Some algorithms – e.g. the BIGCLAM community detection and CUR matrix decomposition – had only been developed about year ago.
So, take a look at the book, and if you haven’t already: enroll at the Coursera course website to make sure you won’t miss the next session of this course.
One thing that’s particularly great about the Internet is the Sharing Economy. So much information, know-how, content is given out for free on a daily basis. Here’s three fascinating unpublished books that you can take a look at right now. And to make them even greater, you can always give the authors your feedback, bugs you’ve discovered or just a big thank you!
The first book from O’Reilly’s Early Release series is “Mastering Bitcoin” by Andreas Antonopoulos. If you want to learn more about how the new crypto-currency works or if you want to imagine how this concept will change the world or just understand how you can use the Bitcoin APIs to build your own tools, this is the place to start. I hope this book will give me lots of inspiration about analyzing and visualizing the Blockchain (see this blogpost).
“Deep Learning” is the somewhat humble title of the second book. This work by Yoshua Bengio, Ian J. Goodfellow and Aaron Courville (University of Montréal) on the theory and practice of neural networks a.k.a. deep learning could someday become a standard introduction. On their webpage, you can download and read the book chapter by chapter – but as this is work in progress, there could be quite a lot of updates in the future. So grab it while it is still fresh.
The third one is already a classic and very well received by the peer group: “Network Science” by Albert-László Barabási. This book explains the science of networks and social network analysis from the beginning (history- and concept-wise) right to the 21. century. From finding and identifying Terrorists to analyzing and optimizing organizational structure – this book abounds with colorful examples and real applications. Everyone who has been thinking “Yeah, network visualizations look pretty nice, but what’s the real use-case besides that?” should definitely take a look at this work. The best thing: it will stay free because it’s published under a Creative Commons license. Thanks, László!