Expanding on my previous comment (https://news.ycombinator.com/item?id=8444705 https://news.ycombinator.com/item?id=8444705), here are some excellent, tried-and

Expanding on my previous comment (https://news.ycombinator.com/item?id=8444705 https://news.ycombinator.com/item?id=8444705), here are some excellent, tried-and-tested resources to help you learn the fundamentals. 1. Probability and stats: For probability, work through John Tsitsiklis's MIT OCW course 6.041. There is also an archived version of the course available on EDX. Norman Matloff's book (http://heather.cs.ucdavis.edu/~matloff/132/PLN/ProbStatBook.pdf http://heather.cs.ucdavis.edu/~matloff/132/PLN/ProbStatBook....) approaches stats from a CS practitioner's point of view (lots of sample simulation code in R), and has intuitive explanations of many different statistical concepts. For Hypothesis testing, checkout the courses Mathematical Biostatistics - 1 and 2 on Coursera. 2. Linear Algebra: Gilbert Strang's OCW lectures / course notes is hands down the best resource. But depending on your mathematical background, the lectures can be a bit long-winded. 3. Programming proficiency: For algorithms, Robert Sedgewick's algorithms course on Coursera has excellent videos. His textbook is very well written too. I really like his style of writing actual working code for each algorithm he describes (in contrast to say, CLRS): i find his code samples rather elegant and worthy of study. For learning object orientation and design patterns, the best way is to write large programs and read the book "Head First Design Patterns" as you do so. The aim is to develop an ability to think with a statistical / algorithmic lens. Many real world phenomena are stochastic in nature. Modelling them by simulation and measuring and drawing confidence intervals around your measurements must become second nature. When confronted with an image / text processing problem, representing you data as vectors and manipulating them must be natural. While writing large programs, being aware of design considerations such as preferring composition must come naturally to you. Of course, you should be able to always recognize when a computation is O(n^2) vs O(n log(n)) vs O(2^n). Once you are done with that, all you need to do is work through Andrew Ng's Machine Learning course and compete in a few Kaggle competitions / build some ML related side projects. Congratulations, you are now a data scientist good enough to work at most places in the tech industry!;)