Math for Machine Learning: Top Math Resources for Data Scientists

What Math Skills do Data Scientists Need Forms of the question “what math do I need for data science” and “what math do I need for machine learning” are popular on sites like Quora..I would encourage all aspiring data scientists to perform their own research on this subject and not to take my post as gospel..However, as I often get asked for my opinion on what math aspiring data scientists need to know/study, I will provide my own list: Basic statistics and probability (e.g., normal and student’s t distributions, confidence intervals, t-tests of significance, p-values, etc.)..Linear algebra (e.g., eigenvectors) Single variable calculus (e.g., minimization/maximization using derivatives)..Multivariate calculus (e.g., minimization/maximization with gradients)..Please note that the above is not an exhaustive list..To be honest, you likely can never know enough math to help you as a data scientist..What I would argue is the above list represents the 80/20 rule – the 20% of math that you will use 80% of the time as a practicing data scientist..A List of Top Math Resources Here’s my list of the top 80/20 math resources for aspiring data scientists: The Cartoon Guide to Statistics is one of the books we provide to our bootcamp students and it is an excellent resource for gently learning – or refreshing – your statistics knowledge.. It covers many of the basic concepts in statistics in easy-to-consume and an entertaining fashion..Well worth a read..Coursera’s Statistics with R Specialization is a must for every aspiring data scientist.. More details

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