Defeating the Wrath of Math

I just made it through my first course at Lewis University: Math for Data Scientists. We went through an intensive eight week survey of calculus and linear algebra, which in addition to statistics form the core of mathematical skills needed to conduct data science. Whew!

As an economics student, I took algebra, pre-calculus, and calculus as core courses in college, but haven’t needed to calculate derivatives or integrals much in my daily work since then. I did, however, develop a major appreciation for their use, and it looks like I’ve finally found a field where optimizing functions would become a routine part of the work.

I never took a linear algebra course in my undergraduate work, and even though The Matrix is my favorite movie of all time, I had no idea how to actually work with vectors and matrices. While calculus and statistics seemed obvious for working with data and building models, linear algebra, once introduced to it, takes these mathematical skills to the next level. This is definitely something you’ll need to be ready to wrap your head around if you’re interested in data science!

One resource that I found extremely helpful was, literally, the No Bullshit Guide to Math and Physics, a compact textbook by Ivan Savov that quickly takes the reader through high school math (algebra, geometry, trigonometry) and into the core of physics and calculus with applications. I highly recommend it.

Another useful textbook was Linear Algebra, by Jim Hefferon. It’s freely available as a PDF, and the hardcopy edition is an affordable $20 on Amazon.com. It carefully walks the reader through the concepts and processes, without assuming much prior knowledge, and has a large number of exercises for practice (which is critical for truly solidifying the material).

Unfortunately it wasn’t available during my class, but I’m also looking forward to the No Bullshit Guide to Linear Algebra, which should be due out soon. If it’s anything like the Math and Physics guide, it should be a solid starting point. In fact, Savov was somehow able to condense the concepts into a four page primer, which you’ll want to keep on your desk in the meantime!

Finally, and still almost too good to be true, is Khan Academy, whose free videos and exercises are some of the best resources to learn and practice math skills, literally from arithmetic to linear algebra. I’ve made it a personal goal to complete the entire “mathematics universe” they cover.

Even if you don’t intend to get into a math-intensive field, I urge you to continue sharpening your skills in math, whatever your path. It’s an incredibly beautiful and intricate reflection of the complexity and connectivity of the world we live in, and an appreciation of numbers will enhance your life in unexpected ways.

For those of you who are intimidated or downright scared off by the sight of an equation, I recommend the book A Mind for Numbers, which is actually about optimizing your learning efforts, overall. There is a high quality companion course on Coursera that covers its contents called Learning How to Learn, as well. I took that as a warm up to help me succeed in grad school, and believe it could be valuable for any student, at any level. Highly recommended!

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