Data Science: Recommended Books for the Plebeian

I am finishing up my Data Science Immersive through General Assembly this week. First of all, what a wild ride. I am still not positive how I worked through it all - but I am on my way to finishing. Second of all, I am writing this post 2 days before our final is due at 1AM on a Saturday. Do with that information what you will, but if you are trusting me enough to read my words, I ask you to trust me enough to believe my book recommendations for the data science n00b, the pleb, the beginner.

I wanted to create a list of the helpful data science (DS, for those in the biz) books that will not only help you understand the technical aspects and coding, but also the inspiring part of data science-the creative ways to employ DS in the real world.

Without Further Ado:

1) My first recommendation is "Everybody Lies" by Seth Stephens-Davidowitz. This book is an exciting exploration of publicly available data from Google searches, and compares those searches to survey answers. One premise for his methodology is that people will google their true thoughts. Google has become our therapists, our doctors, our dictionaries, and more. Because of this, it is a goldmine into peoples' real thoughts - thoughts about sex, love, race, politics, and more. This book gave me inspiration before starting my class. Now that I am at the tail-end, I am reviewing concepts with an entirely new point of view. I highly recommend this book to see how data is used IRL!

2) My next recommendation is "Naked Statistics: Stripping the Dread from Data" by Charles Wheelan. This book is an awesome, simple review of common statistical concepts. Something great about the DS immersive environment is that you will explore complex statistical models. Saying things like "Machine Learning techniques" or "Principal Component Analysis" or "Natural Language Processing" are super intelligent-sounding, and great for resume's and cocktail parties. However, while I was learning these techniques, I sort of forgot the basics. Standard deviation, correlation, and probability are all super important for the entire course. I enjoyed reading this book to keep those concepts fresh in my mind.

3) My favorite instructor recommended "Build a Career in Data Science" by Emily Robinson and Jacqueline Nolis. It is a catchall when it comes to working in data science. I struggled a lot in the beginning of the course with the "data science process." I asked myself: "What are the steps? When do I do them? I have to go back to the first step...is that normal?" First of all - yes, the DS process is convoluted at times. But this book helps spell it out with a chapter titled "Making an Effective Analysis". It also has interviews with data scientists in the wild™️, tips on interviewing, resume-building, accepting an offer, becoming productive at your job...and more. I will be referring back to this book throughout my career. And it's written by women! #grlpwr.

5. One of the more put-together people in my class recommended "Learn Python the Hard Way" by Zed Shaw. I would literally trust this colleague with my life so I trust that this is a helpful book. I have a funny story about this . A long time ago I asked for help on social media for recommendations on learning SQL, another coding language I was familiar with early on in my career. Someone wrote "Learn SQL the hard way" on my post and I thought he was being really rude and told him that I didn't need his sarcasm. Someone else had to explain that it is a series of reference books for learning various coding languages. I haven't read them but I assume they are helpful in learning coding, as they have now been recommended to me twice.

6. My final book recommendation is LITERALLY ANY BOOK that will keep you sane. Whichever DS course you end up completing, at one point or another you will sigh and cry at the thought of thinking about statistics for one more second. If this is the case, I recommend any one of the "Harry Potter" series, the label of your favorite ice cream pint, or maybe you can just sit and stare at the wall for a few minutes.