Everything Data Science

In this video I will give you the resources you need to learn data science from zero knowledge. We will discuss several programming books and math books that are perfect for beginners who want to acquire the skills to become a data scientist. In particular we will look at books on R, Python, Calculus, Linear Algebra, and Statistics. Several more advanced books are also presented in this video. Do you have any other book recommendations for learning Data Science? If so, please leave a comment below.

Programming Books

Calculus Books

Linear Algebra Books

Statistics Books for Beginners

Mathematical Statistics Books

Advanced/Specialty Statistics Books

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[slhermit]

Before doing all these studies, pick a field where you would like to work as a data scientist. That simplifies a lot of things. For instance, you can only focus on learning specific statistical methods, learn about data in your chosen field, master one programming language + SQL, and learn about cloud computing basics.
Let s say that you like to be a data scientist in finance. Then learn statistics relevant to finance, SAS, advanced Excel, SQL.
Let's say that you like to be a data scientist in biotech. Then you need a biotech-related PhD + cloud computing + R or Python programming, SQL
Let's say that you like to be data scientist focused on Analytics (like most META hiring), you need to learn very basic statistics, SQL, knowledge about products by that particular company.
Once you find a job,
Keep learning, research on real-world problem solving.

[chase]

Doing a theoretical math BS with a minor in comp sci and I'm heavily considering data science for grad school. The perfect video to watch!

[federicosilva]

Great resources! I loved the fact that you included textbooks that require proofs. For me, the type of math taught at engineering schools is what I'd call (in an analogy to software testing) "black-box math": you know how to do computations, you know what the theorems are used for, but you don't get to see the "code", the logical structure that makes all these theorems actually true. I prefer "white-box math", even if it's a lot harder, it's a lot more rewarding at the end of the day, and you end up having a more profound understanding of how and why things work.

[jamesedward]

Thank you Sorcerer for your continued prodigious output of Mathematics information sources. You are a truly valuable resource for those of us trying to learn this stuff on our own. Your enthusiasm is infectious. Well done and Kudos.

[PavloFesenko]

I also wanted to recommend "Introduction to the New Statistics: Estimation, Open Science, and Beyond" by Prof. Geoff Cumming (2nd edition coming in 2023). Prof. Cumming really explains very well the predominant importance of confidence intervals and effect sizes as opposed to only null hypothesis significance testing. 😉

[nails]

Highly recommend Introduction to Statistical Learning by James, Witten and Hastie. It is a clear and thorough exposition of the bias variance tradeoff as well as a variety of common models.

[mkwarlock]

Great stuff! I'm currently pursuing a master's degree in data science, and learning a ton of mathematics. This semester I'm enrolled in linear algebra, applied statistics, and graph theory.

[thefourthbrotherkaramazov]

This is what I'm talking about! CS and Math major here. Love the merging between analysis, probability, and data science. My strongest opinion on the books there since I've only read a couple is that Gilbert Strang's Linear Algebra and Its Applications is amazing for a second course in linear algebra and is well suited applied mathematics.

[stevenkies]

Doing a Data Science boot-camp to follow up a Cognitive Science Ph.D. This is a great resourse. Thanks!

[SimonSolves_Math]

Data scientist is one of the careers I’m looking to become, but I’m also interested in becoming a mathematician or math professor. Thanks for the books!

[thepersonperson]

Python and R (Programming Languages) 0:43
Calculus 2:00
Linear Algebra 3:36
Statistics 6:56
Specialized Books 9:52

[JesseMaurais]

My only addition would be a book on design patterns for software development. It helps particularly when you are going to be working in the same code for a long period of time or with a larger team of people. But the choice of book here is going to depend on the language you are working in. Otherwise great picks. The stats book with Mendenhall, Wackerly, and Schaeffer is also my first reference book.

[aflyingtoaster]

It's my first year in university studying data science, and i can tell u this the best video that explains what are the "must know" for a data scientist, and also i appreciate all the books review videos, they're just amazing

[interstellar]

Can we get 'Everything Computer Science' next? Thanks for all the amazing content!

[hellfishii]

As a Data Science undergrad I can say this is a fantastic and comprehesive overview of the matterial we study, great stuff! keep it up!

[bargainbincatgirl]

Some other recommendations to add, but it could be a little over the top, are books of machine learning. Pretty good free books, that can be downloaded free (and legally) are "Elements of Statistical Learning" of Hastie, Tibshirani and Friedman: this one requires advanced mathematics, similar to the requirements you mentioned for the Mathematical Statistics book, and is THE book to learn machine learning.
But there are other book of the same authors, "An Introduction to Statistical Learning" of Witten, James, Tibshirani and Hastie that tries to be more about application of machine learning with less mathematical deep. Still is really good

[Anonymous-qw]

It is amazing how many of the textbooks I used for my Mathematics Bsc(Hons) from the 1980s are still used.
I used both the Gilbert Strang Linear Algebra with Applications and the Seymour Lipschultz Schaum outline Linear Algebra. For Probability and Statistics I used Introductory Probability and Statistical Applications by Paul L Meyer and Introduction to the Theory of Statistics by Alexander M Mood, Franklin A Graybill and Duane C Boes.
Although not my favourite at university, because I worked in the Banking industry afterwards Statistics proved to be one of the more useful subjects I learned at university.

[michaella]

I'm a biologist starting a data science master's in January. It's been a bit stressful trying to relearn calculus and statistics - i didn't do well when I took them for the first time. Your videos calm me down and give me hope! I know I can do it, it just takes a bit of time and practice. Thanks for your videos! I'm very glad to have found your channel.

[nickhill]

Norm Matloffs book on R is excellent.
I use both R and Python. I'd say that for all things stats related, I use R. I tend to use python for a lot of data pipelining and nlp.
The statistics procedures in python tend to be problematic.

I don't believe in language wars though. I use C/C++/fortran within r and python to speed up stuff as needed as well. I also keep SAS guides handy. They are excellent for understanding procedures and have paper references. It's fallen out of favor though.

'Design and analysis of experiments' is good to have. Great job including it. Not many people understand that topic.or the need for it.

I'd recommend knowing hierarchical modeling as well. Gelman and Hills book is one I would highly recommend.

Last thing I would like to make clear is that you would be a good data scientist if you don't think with your tools/math first. Tools are tools. Your job is to solve problems. In most cases, the reason for your job is to enable the employer to make or save money. Many data scientists think their job is an extension of grad school. So, they want to use the latest and greatest algorithm they read about. Great minds. But highly ineffective, who end up wasting their and everyone else's time. Putting things into use in a running machine like a complex business, is hard in itself. The more complicated your solution, the longer it will take to make it useful, it will be expensive to maintain, will need constant supervision, and leave everyone exhausted and exasperated. This is not trivial. Data Science courses popping up produce unusable talent because it's taught by people who have never done any real work.

[tmann]

So crazy because I was researching what I should learn in mathematics next! I’m an engineering major and I’ve taken Elementary Linear Algebra, Ordinary Differential equations, and Multivariable Calculus. I’m curious about statistics because I will take a statistics class for engineering and a math methods class for engineering. I do not want to stop learning math because I love it so much. Should I continue to learn about statistics or should I go down discrete mathematics, math proofs, real analysis ect. Maybe learn about PDEs or complex variables? It’s very confusing which one I should do or how a math subject is relevant to me and engineering. To be more specific, I’m a mechanical engineering major. I’ve been watching your channel since I went back to college and started taking college algebra! That was two years ago! Maybe a minor in math or statistics is in the works whenever i transfer to ASU a 4 year university. Thank you!