THE Data Science VALUE Bomb

Stack of blocks symbolizing the skills necessary to become a data scientist, or someone capable of developing customer analytics
Understand your customers & market. Build a custom strategy. Execute and grow.

Are you interested in learning data science, machine learning, or basic analytics so you can become a better business owner, founder, or marketer?

If so, we’ve put together a massive list of learning resources, just for you!

Whether you want to become a data scientist or learn how to draw insights from your customer analytics through data, the information below will have something for you.

Are you interested in learning data science, machine learning, or basic analytics so you can become a better business owner, founder, or marketer?

If so, we’ve put together a massive list of learning resources, just for you!

Whether you want to become a data scientist or learn how to draw insights from your customer analytics through data, the information below will have something for you.

With that said, I am not one to recreate something when an adequate solution is available. Therefore, this article is largely a practical exercise in curation and geared toward providing as much VALUE to you, the reader, as possible.

This is the first of many lists of this kind, so follow, comment, and share while you absorb the information!


What is Data Science?

Chart showing the relationship between business value and analytics maturity
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Here is a list of places and descriptions which vary in depth and breadth, but they should get you started:

Quora thread “What is Data Science?” — There are some excellent answers in this link!

Check out Data Science Summarized in One Picture from Vincent Granville &

What is Data Science by Mike Loukides

Data Science via Wikipedia

What is Machine Learning?

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

Adam Geitgey

Adam Geitgey covers the description of Machine Learning brilliantly in this article.

Nick Heath’s article What is machine learning? Everything you need to know is an excellent article for anyone trying to get a good feel for the machine learning landscape.

Where to Begin

What is your goal in terms of data science?

What does success look like?

Everyone has motivations, goals, and expectations. It’s best to understand those before you begin.

With that said, I’m going to show you what I think is a sensible approach. If you have specific questions based on your situation, feel free to comment on this post and I’ll get back to you as soon as possible

I’m a bit partial, but I think K2 Data Science’s free Data Science foundations content is an excellent place to begin.

K2 is a Data Science boot camp, and I went through their program. This isn’t meant to be an advertisement for Ty Shaikh & K2, but I have immense respect for the program they’ve built.

Data Science, Data Boot Camp, Data Science Bootcamp, Analytics, Python, Machine Learning, ML, AI, Artificial Intelligence, Neural Networks, Data

K2’s foundation course is meant to be taken in preparation for their Data Science Bootcamp, which I’ll cover in a bit.

The course is built on open source content that provides you with the baseline skills needed to start a Data Science Bootcamp. The course content includes calculus, statistics, linear algebra, computer science, python, and SQL.

The whole piece can be knocked out in around a month if you dedicate the time to learn whichever sections you don’t already know.

If all of the content is new to you, it may take a little longer. Either way, when you’re done, you’ll be ready to begin learning how to become a Data Scientist.

Education Options

If you’re interested in furthering your skills, you will likely choose to either self-teach, go through a Data Science Bootcamp, or enter into a Master’s in Data Science Program.


  1. K2 Data Science: You’ve already heard my spiel. I loved my experience with their program & live mentors. A huge perk is that the course is completely remote & self-paced. Read Quora reviews for the program.
  2. Galvanize: I don’t know enough about their program to comment, but here are the Quora reviews.
  3.’s article, Best Data Science Bootcamps: The 2018 Comprehensive Guide. I don’t know anything about their ranking criteria or where they drew their data from, but the article has some helpful content.

Data Science Tutorials

I’d start with’s list of online tutorials and courses. Their list is voted on by members of their community and consists of paid and free courses.

Online Master’s Degrees in Data Science

I will provide a more thorough breakdown of the pros & cons of schools in a future article. For the time being, these are my top 4 from my research and prep from grad school.

In-person Master’s Degrees in Data Science

If you’re serious about data science and looking to go to school check out this list of schools offering Master’s Programs.

Websites for Data Science Information

Data Science Books & Book Sources

KDNuggets: 10 Free Must-Read Books for Machine Learning and Data Science

Image of O'Reilly Safari logo - an excellent source for technical books
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O’Reilly: Their learning platform, Safari, is incredible. It has books, videos, and plenty of other resources. The one downside is that it costs around $40/mo for a membership. If you sign-up, there is a free trial period to test it out. is my favorite of all of these, but that is just preference.
Mapt has thousands of books and videos for virtually every tech topic you can imagine. They have books on machine learning, crypto, technical design, and the list goes on.

Even better, Mapt has curated tracks for different career paths like the following: Machine Learning EngineerPython Data Scientist.

The curated paths make the process easier to maintain course while trying to improve. Once again, this cost $20–30/month depending on your plan.

Another Source:

I have around 60 Data Science, Machine Learning, NLP, and Computer Vision books that I’m willing to share with anyone looking to expand their knowledge-base. Reach out to me or post in the comments if you are looking for something in particular.

Some of my personal favorites for beginners:

  • Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett
  • Numsense: Data Science for the Layman (no math added) by Annalyn Ng and Kenneth Soo.
  • The Art of Data Science: A Guide for Anyone Who Works with Data by Roger Peng (Author) and Elizabeth Matsui (Contributor)

Data Science Influencers

  • Vincent Granville: The guy puts out high-quality content on Data Science Central… EVERY DAY. His LinkedIn feed is jammed with value. Twitter
  • Kirk Borne: His Twitter feed is stupid valuable. I stalk Kirk on Twitter & LinkedIn for information. I suggest you do the same.
  • Andrew Ng: His Machine Learning courses at Stanford are the stuff of legend & also free on Youtube. Andrew is also active on Medium, as well as Twitter. Andrew is a co-founder of Coursera, a professor at Stanford, and all-around genius. Here’s his personal site if you can’t get enough of Andrew.
Andrew Ng’s Machine Learning Lectures
  • KDNuggets — Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated. This is a pretty solid list but still lacks Vincent. That may be due to his site being a competitor to KDNuggets.

Machine Learning & Other Helpful Content Series

Adam Geitgey’s Machine Learning is Fun! Series

Adam’s produces some of my favorite content to read, review, and share with people. Any new Data Scientist would be smart to at least skim through his articles.

  • Part 1The world’s easiest introduction to Machine Learning 
  • Part 2: Using Machine Learning to generate Super Mario Maker levels
  • Part 3: Deep Learning and Convolutional Neural Networks
  • Part 4: Modern Face Recognition with Deep Learning
  • Part 5: Language Translation with Deep Learning and the Magic of Sequence
  • Part 6: How to do Speech Recognition with Deep Learning
  • Part 7: Abusing Generative Adversarial Networks to Make 8-bit Pixel Art
  • Part 8: How to Intentionally Trick Neural Networks

First Round article by Instacart VP of Data Science, Jeremy Stanley

Mike Tamir’s Machine Learning Blueprint Series

Burak Guzel wrote the SQL for Beginners series

Thank you for reading, skimming, clicking, sharing, & commenting!

Don’t see what you want or need? Let me know in the comment section below!

Be sure to check back, because we’ll continue to update and improve upon this list of resources.

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