Learn Data Science in 2021: Books, Blogs, YouTube channels & More
A comprehensive selection of sources.
You heard it before: we are what information we consume. That’s why when it comes to acquiring new knowledge or just following the latest trends, it is essential to be selective with the choice of sources to get information.
As a curious person and practitioner in the field of AI, ML, and Data Science, I never stop my research, and I see myriads of blogs everyday. So, I decided to gather the most insightful and valuable stuff (as I see it) in one place to help others in their growth.
You will not find mainstream stuff here or something everyone already knows. Here are my top picks of fresh material and content you can read, watch, or listen to. So let’s start our adventure!
Disclaimer: There are no affiliate links in this article. This article is for information purposes only.
New Dope Books on Data Science
Reading books is the most subtle and profound way to excel with your skills in data science. I think you know about some classic stuff like Naked Statistics by Charles Wheelan, or Deep Learning by Ian Goodfellow. They are old but gold, of course. But what about something more fresh published recently?
More often than not, it is challenging to envision whether a new book will be useful, especially if you haven’t heard anything about it. Let me share my insights and impressions regarding some of the newest stuff, so you can get a better idea of what will give you valuable pieces of knowledge.
#1 Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects
Authors: Neal Fishman, Cole Stryker, Grady Booch
Published in: 2020
For whom: Good for beginners, IT business owners, and analysts who want but don’t know how to expand data science capabilities.
Why worth reading: You will get a fresh eye and possibly some new insights on the usage of data science for business needs.
Moreover, you will look behind the curtain of data science and statistics-related concepts ( collecting, organizing, analyzing data) with clear explanations.
#2 The Atlas for the Aspiring Network Scientist
Author: Michele Coscia
Published in: 2021
For whom: For data scientists with an entry or middle level of proficiency. It can be beneficial to learn how to represent high-dimensional data in the form of a network.
Why worth reading: The most comprehensive way to step into pure network science, which is needed for procedures connected with network-based analysis and Big data.
After reading this book, you will have a solid understanding of stuff like neural connections, graphs, nodes, and more.
#3 Build a Career in Data Science
Authors: Emily Robinson, Jacqueline Nolis
Published in: 2020
For whom: For everyone who is just starting their path in data science and wants to move faster on the career ladder.
Why worth reading: I liked this book because it is one of a few that managed to shed light on things beyond the technical side of data science.
Such aspects as how to land your first job or how to be a manager within a data science project — all this stuff is important, and this book is perfect for figuring them out.
#4 Essential Math for Data Science Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics
Authors: Neal Fishman, Cole Stryker, Grady Booch
Published in: 2020
For whom: For a data scientist who lacks (or wants to improve) a math or scientific background.
Why worth reading: This long title speaks from itself, this book covers all math terms you need to know. After all, how can we live without math in the data science world?
Besides, you’ll also find nice explanations on what’s under the hood of the algorithms.
#5 Python Data Science. The Ultimate Crash Course for Beginners. Learn Python in a Week and Master It.
Authorship: Press Computer Programming
Published in: 2020
For whom: For data scientists who look for the shortest way to step into programming.
Why worth reading: This is a brand new crash course that promises to teach you Python in less than a week. Well, I will suggest not to believe in such statements, but there is obviously something useful in this book.
It is great to figure out all the fundamentals, browse through the methods, and go through all the needed stuff to organize unstructured data sets with Python.
#6 A Hands-On Introduction to Data Science
Author: Chirag Shah
Published in: 2020
For whom: beginners
Why worth reading: One more good book that covers complex terms on data science in plain language. It flows from the very basics of data science all the way to machine learning concepts. Other than that, it gives hands-on examples in multiple different tools such as Python, R, MySQL, etc.
You can also find there great supplements like datasets, chapter slides, sample exams, and more.
#7 Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions
Author: Matt Taddy
Published in: 2019
For whom: beginners
Why worth reading: I find this book a quite non-standard way to break into the field of data science. All this because it groups topics considering their impact on the real business environment. So, as for me, this is definitely a must-have in your bookshelf if you want to keep a broad horizon about the application of data science for a limited amount of time.
Data Science Writers I will definitely suggest to follow
One more way to stay up-to-date with the scientific community is to follow Data Science practitioners, especially here on Medium. I guess you are already familiar with some major publications like Towards Data Science, or Towards AI, where you can read dozens of new articles every day. But, let me share more specific authors worth following.
#1 Rebbeca Vickery
Medium: @rebecca.vickery
My favorite post: A Simple Guide to Scikit-learn Pipelines
Why to follow: Rebbeca is a self-taught Data Scientist who is not afraid to share her knowledge with others. Most importantly, she makes clear and quite deep guides, where she explains mathematical and statistical topics in plain language. She also gives a lot of advice on how to write about Data Science, and in that sense, she is a real pro you can look up to.
#2 Eryk Lewison
Medium: @eryk.lewinson
My favorite post: Algorithmic trading based on Technical Analysis in Python
Why to follow: Eryk Lewinson is another writer on Medium whose posts regarding Data Science I appreciate a lot. Apart from the usual stuff like tips for using Python, he also gives nice recommendations on such rare-revealed topics like automatic trading or finance problems and how they relate to Data Science.
#3 Cassie Kozyrkov
Medium: https://kozyrkov.medium.com/
My favorite post: How do A/B tests work?
Why to follow: Cassie is Head of Decision Intelligence in Google, who shares her visions regarding Data Science, ML, and AI with everyone on Medium. Many say she is a real legend, and I can not disagree with this. She gives brilliant overviews on complex topics like classification, regression, prediction, A/B tests, p-value, and more.
#4 Datafloq
Run By: Mark Van Rijmenam
Website link: Datafloq.com
Why to follow: Another blog that I want to mention on this list is Datafloq, which is the whole platform with articles and insights. It mainly focuses on big data’s business aspects and how to make data science work for organizations. Plus, it provides additional opportunities through job postings, vendors, events, and training.
YouTube bloggers talking about Data Science
You may agree, YouTube is the most interesting and non-boring way to learn anything. Hopefully, there are lots of great people involved in Data Science who share their insights there. Here are some of them.
My favorite video: What REALLY is Data Science? Told by a Data Scientist
Why follow: The first person, who came into my mind, is, of course, Joma Tech, who shares top-quality learning materials and some entertaining fun videos. He has tremendous experience working with statistics, data science, Coding languages like Python, so he provides brilliant visions and advice. His enthusiasm towards coding and computer science will also make you more motivated for the next one.
My favorite video: 10 tips for learning PYTHON fast! Master Python in 2021!
Why follow: Another great and constantly evolving channel run by Giles McMullen since, for a minute, 2008. Here you will find cool and short reviews of books on data science and machine learning. You can find videos related to Python, data analytics, machine learning, and much more on his channel.
What I like the most about Giles’ channel is that he gives a lot of advice from his own experience, and the way he explains something is quite clear and always original.
My favorite video: Why do prime numbers make these spirals?
Why follow: The owner of this channel is Grant Sanderson who graduated from Stanford University in 2015 with a bachelor’s degree in math. His videos cover topics like Linear Algebra, Calculus, Differential equations, and Neural Networks. He views problems with a different unconventional perspective.
My favorite video: Deep Learning with Python, TensorFlow, and Keras tutorial
Why follow: Harrison is also a founder of multiple businesses, all of which leverage the Python programming language. He offers some of the best content related to python programming, and tutorials on data science.
Podcasts on Data Science: Why not?
Podcasts are a great way to immerse yourself in an industry quickly while doing other stuff (like walking in the street or working out). Here are my picks which I find engaging and informative that will help you to keep up with all the trends.
Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM
Why listen: You will find here an outstanding explanation of complicated data science topics, like natural language processing, or k-means clustering. This podcast is hosted by computer science and AI experts — Kyle Polich and Linh Da Tran. Generally, it has a wide appeal for both complete beginners and technically skilled dudes.
Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM
Why listen: It is a good-humored podcast run by Katie Malone and Ben Jaffe who cover recent developments in data science, machine learning, and artificial intelligence. They do a great job at demystifying a complex technical topic and strip it down to its fundamentals. For instance, just a few short minutes, they demystify neural networks, autoencoders, the Fourier transform, and more.
Listen: Apple | Spotify | Learn More
Why listen: This is a brand new biweekly podcast that focuses on news and trends from the data science ecosystem. So far, they’ve discussed topics including ethical AI and biased data.
Listen: RSS ⋅ iTunes ⋅ Player FM
Why listen: Last but definitely not least, we get to Talking Machines. As the title suggests, it is very machine learning-centered. In fact, it is great for getting in-depth analysis on machine learning techniques.
Final Words: Knowledge is power
As Andrew Yang said: Data is the new oil. Data science is evolving rapidly, and data is worth much more than most people think. The data we generate gets monetized in myriad ways, and people don’t really care about giving their data away for free in exchange for using, for example, TikTok for free.
All this means we should be more aware of data processes and manipulations with data. Solid knowledge of data science is a key thing now, so don’t lose any second and use the moment of ‘now’ to the fullest to learn new stuff. But most importantly, be selective with what you choose to attain knowledge.
P.S.
If you somehow reach my new profile again (or for the first time), please support my efforts and follow me to read more dope articles later. In case you enjoy some of my previous articles and you want to use them again, visit my Linkedin (hopefully, everything is totally safe there). Here are some of my previous guides (for total beginners) which come in handy for many:
- Deep Learning Algorithms — The Complete Guide
- A Gentle Intro to Probability and Statistics for Data Science
- Who Is a Data Engineer & How to Become a Data Engineer?
- A Beginner’s Guide to Python for Data Science
- A Beginner’s Guide To Data Science
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