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Resources for Learning Data Science

The catalog of resources is endless, here are my recommendations.

Photo by Alex Knight on Unsplash
Photo by Alex Knight on Unsplash

There is a vast and growing number of Data Science resources. It can be hard to find the best ones for you. It may even be hard to find the right "Roadmap for Data Science" or "Top Skills to Learn for Data Science". I don’t claim to have the best resources or the correct path to a career in Data Science. What I have is a list of useful resources and if even one of them furthers your learning my goal is accomplished. So, enjoy the list and pick whatever suits your needs, I hope it gets you a step closer to your goals.

Podcasts

Photo by Austin Distel on Unsplash
Photo by Austin Distel on Unsplash

I want to start off with Podcasts, although they may not be the very best medium to learn Data Science. They do however provide very broad insights, give you a lot of ideas and something to listen to during your commute or whilst doing chores.

Towards Data Science Podcast

This Podcast is probably one of the best known out there and for good reason. The host does a fantastic job, the guests are interesting experts and the topics cover a wide range. I mainly use(d) this podcast for career advice and to discover new topics.

Data Crunch

Data Crunch offers bite-sized insights into how Data Science is shaping different industries. The episodes are relatively short (15–25min) I use(d) this podcast to understand the applications of Data Science.

DataFramed

The Podcast is provided by DataCamp and offers content similar to the Towards Data Science Podcast. Each episode also features small segments to explain certain things, e.g. distributions.

Machine Learning Guide

This series of episodes explores the topic of machine learning, it was one of the starting points of my journey. The host explains machine learning concepts in a very accessible way. I recommend it as a supplement to other resources when you get started with the topic.

Books

Photo by Sincerely Media on Unsplash
Photo by Sincerely Media on Unsplash

Python Data Science Handbook

If you are just getting started and want to learn how to use pandas, NumPy matplotlib, and Scikit-Learn efficiently Python Data Science Handbook is a great resource. Although I would argue that you can get started with Data Analysis quickly through a few videos or articles, this book provides a comprehensive in-depth tutorial for all of the most important packages for Data Science.

Hands-on Machine Learning

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is my go-to recommendation for anyone trying to understand and learn how to use Machine Learning. It provides a lot of code, enough theory to understand how and why algorithms work, and an end-to-end introductory project.

Data Science from Scratch

I recommend Data Science from Scratch: First Principles with Python by Joel Grus for anyone trying to dig a little deeper and wanting to understand how programming for Data Science and Machine Learning works under the hood. The book shows how problems can be solved by using almost pure python.

Mathematics for Machine Learning

This book takes a deep dive into the math behind the most popular machine learning algorithms. It starts with the basics of linear algebra, probability theory etc. and goes on to explain how the algorithms function mathematically. If you have an interest in going really deep and got curious about the math whilst reading e.g. Hands-on Machine Learning this is the book for you. It’s freely available under https://mml-book.github.io/. You can also find a paperback copy on Amazon.

Courses

Photo by Christopher Gower on Unsplash
Photo by Christopher Gower on Unsplash

Applied Data Science with Python

This specialization consists of five courses that cover data wrangling, visualization, machine learning, text mining, and network analysis through a hands-on approach. The exercises at the end of some modules can be a little challenging but they teach you quite a bit about solving day-to-day Data Science problems. If you are looking for a way to learn the data science basics with Python, this specialization provides one, there are however alternatives if you don’t like courses.

Machine Learning by Andrew Ng

You can’t really "avoid" a recommendation to Andrew Ng’s Machine Learning course on Coursera. For a reason. Although the course is a little dated and won’t teach you cutting-edge AI and Deep Learning techniques it is one of the best, if not the best introductions to Machine Learning. The course will introduce you to basic topics and common (shallow) learning algorithms over the course of 11 weeks (or less). Prof Ng will guide you through the theory and math behind these techniques.

fast.ai Practical Deep Learning for Coders

fast.ai’s course is probably the quickest way to jump into deep Learning. The course follows a top-down approach. You will be building up-to-date deep learning models in your first lesson. In later lessons, you will learn how the techniques work and which ones to use in practice. There is also a book available for free (as jupyter notebooks) or as a paperback copy.

Deep Learning by Andrew Ng

Another Course/Specialization by Andrew Ng, this one covers the theory and inner workings of the most common Deep Learning. I see it as a supplement to the fast.ai course which teaches in a more hands-on approach.

SQL

I rarely see recommendations for SQL Courses as many posts don’t touch on SQL at all. I think at least some SQL basics are necessary for Data Science. I used the Ultimate MySQL Bootcamp on Udemy to understand the basics of SQL, I think it provides a nice introduction, however there seems much more to know about SQL.

Blogs/Websites

Photo by NeONBRAND on Unsplash
Photo by NeONBRAND on Unsplash

There are many fantastic blogs and websites to learn about data science and stay up to date. I use Towards Data Science, Towards AI and other medium publications to learn and stay up to date.

Resources are one part of the equation, another big part is you, mainly your time and your effort. I hope some resources helped you on your journey. If you have others share them, so everyone can benefit! Good luck on your journey!

-Merlin


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