As a Data Scientist, I’ve been asked this question – “Where can you find free resources for learning Data Science?” Many times. There are many resources out there for beginners and advanced learners but it is difficult to know which one will suit you best. So if you go search on Google or YouTube, you may end up spending whole day sifting through outdated articles and tutorials that either too complicated to understand or just won’t work! And trust me; we don’t want to miss out the awesome job opportunities by asking irrelevant questions during an interview 🙂 The first step towards becoming a Data Scientist knows exactly what and where to study and practice.
This article should help you decide the best path forward as a beginner who wants to become a Data Scientist.
Let’s begin! To make it easier to digest, I divided all data science learning materials (books, courses etc) into groups which you can jump directly to by clicking on the links below:
1. Best FREE Books to Learn Data Science 2. Best Paid Books 3. Free Data Science MOOCs 4. Best FREE Courses 5. Blogs & Tutorials 6. Free Cheat sheets 7. Data Sources 1) Best FREE Books to learn Data Science It is always advisable that you should read books written by professionals who have 5-10 years of experience doing what they are writing about. And yes, there are several books written by Data Scientists about data science.
Here are some of the best ones I have read so far –
1. This is one of the well-written books for beginners who want to learn to use R as a Data Science tool!
The book assumes no prior knowledge in Statistics or programming and even includes interactive tutorials that you can run on your local computer to follow along with the author’s examples. This gives you an opportunity to work alongside this book without necessarily installing R Studio! Now, what makes it stand out from other books is – It starts with basic statistics which lay a strong foundation required to understand machine learning concepts It follows step-by-step instructions on how to create various plots needed for building predictive models It covers machine learning models in-depth with maximum focus on Scikit-Learn It has a separate chapter for deep learning about how many megabytes in a gigabyte which is not covered in many books!
2. Pattern Recognition and Machine Learning by Christopher M Bishop
This is one of the most comprehensive books written about machine learning, pattern recognition and statistical inference which you will find out there! If you are looking to learn more about data science without any prior experience, this is the only book you need to read – it covers topics from basic statistics to advanced machine learning concepts along with practical codes in R. You can easily follow up this book even if your programming skills are not that good because author explains everything in an informal yet clear manner. The best part I about this book was its clarity in defining new terminologies and concepts which is an important factor for beginners. I learnt a lot from this book and would highly recommend reading it if you want to become a Data Scientist!
3. Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville
If you are looking to learn deep learning as quickly as possible, then this is the only book you will need to read. It covers all aspects of deep learning such as – basic machine learning concepts, how regularization works in machine learning models, designing different neural networks with practical codes using Theano library and also goes into important layers such as Convolutional Layers and Recurrent Layers with real-world examples. What makes this book stand out from other books on deep learning is – explains everything using simple language, easy to follow codes and real-world examples. The exercises at the end of each chapter are quite challenging which will help you master the concepts learned in this book!
4. Machine Learning Yearning by Andrew Ng
This is one of my most favorite books on machine learning written by the father of machine learning himself; Andrew Ng! It is suitable for beginners who want to learn machine learning without any prior experience in coding or mathematics. The author covers all important topics like supervised vs unsupervised learning, several types of regression (Linear Regression, Ridge Regression etc) and classification methods along with practical codes using Python libraries such as NumPy, SciPy & Matplotlib.
Reading these four books will give you a complete understanding of machine learning concepts, programming in Python and R along with statistics. Now the key is – how you use them to solve real life problems!