Initially what I thought is that the best way to learn data science is through videos. But referring to these books along with videos is super beneficial. So let’s have a look at our amazing and comprehensive ebooks for learning Data Science.
Python Data Science Handbook:
So to begin with the first ebook of Data Science ebooks list that I like to mention is from O’Reilly. The book’s name is Python data science handbook. I’ll tell you what all things this wonderfully written book includes.
So first of all this is a very very good comprehensive guide for Python for data science. If you are very much interested in Python and aspire to become an expert in Python with respect to a fight then you want to implement some data science projects. So I think this book is absolutely perfect.
Secondly, you know it has wonderful quotes which were very very beneficial to me. This book covers all the important libraries as non Python does. The best part is the exploratory data analysis. It becomes more wonderful because the flow that continues after the exploratory data analysis to the machine learning algorithms actually includes a whole lot of things. Which encapsulates a whole idea.
Thirdly when I talk about machine learning it covers both the practical implementation of the libraries and how do they work. Moreover, you will see the advanced concepts like Python graphics libraries, some of the maps which include Seaborn, etc. So definitely I suggest this ebook as a great option for learning Data Science. It is available completely for free and online.
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Think Stats 2e:
The second among the Data Science ebooks that I would like to say is about Think Stats 2e. This Thing stats 2e by Alan B Downing is also an O’Reilly Edition. That means the publisher is O’Reilly.
The best part is that it discovers the practical overview of statistics for Data Science. What I have observed from this particular book is that it uses Data Set from the National Institute of Health. National Institute of Health with respect to various research that they have done they’ve collected those data set. You can use that data set to do a whole lot of things like expert data analysis.
When I specifically say with respect to statistics I think you should prefer this particular book. As it has done a lot of things. If I just see go and see the content the limits of PMF (probability mass function), Percentile, CDFs, representing CDFs, Percentile based Statistics, Random numbers, Modeling distribution, Exponential distribution, Normal distribution, Lognormal distribution, Pareto distribution, like everything is here. All the important scenes that are required actually to cover most of the things in feature engineering have been covered over here.
I suggest that if you have been looking for where should you learn statistics and how should you cover these topics. This book will suffice that. It properly covers each and every concept in statistics. Moreover, it has a lot of examples like Correlation, Covariance, Pearson Correlation, Nonlinear relationships, Spearman’s rank correlation, Coefficient & Causation.
It also covered a separate chapter called hypothesis testing. That is in chapter 9. Along with simple explanations, they have given a lot of examples. That will definitely make it easy for you to learn these things.
R For Data Science:
Now many people are also interested in learning the R programming language for Data Science. So this is the book that I have for them. Again this is from O’Reilly and the author if you see is Hadley Wickham and Garrett Grolemund.
If I just say about this particular book this is the go-to book for learning R programming Language. Above that, it’s available for free in the form of a PDF. It’s a comprehensive guide to do Data Science with R. It covers everything from Data Visualization, Data Manipulation, Data Modeling, Data Transformation, Exploratory Data Analysis, and so on. Additionally, projects are also available here. So you can actually go through this particular book. It will direct you even if you don’t have any amount of expertise in the R programming language.
Rules For Machine Learning:
My next ebook for you is Rules of Machine Learning: Best Practices for ML Engineering written by Martin Zinkevich.
This is also a wonderful document altogether. Primarily this document is intended to help those with a basic knowledge of machine learning. Especially to get the benefits of best practices and machine learning from around Google. This ebook presents a style for machine learning similar to the Google C++ style guide and very same function over here.
You can go and try to click each and every link given into that ebook and try to read. This book is just somewhere around 24 pages and it is a great resource. It covers very good practices that I have learned and has always been beneficial for me.
Now our last Data Science eBook is Deep Learning. It is from an MIT press book co-written by Ian Goodfellow and Yoshua Benglo and Aaron Courville.
Inside this, you’ll be able to see the particular content of Deep Learning. You should also try to read research papers with respect to deep learning then only you’d be getting a whole lot of ideas. I have referred to many research papers so because of that I’m good at deep learning with respect to theoretical concepts and practical implementations. So definitely you should read this ebook.
So these were our 5 Data Science eBooks, and trust me if you start with the Python Data Science Handbook you will already get a whole lot of knowledge about it.
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