Top Data Science Books for Beginners and Advanced Data Scientist
Last updated on 28th Sep 2020, Artciles, Blog
Apart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more. There will be enough data science jobs that can fetch you a handsome salary as well as opportunities to grow.
That said, there is nothing better than reading data science books to get the ball rolling.Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more.
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Data Science Books
Here are some of the best books that you can read to better understand the concepts of data science
1. Head First Statistics: A Brain-Friendly Guide
Just like other books of Headfirst, the tone of this book is friendly and conversational and the best book for data science to start with. The book covers a lot of statistics starting with descriptive statistics – mean, median, mode, standard deviation – and then go on to probability and inferential statistics like correlation, regression, etc… If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner. There are a lot of pictures and graphics and bits on the sides that are easy to remember. You can find some good real-life examples to keep you hooked on to the book. Overall a great book to begin your data science journey.
2. Practical Statistics for Data Scientists
- If you are a beginner, this book will give you a good overview of all the concepts that you need to learn to master data science. The book is not too detailed but gives good enough information about all the high-level concepts like randomization, sampling, distribution, sample bias, etc… Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. The book also surprises one with a survey of ML models.
- This book covers all the topics that are needed for data science. It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.
3. Introduction to Probability
- If you are from a math background in school, you might remember calculating the probability of getting a spade or heart from a pack of cards and so on.
- This is perhaps the best book to learn about probability. The explanations are pretty neat and resemble real-life problems. If you have studied probability in school, this book is a must-have to further your knowledge of the basic concepts. If you are going to learn probability for the first time – this book can help you build a strong foundation in the core concepts, though you will have to work for a little longer with the book.
- The book has been one of the most popular books for about 5 decades and that is one more reason why it should definitely be on your bookshelf.
4. Introduction to Machine Learning with Python: A Guide for Data Scientists
- This is a book that can get you kick-started on your ML journey with Python. The concepts are explained as if to a layman and with sufficient examples for a better understanding. The tone is friendly and easy to understand. ML is quite a complex topic, however, after practicing along with the book, you should be able to build your own ML models. You will get a good grasp of ML concepts. The book has examples in Python but you wouldn’t need any prior knowledge of either maths or Programming languages for reading this book.
- This book is for beginners and covers basic topics in detail. However, reading this book alone won’t be sufficient as you get deeper into ML and coding.
5. Python Machine Learning By Example
- As the name says, this book is the easiest way to get into machine learning. The book gets you started with Python and machine learning in a detailed and interesting way with some classy examples like the spam email detection using Bayes and predictions using regression and tree-based algorithms. The author shares his experiences in the various areas of ML such as ad optimization, conversion rate prediction, click fraud detection, etc. which beautifully adds to the reading experience.
- Though the book covers the basics of Python, you might want to start the book after you gain some basic knowledge of Python. The book will help you through the process of setting up the required software until the creation, update, and monitoring of models. Overall, a great book for beginners as well as advanced users.
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6. Pattern recognition and machine learning
- This book is for all age groups, whether you are an undergraduate, graduate or advanced level researcher, there is something for everyone. If you have a Kindle subscription, this book will cost you nothing. Get the international edition that has colorful pictures and graphs making your reading experience totally worth it.
- Coming to the content, this is one book that covers machine learning inside out. It is thorough and explains the concepts with examples in a simple way. Few readers could find some of the terms tough to understand but you should be able to get through using other free resources like web articles or videos.
- The book is a must-have if you are serious about getting into machine learning, especially the mathematical (data analytics) part is exhaustive in nature.Though you can use the book for self-learning, it would be a better idea to read it alongside some machine learning courses.
7. Python for data analysis
- True to its name, the book covers all the possible methods of data analysis. It is a great start for a beginner and covers basics about Python before moving on to Python’s role in data analysis and statistics. The book is fast-paced and explains everything in a super simple manner. You can build some real applications within a week of reading the book. This book can also give you a guideline or be a reference for the topics that you will be otherwise lost for when you search for online courses.
- With focussed learning of both Python and data science, this book gives you a fair idea of what you can expect by being a data analyst or data scientist when you actually start working. The author also gives a lot of references in the book and points to useful resources that you will enjoy going through. Overall, a well-organized book with a thorough explanation of data analysis concepts.
8. Naked statistics
- This book brings out the beauty of statistics and makes statistics come alive. The tone is witty and conversational. You will not get bored reading this book or feel the heaviness of math! The author explains all the concepts of statistics – basic and advanced with real-life examples. The book starts with very basic stuff like the normal distribution, central theorem and goes on to complex real-life problems and correlating data analysis and machine learning.
- While the book explains the basics well, it will be good to have some prior knowledge of statistics with some of these courses, so that you can quickly get on with the book.
9. Data Science and big data analytics
- This book gently introduces big data and how it is important in today’s digitally competitive world. The whole data analytics lifecycle is explained in detail along with case study and appealing visuals so that you can see the practical working of the entire system. The structure and flow of the book are very good and well organized. You can easily understand the entire big picture of how analytics is done as each step is like one chapter in the book.
- The book includes clustering, regression, association rules and much more along with simple, everyday examples that one can relate to. Advanced analytics using MapReduce, Hadoop, and SQL are also introduced to the reader.If you are planning to learn data science with R, this is the book for you.
10. R for data science
- Another book for beginners who want to learn data science using R. R with data science explains not just the concepts of statistics but also the kind of data you would see in real life, how to transform it using the concepts like median, average, standard deviation etc. and how to plot the data, filter and clean it.
- The book will help you understand how messy and raw real data is and how it is processed. Transformation of data is one of the most time-consuming tasks and this book will help you gain a lot of knowledge on different methods of transforming data for processing so that meaningful insights can be taken from it. If you want to learn R before you start with the book, you can do so with simple online courses, however, the book has enough basics covered so that you can start off right away.
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