Hello! I know it’s been a while since I’ve released an article, but I’m excited to be back ????. A question that I’ve frequently gotten by many of my followers is “what are the best data science books to read?”
Data Science has emerged to become one of the most paid and highly reputed domains for professionals. As we see more and more companies adopting data science application in their businesses, there is a surge in the requirement for skilled data science professionals. If you are considering making a move in this domain, then look into Great Learning’s Data Science books that you can choose from.
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.
Here are some of the best books that you can read to better understand the concepts of data science
Often the best way to get information is straight from people in the field, and what better way than to talk with 25 of the industry’s top experts? “The Data Science Handbook” interviews top leading data scientists, from the former US Chief Data Officer to team leads at prominent companies to rising data scientists creating their own programs, in order to offer a unique look into the industry. The selection of interviews will guide newcomers through the industry, offering data life advice, learning mistakes, career development tips, and strategies to succeed in the world of data science. The book doesn’t delve into the technical aspects of the subject or try to be an all-encompassing guide. Rather, it offers a trove of practical advice and insight.
“Doing Data Science” gets straight to the point. It is based on Columbia University’s Introduction to Data Science class and is aimed at any beginners looking to make their way into the subject. Data science consultant Cathy O’Neil collaborates with course instructor Rachel Schutt to bring the data science course to the general public. These experts not only offer knowledgeable lectures on the subject but also share relevant case studies and code, diving into accessible examples. It covers algorithms, methods, models, and data visualization, acting as a practical go-to technical resource.
Data science has a lot to do with math, which can make data science seem inaccessible and daunting. “Numsense” promises to deliver a math-light introduction to data science and algorithms in layman’s terms to make things less intimidating and easier to understand. Each chapter is dedicated to a particular useful algorithm, complete with a breakdown of how it works and real-world examples to see it in use. Visuals accompany the processes to aid in understanding. Reference sheets detail the pros and cons of each algorithm and a handy glossary of common data science terms completes the book.
“The Art of Data Science” dives into the practice of exploring and finding discoveries within any lake of data at your fingertips. It focuses on the process of analyzing data and filtering it down to find the underlying stories. The authors use their own experiences to coach both beginners and managers through analyzing data science. Both authors have experience in managing data projects themselves, as well as managing analysts in a professional setting. They discuss their own experiences on what will reliably produce successful results and what pitfalls make a data project doomed to fail.
The “Dummies” series has always been adept at teaching concepts in simple terms, and “Data Science For Dummies” seeks to do the same. It focuses more on the business side of data science and acts as an introductory guide to entering the field as a professional. It’s a resource for beginners that gives a broad overview of the discipline to get readers familiar with the concepts of big data and how data science is applicable in our lives. The book also explores broad overviews of topics like data engineering, programming languages like R and Python, machine learning, algorithms, artificial intelligence, and data visualization techniques. If you have a passing curiosity about data science, or really just want your parents to understand the gist, this might be a good place to start.
While we’re on the topic of data science for “dummies,” we also have an overview of big data and why it’s important. The book covers the central question — “What is big data?” — and explains the concept from both technical and business perspectives. It presents how big data is used in business intelligence and how it can help analysts discover and solve problems. The book also provides technical advice on topics like how to organize and support the data you collect and how to adapt methods and tools to analyze data. “Big Data for Dummies” promises to help you figure out what your data means, what to do with it, and how to apply it in a business setting.
If you’re going to take advice from one person about data science, it probably wouldn’t hurt to ask a former Chief Data Scientist of United States Office of Science and Technology Policy. DJ Patil is credited for creating the term “data science” and in “Data Jujitsu,” Patil introduces data science as a mindset of problem-solving. He highlights different issues found in data-motivated industries and notes that there’s a difference between problems that are merely difficult to solve and problems that are impossible. Complex problems can be solved by breaking them down into simplified parts and examining them with data analysis. “Data Jujitsu” covers a wide variety of examples and advice for harnessing the power of data.
Big data seems like it never really leaves the news cycle. Data-first companies rise in power, data breaches and leaks of personal and banking information happen, policy debates rage, and regulations regarding data privacy become law. This book aims to discuss the effect data has on just about all aspects of our lives, from business to personal, to even the government and individual scientific disciplines. Mayer-Schönberger and Cukier explain how algorithms can reveal things about ourselves we didn’t think anyone knew just by analyzing our habits online. Online retailers can recommend products or predict buying patterns based on browsing, social media feeds target our political biases and echo chambers. Even dating apps use data to shape love lives. As we take steps to curb what databases know about us, we also have to be careful that our data stays in the right hands. This book discusses the scary, great, and downright interesting ways our own data will — and already does
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.
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.
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.
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.
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.
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.
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.
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 the courses, so that you can quickly get on with the book.
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.
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.
This is not a technical book. However, since you have decided to move into Data science career path, it will be necessary to know why data science and big data holds such an important place today. The book is written from a business perspective and offers a lot of insight into how all the technologies like cloud, big data, IT, mobility, infrastructure, and others are transforming the way businesses work today along with interesting stories and personal experiences to share. The changing times and how we should cope with it are described beautifully in this book.
It is a good read and will keep you motivated during your data science learning journey.
Anything told as a story and shown as graphics fit into our mind easily and stays there permanently. The book is quite impactful and deals with the fundamental concepts of data visualization for you to understand how to make the most of the huge chunks of data available in the real world. The author’s way of explaining every concept is totally unique as he tells it in the form of a compelling story. You wouldn’t even realize how many concepts you can grasp in a day of reading the book — getting to know the context and audience, using the right graph for the right situation, recognizing and removing the clutter to get only the important information, utilize the most significant parts of the data and present them to users — all of these and more.
Here We are listing a few more good books which you might be interested in:
This is a must-have book, a primer to your big data, data science, and AI journey. It is not a technical book but will give you the whole picture of how big data is captured, converted and processed into sales and profits even without users like us knowing about it. It explains how companies are using our data and the information that we share over the internet is used to create new business innovations and solutions that make our lives easier and connect all of us. It also talks about the risks and implications involved in doing so, and how security measures are placed to avoid breach or misuse of data. There are technical papers in the end that are quite helpful. A good, simple read for everyone.
This is a medium level book, a good balance of basic principles and advanced data science principles. The keen focus is on business demands which is what makes the book very practical and interesting. It also explains statistics thoroughly which is one of the foundations of data science. Most books just explain how things are done — this book explains how and why! That helps motivate the readers to get into deep learning and machine learning. This is a good book for beginners and advanced level data scientists alike. It gets tougher as the advance of the topic but you can follow most of the book easily.
This is an advanced book. If you have a little knowledge about statistics and data science through other books or tutorials, you will be able to appreciate the content of the book. It is not a purely technical book but a quick reference as it contains information in the form of questions and answers from various leading data scientists. The questions flow in an organized manner and help you understand each aspect of data science like data preparation, the importance of big data, the process of automation and how data science is the future of the digital world. The book lacks real case-studies though, however, if you have a business mindset, you will get to know a lot of strategies and tips from renowned data scientists who have been there, done that.
This is an awesome in-depth book that explains the theory as well as practical applications to give wholesome knowledge. The author approaches the topics with subtlety and presents many case studies that are easy to understand, comprehend and follow. The book has everything from economics, statistics, finance and all you need to start learning data science. The book has been written with a lot of effort and experience and the way insights have been presented shows the same. It includes statistical and analytical tools, machine learning techniques and amalgamates basic and high-level concepts very well. You will also learn about scholastic models and six sigma towards the end of the book.
A wonderful book that explains data mining from scratch. So much so, that you need not be a computer science graduate to understand this book. It starts with explaining about the digital age, data mining and then moves to explain the kinds of data that can be mined, the patterns that can be mined, for example, cluster analysis, predictive analysis, correlations, etc., and the technologies that are used — statistics, machine learning, and database. The book is purely technical and you can go step-by-step to fully enjoy the book. The book is detailed — a must-have on your collection.
It has a lot of basic and advanced techniques for classification, cluster analysis and also talks about the trends and on-going research in the field of data mining.
This is a small book that can be read along with other reading materials and online courses. It provides a lot of useful insights and enables critical business thinking in the reader. It helps you relate to why things are happening the way they are. Through the chapters, you will learn how to ask good meaningful questions, note down the important details of an idea and get key information to focus on. It nicely covers data-specific patterns of reasoning. The book will help you think ‘why’ and not just ‘how’. It covers what is called as CoNVO — context, needs, vision, and outcome.
The book covers in detail about machine learning models, NLP (Natural language processing) applications and recommender systems using PySpark. It helps you understand the real-world business challenges and solve them. It covers linear regression, decision tree, logistic regression, and other supervised learning techniques. This book will enrich your knowledge greatly especially if you don’t just read it, rather work with the book and practice. You will also be able to appreciate the rich libraries of PySpark that are ideal for machine learning and data analysis. A great book to learn recommender systems using Spark — neat and simple.
The book is like any other fiction book that keeps you hooked up till the last page. If you have read Harry Potter, you will know what we are talking about. The author has done an exceptional job in penning all the concepts in the form of stories that are easy to comprehend. The subjects of statistics and intuitive learning are a bit dry otherwise and this book does its best to make it as interactive and interesting as possible. If you read other books, you will realize how complex neural networks and probability are. This book makes it simple. Before starting the book, familiarise yourself with Python through some courses or tutorials. One of the best books for deep learning techniques from scratch.
Purely business-oriented, this is one book to start with if you are not able to make up your mind into the field of data science. It clearly explains why you should learn data science and why it is the right choice for you. There are beautiful examples like the recommendation system, telecom churn rate, automated stock market analysis and more. The book keeps you motivated. It is not a book that will preach though. It is practical and gives you enough references to start with your technical journey too. The book emphasizes on discovering new business cases rather than just processing and analyzing data.
Check out a preview of the book on Amazon to know the concepts that are taken up in the book.
Last, but not least, this book helps understand the architecture of today’s data systems and how they can be fit into applications that are data-driven and data-intensive. It doesn’t go into depth on management, security, installation and other things but explains data retrieval, database systems and fundamental concepts at length. This book is for you if you are an architect. The author discusses various aspects of designing database and data solutions and gives loads of other resources too (at the end of every chapter!) for you to further your knowledge on the topic.
Let me know your takeaways and other topics you would like me to write about in the comment section — I am eager to see what you have to say.
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