If you don’t know where to start your mathematical journey, our top 3 picks of the books for statistics will help you make your first steps.
There are a lot of resources for learning about statistics, but which ones should you read first
To do well in the data science career, you need to have strong foundations. This means gaining basic knowledge of engineering, statistics, computer science, mathematics, and programming. These are basic subjects that every student should study before embarking on a Data Science career.
Here are some of the best statistics books to get you started in Data Science. They are not all great but will serve to get you started on your journey.
They are the Top Books in the Field of Statistics;
By the time you’ve got a working knowledge of each of these areas your Data Science skills will really be in great demand!
Statistics is more than just a set of statistical tests.
It is a collection of ‘how to’. It is a collection of ‘nice to have’. It is a roadmap that helps you to use proper methods of collecting and organising data, use appropriate methods of analysis to yield correct interpretation and effectively present the results in a way that inspires your audience.
It is how we make discoveries in science, make decisions based on data and make predictions of what the future will look like.
top 10 statistics books for data science
There are a number of data science job opportunities that will remain open into the 2020s, including jobs in data collection and preparation, analytics jobs, jobs in exploratory data analysis and job roles for collecting and cleaning data. These career paths give you a clear training path so you can take your fast-paced field to the next level.
Those without technical backgrounds needn’t worry. We’ve compiled a list of great books for anyone beginning to learn the craft of data science. Some are targeted at business professionals, while others are suited to those with programming experience or want to understand how data science fits into their day jobs. Take your pick, read one, and enjoy the benefits of understanding what makes data science so lucrative. Read on!
Data science is big. It combines math, statistics, programming, machine learning, data mining, probability, optimization, optimization theory and algorithms in ways that are useful to us all. Learning data science through books can help you gain a comprehensive view of data science-and help you start your career in the field. From there it’s just one big question away from being able to apply what you’ve learned to real business problems.
…10 Books to Learn Statistics in Data Science for Data Analysts…Data scientists are in high demand, with the job market projected to grow 21% between 2014 and 2024. This is why it’s important to learn statistics, especially when you’re looking to make a career change.
Computer Age Statistical Inference
This book is for data geeks and data scientists, and it’s appropriate for use by professionals and newcomers alike. No previous knowledge of statistics is required: the focus is entirely on practical methods fostered by Bayesian inference-from theory straight into practice. The book covers both Bayesian and Frequentist statistical inference approaches, and can be used as a reference for data scientists who want to learn about the theory behind the decision making involved. The text begins with basic concepts such as probability theory and prediction, and expounds on the differences between them. This book is ideal for those who already know the basics of data analysis statistics.
Head First Statistics: A Brain-Friendly Guide
The tone of this book, like that of other Headfirst books, is warm and conversational, making it the finest book for data science beginners. The book covers a wide range of statistics , beginning with descriptive statistics such as mean, median, mode, and standard deviation before moving on to probability and inferential statistics such as correlation and regression . A user-friendly format helps you grasp concepts easily, followed by programming exercises to help apply what you’ve learned.The Data Science Toolkit is your practical companion for all of your data science projects. This guide will help you become more familiar with the tools you’ll need to start analyzing data. No matter what NLP or data science class you took, this book will show you how to apply the skills learned at school right away.
Statistics in Plain English
This is a great book to read if you’re new to data science and don’t have a math degree, or if you’re a data scientist or developer who wants to start a new job where a significant statistical background is not required.
Statistics Made Simple takes readers through the world of statistics by walking them through each step. Utilizing an easy-to-understand style, Statistics Made Simple explains the hows and whys behind all of the common concepts, making it easy to understand statistical concepts. It also covers some non-statistical applications of statistics that may not be so commonly known. For example, this book discusses how insurance companies use statistics to calculate their annual premiums and issues life insurance. It also gives insights into the many advantages of using statistics in everyday life, such as how merchants can use percentages to determine credit card interest rate,s several of these courses will benefit from some prior knowledge of statistics so that you can dive right into the book.
Bayesian Methods for Hackers
Bayesian inference is an area of machine learning that suffers from a lack of clear concrete examples. This is exactly what makes Bayesian machine learning by examples such an excellent textbook. The author’s focus on creating concrete, real-world examples (e.g., sentiment analysis, click prediction, survival probability estimation) enables the reader to quickly apply the material to their own use cases. It also serves to ground the concepts in practical applications, ensuring that you understand why things work the way they do instead of simply assuming you know how they work. Although the book seems aimed at programmers, I am writing this review for researchers, it’s a fantastic hands-on introduction to the subject.
Introduction to Statistical Learning
Statistics and Machine Learning: Data Mining and Predictive Analytics with R is the first book that makes machine learning accessible, comprehensive and practical. Written by 32 figure leaders in the field of data mining and predictive analytics, it covers critical statistical concepts and critical real-world examples to provide the foundations to build an intelligent data science career. In addition to theory, this book emphasizes the use of machine learning algorithms in real-world scenarios.
It helps that the authors are professors at Stanford, two of whom founded the Stanford Statistics Club. After publishing The Hidden Pattern Behind Intelligence, they decided to write another book focused on data science principles. This time they are using Python language for presentation, which is suitable for programmers. This book focuses on statistical topics such as message passing, sampling without replacement, and multiple summary statistics. They also cover probability distributions, which is one of the most salient topics in data science.
Discovering Statistics using R
With examples explaining statistical concepts using R, this book is ideal for the beginner to the intermediate level so that they may learn how to apply statistics in real life. By reusing real-life scenarios and examples, students will get an added hands-on experience. It comes with R code already prepared, ready for students to get started quickly.
Practical Statistics for Data Scientists
The goal of this book is to help you learn what you need to know about statistical data science and apply it to real-life problems. Most commonly, this means applying machine learning algorithms to evaluate the skills and fit of an athlete in a tournament. This is a reasonable book for anyone who needs a quick review on many data science topics, with an emphasis on data visualization and probability with Bayesian inference. The book is a great starter resource that covers a wide swath of topics most data analysts would need to know.
The Art of Statistics: How to Learn from Data
Whether you’re a statistician or a developer, Data Smart is a must-read for everyone interested in the ins and outs of data science. Showing you how to use raw data to solve real-world problems, this book is a fantastic supplement to your data science journey. From data collection to analysis and visualization, Data Smart shows you how to think like statisticians and utilize data to solve real-world problems. Less theory and more hands-on examples make this a must-read for anyone interested in using smart techniques gleaned from statisticians from all disciplines not only for business but for education, scientific research
Best Statistics Books For Data Science
With the latest data and powerful new tools, is it possible to succeed at Data Science? Can we interpret and understand data so we can uncover secrets of the world around us? There are four books in this post that cover the topics described below. These statistics books will help you build your roadmap so you’ll have the confidence you need to know that your discoveries are based on good science. The books below will teach you how to take advantage of your data environments to answer scientific questions, whether they’re about climate change, sports teams, or even babies’ behavior! Great statistics skills transform science into plain English.
Can You Learn Statistics On Your Own ?
This is the easiest question about statistics to answer – yes, yes, yes – absolutely yes!
Statistics have a bad reputation in graduate school. It is easy to figure out statistics. It is boring to learn, but that’s because it’s necessary for you to learn the material.
If they can do it, so can you!
My job is to help people understand and evaluate the different ways data and statistics, and to find new ways to combine both statistics and data. I don’t aim for people to become statisticians, but rather think of my role as extending the process that allows people to make simple, correct statistical decisions into all aspects of their lives. Statistics are about making empirical statements that are backed up by evidence found in available data sets, and they are therefore useful everywhere real decisions need be made.
The statistics books for Data Science in this post will certainly help you on that journey.
TOP 3 BEST STATISTICS BOOKS FOR DATA SCIENCE:
- Naked Statistics: Stripping the Dread from the Data
- Practical Statistics for Data Scientists: 50 Essential Concepts
- Statistics Done Wrong: The Woefully Complete Guide
They are all for beginners, are very entertaining and give you a great idea of how to do stats right – and how to spot when they’re wrong!
The best statistics books are the ones that are relevant to Data Science, or at least relevant for data scientists to expand their knowledge of Statistics. These are the top 3 best statistics books for Data Scientists listed here, in order of personal value and preference. These are not all great stats books, but they’re all great reads that will help you in your Data Science studies.
You’ll also find our recommendations for the best multivariate statistics books, the best Bayesian statistics books and the best time series analysis books too.
Something for everyone!
best statistics book for machine learning
Statistical methods are used at each step in an applied machine learning project.
Statistics is used on an everyday basis in many different areas, ranging from marketing to law. Many careers require knowledge of statistics and/or statistics methods. For this reason, some college courses in statistics are offered. Many students who graduate with a major in Statistics do not pursue statistical work; instead they pursue careers in marketing or management.
We are all aware of the importance of statistics in almost every field. Yet, for software engineering, statistics is hardly taught at all. Consequently, many fresh engineers are unprepared to tackle areas that have become commonplace in modern computing, such as data mining, machine learning, and predictive analytics. This book helps you learn the fundamentals of these subjects by showing how to use one of the most popular software development tools: R.In this post, you will discover some top introductory books to statistics that I recommend if you are looking to jump-start your understanding of applied statistics.
I own copies of all of these books, but I don’t recommend you buy and read them all. As a start, pick one book, but then really read it.
Kick-start your project with this new book Statistics for Machine Learning,
Let’s get started
So the best books for a statistics course are ones that keep it relevant and interesting. These books can be full of statistical examples or they can be philosophy oriented. In either case, they offer the opportunity to learn from others’ mistakes and from learning from their successes…
Do not overlook these types of books.
I read them all the time even though I’ve pawed through statistics textbooks. The reasons I recommend them are:They’re just meant to be used, can be easily combined, and are intended for the lay audience.
They will help show you why a working knowledge of statistics is important in a way that you will be able to connect to your specific needs in applied machine learning.
There are many great popular science books on statistics; the three I would recommend are:
Naked Statistics: Stripping the Dread from the Data
Written by Charles Wheelan.
In this practical, no-nonsense book, Wheelan demonstrates how a basic understanding of statistics can lead to a better understanding of the world around you. He draws on dozens of case studies to explain techniques for conducting your own investigations. Impressively illustrated with numerous examples from history and everyday life, the book ends with a section devoted to using statistics to solve problems in everyday life . Each chapter includes links to further reading and other helpful content. Readable and engaging, this is a must-have guide for individuals who want to dig deep into their data, investigate questions that matter from the outside in—and from the inside out.
The Drunkard’s Walk: How Randomness Rules Our Lives
Written by Leonard Mlodinow.
New York Times bestselling author Leonard Mlodinow explains in clear language how our lives are profoundly informed by chance and randomness, when everything from wine ratings to corporate success is less reliable than we believe.
The Signal and the Noise: Why So Many Predictions Fail – but Some Don’t
Written by Nate Silver.
Most predictions do not survive contact with reality at all. The road they travel is an even steeper one than most would expect, and travelers such as Silver fall by the wayside along the way. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones.
But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for and overcoming future contingencies. We live at a time when we’re increasingly aware of our ability to make forecasts – and increasingly frustrated by their failings – but we don’t always take this awareness and frustration to heart as we should. To understand why, consider your objection to predictions as foresight. The forces that shape our world
You need a solid reference text.
A textbook contains the theory, the explanations, and the equations for the methods you need to know.
Do not read these books cover to cover; rather, once you know what you need, dip into these books to learn about those methods.
In this section, I have included a mixture of books including (in order) a proper statistics textbook, a text for those with a non-math background, and a book for those with a programming background.
Pick one book that suits your background.
All of Statistics: A Concise Course in Statistical Inference
Written by Larry Wasserman.
The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data.
Statistics in Plain English
Written by Timothy C. Urdan.
This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. Using the author’s own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics.
Practical Statistics for Data Scientists: 50 Essential Concepts
Written by Peter Bruce and Andrew Bruce (Author)
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
Statistical Research Methods
Once you have the foundations under control, you need to know what statistical methods to use in different circumstances.
A lot of applied machine learning involves designing and executing experiments, and statistical methods are required for effectively designing those experiments and interpreting the results.
This means that you require a solid grasp of statistical methods in research context.
This section provides a few key books on this topic.
It is hard to find good books on this topic that are not too theoretical or focused on the proprietary SPSS software platform. The first book is highly recommend and general, the second uses the free R platform, and the last is a classic textbook on the topic.
Empirical Methods for Artificial Intelligence
Written by Paul R. Cohen.
Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.
Statistical Research Methods: A Guide for Non-Statisticians
Written by Roy Sabo and Edward Boone.
This textbook will help graduate students in non-statistics disciplines, advanced undergraduate researchers, and research faculty in the health sciences to learn, use and communicate results from many commonly used statistical methods. The material covered, and the manner in which it is presented, describe the entire data analysis process from hypothesis generation to writing the results in a manuscript. Chapters cover, among other topics: one and two-sample proportions, multi-category data, one and two-sample means, analysis of variance, and regression. Throughout the text, the authors explain statistical procedures and concepts using a non-statistical language. This accessible approach is complete with real-world examples and sample write-ups for the Methods and Results sections of scholarly papers. The text also allows for the concurrent use of the programming language R, which is an open-source program created, maintained and updated by the statistical community. R is freely available and easy to download.
Statistics for Experimenters: Design, Innovation, and Discovery
Written by George E. P. Box, J. Stuart Hunter, and, William G. Hunter.
Rewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.
statistics books for college students
If you are a beginner in statistics, then, this book is for you.
It will guide you from the basic statistics and help you to get your knowledge to the undergraduate level.
You will get well-organized chapters inside this book.
The student will not found any issue while reading this book.
All the text and graphs in this book are well written and easy to understand.
I would like to recommend this book to those who are looking to start their studies in statistics.
Features of this top best statistics book
As already mentioned, this book is the best statistics book for beginners.
If you are a beginner, then this book will help you to clear statistics on basic concepts.
It does not just cover the basics, but it also helps you understand eloquent measures involving statistical analysis.
With the help of this book, you will be capable of dealing with interpretation, a variation of coefficient and correlation, hypothesis tests, and lots more.
2. Barron’s AP Statistics, 8th Edition
Written by-Martin Sternstein, PhD.
We all know that math and statistics work together, likewise body and soul. In other words, maths is an essential aspect of statistics.
The mathematics expert has written this book based on his experience.
That’s why this book is considered among the best statistics textbooks in the world.
The writer of this book had been the head of the math department in various universities.
Also, he has won several awards in maths.
That’s why we can’t doubt the perfection of this book.
The author of this book believes that everyone should have an equal right to access the subject.
Summary of this best book of statistics
This book also has well-managed content. It comes with 15 chapters that cover almost every topic of statistics.
With the help of this book, you will learn how to use the professional calculator with perfection.
It also helps you to do some practice with the 5 full-length exams.
Don’t worry about the answers. The author has given the answers to these question papers.
Also, he has provided multiple-choice questions with answers.
If you get bored too early while reading the book, you can also watch the CD that comes with this book.
It will help you to understand the subject better.
3. Statistics for Business and Economics
Written by- James T. McClave, P. George Benson and Terry T Sincich
This book is not written by a single expert. A couple of experts have written this book with their experience.
That’s why this book contains more expertise than other books I have mentioned on the top.
All the authors of this book are statistics experts at various levels.
They all have a high degree of achievement
With their combined efforts, they have made the subject more interesting and understandable in this book.
Summary of this best statistics books on business Statistics
This book is incredible for the statistics students.
This book has real-world data that is formed into exercises, examples, and applications.
Every chapter of this book has the latest conversational issue along with its case study.
The author has created an exercise with perfection.
The students always have an opportunity to evaluate the scenario critically.
4. Naked Statistics: Stripping the Dread from the Data
Written by— Charles Wheelan
The name implies that this statistics book is written in a funny manner.
This book is almost for everyone.
Even if you hate statistics, you will definitely love this book.
In other words, if you are not good with statistical data, you would like this remarkable book.
This book is quite engaging for all the statistics students.
This book will help you learn why statistics is precise in almost every topic of statistics.
Summary of this best statistics books for data science
Because of his comic-style book, the author is a best-seller writer.
He shows the real-life data. He has also shown free statistical tools in his statistics book.
It will help the students clear all their doubts and answer almost every question that comes into their minds.
He has cleared the doubts statistics of all level readers.
5. OpenIntro Statistics
Written by-David M Diez, Mine Çetinkaya-Rundel, and Christopher D Barr
Statistics is the most complex subject for students. If its concepts are not written clearly in the books, it could be overwhelming for them.
Even the students will not learn anything from the book.
The book can become garbage for the students.
The author of this book keeps this in his mind and writes a simple words statistics book.
He has also taken care that the written content is well written and clear.
This book is a gold mine with good command over maths and looking to learn stats at a rapid pace.
All the statistics concept is cleared with proper details.
Summary of this book about statistics
This book covers free and easy-to-use tools and techniques.
The techniques can easily be modified as per the requirements.
The author believes that probability is just an option.
On the other hand, the conclusion is the key. The best way to get the key is by analyzing the real-world data.
6. Head First Statistics – A Brain-Friendly Guide
Written By – Dawn Griffiths
O’Reilly Media is one of my favorite publications in the world. The way they present the topic is quite interesting.
All the books of these publications are beginner-friendly, and you will love all their books when you start learning from these books.
Now let’s talk about this book. This book offers you the best way to learn about histograms, probability distributions, chi-square tests, z scores, and many more topics of statistics
If you are in high school, you should look at this book to clear your statistics exam like a pro.
7. Think Stats Probability and Statistics for Programmers
Written By – Allen B. Downey
Think Stats book is for programmers. If you want to grow your career in statistics as a programmer, Think Stats is the best book.
Here you will learn the probability and statistics for Python programming.
This book is not for beginners or those who are going to start their education in statistics.
This book offers the simple techniques that are used to explore real data sets.
It also answers the most critical questions that come to Python programmers’ minds.
This book was written by the writer of O’Reilly Media. But it is freely available to you under the Creative Commons license.
But you can’t use it for commercial purposes. You can make changes in this book in the live version that is available on greenteapress.
In fact, this statistics book is distributed by greenteapress.
Summary of best statistics book
In the introduction phase, I have already mentioned that this book is not for beginners.
But some python programming beginners can start with this book to learn probability and statistics for python.
Even if you already have basic Python skills, you can learn the basic concepts of probability and statistics more easily than other students.
This statistics book is based on the Python libraries, which include PMFs and CDFs libraries.
Here you will find lots of short programs that will help you to clear your concept easily.
Written By – Woollcott Smith
This book is published by larrygonick.
As the name reflects what you will get in this statistics book.
The author of this book is known for the comic version of the educational books.
They have published plenty of books in history, science, and other subjects.
If you hate statistics the most then this book will turn your hate into love with interesting comic form material.
Summary of best book about statistics
Here is this book you will learn everything about statistics with the help of comic diagrams.
This book covers almost everything about statistics.
With the help of this cartoon guide, you will learn the basics of probability, data sets, random variables, binomial distributions, sampling, and much more.
It also covers all the formulas of these statistics topics.
In contrast, we can say that it is a real statistics book for beginners.
9. Statistics II for Dummies
Written by – Deborah J. Rumsey
Statistics 2nd for dummies is the book for dummies.
They are known for their best materials. Most of the universities recommend the books for Dummies.
Because they cover every topic with perfection.
Here in this book, you will learn the practical explanation of statistical ideas, techniques, formulas, and calculations.
Here you will also learn how statistics is applicable in our life with the help of examples.
Summary of Best book of statistics
With this great statistics book, you will learn how to interpret and critique graphs and charts in statistics.
You will also learn probability, confidence intervals, hypothesis tests, statistical formulas, and much more.
Here you will find all the latest examples.
Apart from that, there is an additional benefit for the students because they will get free advice to take real-world problems in statistics.
In contrast, this book makes you ready for the statistics job.
Written by: Larry Wasserman
If you have no time to learn probability and statistics then this book is for you.
This book is published by Springer publication.
Here you will learn most of the statistics concept with just a single book.
This book is specially designed for students, researchers, computer scientists, data mining students, and machine learning students.
In other words with these books you can learn from the basic statistics to the advanced level.
Summary of this best books about statistics
This book covers more topics than any other book in the world.
With this book you will cover almost everything about statistics.
Here you will learn some of the advanced topics i.e. non parametric curve estimation, bootstrapping and classification.
It covers all your statistics course material.
You can start to learn from this if you are a beginner to statistics and probability.
The graduate and undergraduate students will also find it beneficial for them.
This book is amazing for students who want to enhance their careers in statistics.
best data analytics books for beginners
You’ll find no shortage of excellent books on data analytics out there, but we’ve decided to focus on those that are most relevant to beginners. Many of these titles offer an introduction or overview of a topic rather than a technical deep dive. Some of the more skills-based books include exercises to get you practicing real world data skills.
1. Data Analytics Made Accessible by Dr. Anil Maheshwari
Best data analytics overview
The chapters in this book are organized much like an introductory college course—many universities have adopted it as their textbook. It’s an excellent introduction if you’re just getting started in data analytics or wondering what data analytics is all about. Besides high-level overviews of key data concepts, the book also includes:
- Real-world examples of data analysis in practice
- Case study exercises that could lead to potential portfolio pieces
- Review questions to help you check your comprehension
- R and Python data mining tutorials for complete beginners
While the book was originally published in 2014, it has been updated several times since (including in 2021) to cover increasingly important topics like data privacy, big data, artificial intelligence, and data science career advice.
2. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo
Best data science overview
Reading this book provides a gentle immersion into the world of data science—perfect for someone coming from a non-technical background. The authors walk you through algorithms using clear language and visual explanations, so you don’t get bogged down in complex math.
While this book is geared toward beginners, it offers value to practicing data scientists as well. Use it as a refresher on how to communicate what you’re working on to business partners.
3. Python for Everybody: Exploring Data in Python 3 by Dr. Charles Russell Severance
Best book to learn Python
If you’ve never written a line of code before (or if you still consider yourself a beginner), this book will have you writing your first program in minutes. Dr. Charles Severance of the University of Michigan walks readers through the process of learning to “speak” to a database through Python.
It’s a useful resource on its own and even more valuable when used alongside Dr. Chuck’s popular course, Python for Everybody.
At the time of writing, you can download a free electronic version of Python for Everybody at py4e.com.
4. SQL QuickStart Guide: The Simplified Beginner’s Guide to Managing, Analyzing, and Manipulating Data With SQL by Walter Shields
Best introduction to SQL
This is so much more than a book. When you buy this book on Structured Query Language (SQL), you get access to a sample database and SQL browser app, so you can put what you’re learning into action right away. You’ll also get lifetime access to a host of digital tools—workbooks and reference guides among them—to complement your learning.
This book covers topics like:
- Database structures
- How to use SQL to communicate with relational databases
- Key SQL queries to complete common data analysis tasks
- Advice on how to pitch your new SQL skills to potential employers
5. Big Data: A Revolution That Will Transform How We Live, Work, and Think by Kenneth Cukier and Viktor Mayer-Schönberger
Best big data book
Whether or not you’re involved in the world of data analytics, you’ve probably heard the term “big data” at some point. This book by two experts in the field goes beyond the buzzword to illuminate just how big data is already changing our world, for better and sometimes worse.
This isn’t a technical text to teach you big data algorithms. It’s more of a primer in what big data is, what it can do, and how it might impact the future.
6. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett
Best business analytics book
This book digs deep into the importance of data for business decision making. If you’re interested in pursuing a career as a business analyst, consider this an introduction to how data science and business work together, and what goes into data-driven decision making.
The authors do a good job of outlining data science techniques and principles as they relate to business without getting caught up in the technical details of algorithms.
Honorable mention: Too Big to Ignore: The Business Case for Big Data by Phil Simon
7. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
Best artificial intelligence book
By reading this book, you can start to separate the hype surrounding the idea of artificial intelligence (AI) from the reality. Author Melanie Mitchell, a computer scientist, explores the history of AI and the people behind it to help readers better understand complex concepts like neural networks, natural language processing, and computer vision models.
While data analysts don’t necessarily need a deep understanding of AI, it can be helpful to understand these technologies and their impact on the world of data analytics. Mitchell approaches these topics in a way that’s clear and engaging.
8. Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic
Best data visualization book
In data analysis, our data is often only as good as the stories we tell with it. This book walks you through the fundamentals of communicating with data through storytelling and visualization. It combines theory with real-world examples to help you:
- Recognize context
- Choose the right visualization for the right situation
- Eliminate clutter and highlight the most important parts of the data
- Think like a visual designer
- Build presentations using multiple visuals to tell a compelling story
Reading this book won’t teach you to create masterful visualizations using R or Tableau, but its insights can equip you to use those tools more effectively when you do learn them.
9. The Hundred-Page Machine Learning Book by Andriy Burkov
Best machine learning book
This title delivers on its promise: an overview of machine learning in a little bit more than 100 pages (140 to be exact). It’s short enough to read in a single sitting. Andriy Burkov offers a solid introduction to the field, even if you have no statistical or programming experience.
This compact read covers an immense amount of information. Topics include supervised and unsupervised learning, neural networks, cluster analysis, and hyperparameter tuning. If you’re not familiar with those terms, don’t worry. You will be after reading this one. You can always turn to the companion wiki for recommendations on further reading and resources.
This book comes with a read now, buy later offer (at the time of writing). Download and read the book for free, and if you find it valuable, support the author by buying it.
10. Business unIntelligence: Insight and Innovation beyond Analytics and Big Data by Dr. Barry Devlin
Best business intelligence book
This book explores how the trinity of people, process, and information come together to drive business success in the modern world. This is not a book about traditional business intelligence (BI) concepts. Instead, it outlines the ways in which BI can fall short and presents new models and frameworks to improve the practice.
If you’re looking for an overview of the past, present, and future of BI, give this book a try. Topics discussed include:
- The birth of the biz-tech ecosystem
- Practical tips for using big data
- Data-based, intuitive, and collaborative decision making (and why companies need all three)
11. Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
Best statistics book
If you need a refresher of what you learned in college statistics, pick up this book. If you’re someone who struggles with mathematical concepts presented as a series of numbers and symbols stripped of context, pick up this book.
Charles Wheelan dives into key concepts in statistical analysis—correlation, regression, and inference—in a way that’s both enlightening and entertaining. Wheelan makes a good (and humorous) case for why everyone should understand statistics in our modern world, not just data professionals.
You may not walk away knowing with mastery of statistics. But this book can help you understand the underlying concepts and why they matter, making it an excellent companion to more technical statistical coursework.
12. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil
Best book on data bias
Big data can be a powerful tool, and this book serves as a warning and reminder that we need to use it responsibly. Data scientist and mathematician Cathy O’Neil explores the consequences of machines making decisions about our lives, and how the algorithms driving those decisions often reinforce discrimination.
Even if you don’t agree with the author on every point, you might walk away with a better understanding of the darker side of data. These relevant and urgent insights are particularly important for those just getting started in the world of data—those whose responsibility it will be to ensure that the data of the future is used for the benefit of all, not just the privileged.
Honorable mention: Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble