Humans typically use our senses and brains, two naturally occurring instruments, to make sense of the complexity of the natural world. However, data science uses algorithms and prediction models to enhance such natural abilities. Data science has, in a sense, become the sixth sense for humans. However, it’s also most likely the sense that the general public is least familiar with. There, Data Science Books can help keen students.
The resultant reading list consists of math textbooks and technical machine learning literature, as well as data science books addressing social studies of how algorithms affect our everyday lives. These data science books include accessible explanations and insights into the advantages and disadvantages of this area, along with real-world applications. These data science books provide a variety of expertise to help you expand your knowledge base, regardless of your level of familiarity with these ideas or your level of technical ability.
Highly Appreciated Data Science Books
1. Everybody Lies: Big Data, New Data, And What The Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz
This book may be compared to Freakonomics for the data science era. It is not at all a technical book. Each chapter presents an unusual narrative that exemplifies a notion in data science.
For example, there are chapters concerning news, Google searches, picture data, and so on. It’s a collection of tales about inventive individuals who see patterns in the most unlikely objects, as these seemingly little objects may disclose a great deal.
The reason the book has that title is that, even if you lie about your reading and eating habits and your intended candidate, if others get access to your search history, they will be able to uncover the truth. This book is meant for readers who are interested in learning more about data science, particularly as it relates to social data.
In closing, the author predicts that there will be data scientists in the future who will study Freud, Foucault, and Marx. That could be a bit excessive, in my opinion, since data science isn’t able to address every query. But take it with a grain of salt—it’s one of the entertaining Data Science Books.
Available on: Amazon, AbeBooks.
Price:
- Audio book: Free with Audible membership.
- Kindle edition: $13.39.
- Paperback: $16.99.
- Hardcover: $23.11.
2. Weapons of Math Destruction: How Big Data Increases Inequality And Threatens Democracy by Cathy O’Neil
Cathy O’Neil, the book’s author, was once a professional mathematician. After that, she visited Wall Street, participated in Occupy Wall Street, and is now an activist spreading awareness of how algorithms control our lives and that they are not as objective or impartial as we would like to think.
The book is an anthology of real-world algorithmic application tales, many of which center on individuals who were rejected by algorithms. An example of this would be if a customer made a purchase at a certain business and their credit limit was automatically reduced, or if an algorithm prevented a college student from being hired at a nearby grocery store.
She tries to describe the dynamics that may lead to an algorithm becoming racist, for example, rather than simply saying, “boo hoo, bad algorithm, bad machine!” Why, therefore, do police officers visit Black neighborhoods more often according to an algorithm? In one instance, data from earlier police patrols—which were more often conducted in Black neighborhoods—was incorporated into the program.
The computer then discovered that they were the neighborhoods with higher patrol counts. All the algorithm did was mimic what it had been trained. The book forces you to consider several approaches to dealing with it in the creation of algorithms and data science procedures.
Available on: Amazon, Penguin Books.
Price:
- Audio book: Free with Audible membership.
- Kindle edition: $12.99.
- Paperback: $12.77.
- Hardcover: $32.93.
3. Data Science from Scratch: First Principles with Python by Joel Grus
Joel Grus’ Data Science from Scratch is a great place to start if you’re new to data science, particularly if you’re taking a course in data science or are a newbie who wants to use Python for data science.
The author’s impressive background as a software engineer at Google and a research engineer at the Allen Institute for Artificial Intelligence is another plus. This book is notable for its practical use of Python and its straightforward presentation of data science principles.
Of course, you might argue that R or Python is superior for data science, but for the sake of argument, let’s use Python instead.
Available on: Amazon.
Price:
- Kindle edition: $13.20.
- Paperback: $44.17.
4. A Hands-On Introduction to Data Science by Chirag Shah
If you want to learn useful data science and analytics abilities, you should read this one of the Top Data Science Books. Students like Shah’s hands-on teaching style, which emphasizes practical applications and data science projects. This makes it an excellent resource for both beginning and intermediate students.
Before learning how to manage and prepare data for analysis, a critical stage in the data science process, expect to start with the fundamentals of data manipulation and cleaning—skills that are essential for every data scientist.
You get deeper into areas like statistical analysis and machine learning as the book goes on. As previously mentioned, you will also have practical exposure to important methods like clustering, regression analysis, and classification. These abilities are essential for identifying trends in data and generating forecasts.
The book also covers fundamental tools and programming languages used in data science, with a particular concentration on Python. This is fantastic news since it will teach you how to work with Python libraries such as Scikit-learn for machine learning, matplotlib for data visualization, and Pandas for data processing.
Available on: Amazon, Cambridge University Press.
Price:
- E-book: $52.24.
- Hardcover: $54.99.
5. Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Noble
This book offers a few anecdotes, with relatively basic “data,” which the author discusses in detail. Readers found it a intriguing read, considering the author’s background is virtually diametrically opposite. She’s 100 percent qualitative, creating tales based on “small data” with a lot of contexts.
In one of these tales, the author of one of the best Data Science Books, Safiya Noble, was preparing a party for her niece and other youngsters, and she searched something like “Black girls” on Google. To her astonishment, she didn’t locate photographs of children. She saw websites like “HOT BLACK SINGLES IN YOUR AREA.” For other search phrases, like “Latina girls” and “Asian girls,” she discovered the same thing.
The reason this occurred, she added, is Google’s income strategy. The algorithm will serve whichever ad pays the most. And it becomes a worrisome scenario because even though Google is an advertising firm, we utilize it like a public library — like some type of publicly available collection of knowledge. Some find it a really depressing read.
Available on: Amazon, WorldCat.
Price:
- Audio book: Free with Audible membership.
- Kindle edition: $15.12.
- Paperback: $17.65.
- Hardcover: $24.99.
6. Essential Math for Data Science: Take Control of Your Data by Thomas Nield
There’s no denying that math is necessary if you want to work in data science. Thomas Nield’s Essential Math for Data Science is an excellent resource for anybody wishing to get a deeper grasp of the mathematical underpinnings that are essential to data science, which is why I had to add it. Students particularly like how this data science book offers clear, concise explanations of challenging mathematical concepts that are only available to data scientists.
You should prepare to begin by studying the foundational concepts of algebra and calculus—two of the most important mathematical languages for data science. Here, the objective is to review your core competencies and ensure that you have a solid basis on which to build more complex skills.
This book’s emphasis on statistics and probability, which are both necessary to comprehend data analysis and machine learning, is seen by many as one of its strongest points. This implies that you will gain knowledge of probability distributions, statistical inference, and descriptive statistics, which will help you analyze data and make defensible conclusions.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $14.75.
- Paperback: $32.99
7. Think Stats: Exploratory Data Analysis by Allen B. Downey
T three distinct fields are combined to create data science. Three areas of study include computer science and programming, linear algebra, statistics, and highly mathematical analytics, and machine learning and algorithms.
The perfect data scientist excels in each of these areas. However, because that isn’t always the case, this book focuses on expanding your understanding of analytics, arithmetic, and statistics in the field of data science.
How do you test your answers, how do you make sure the distributions are correct, and how do you use all of this mathematical knowledge to address real-world business problems? It’s not a strict textbook, but it seems textbook-like. Additionally, it combines statistical analysis with the Python writing style.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $6.73.
- Paperback: $15.92.
8. Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry
Rafael Irizarry’s Introduction to Data Science, written by a professor of data science and fellow of the American Statistical Association, is one of the Top Data Science Books for anyone looking for a thorough and approachable read.
As such, it’s a great fit for both new-to-the-field professionals and students. The foundational ideas of data science, such as the fundamentals of data kinds and data collecting, will be covered first. These are important to grasp to manage and analyze data efficiently.
This data science book will show you the value of visualizing data to spot trends, patterns, and outliers. It also spends a good deal of its material on data visualization and exploratory data analysis (EDA). Readers also like that it helps you build critical skills in presenting data insights by offering real-world examples utilizing widely used data visualization technologies.
Another important topic that is well covered is statistical inference, where ideas like probability, hypothesis testing, and confidence intervals are addressed understandably and straightforwardly.
Available on: Amazon, Rafalab.
Price:
- Kindle edition: $25.48.
- Hardcover: $62.89.
How To Choose The Best Data Science Book
We took into account the following factors while choosing the best Data Science Books, and we advise you to do the same:
Author qualifications: To make sure the writers have the skills to provide you with the information you need, we sought authors with a wealth of data science experience.
Experience level: We searched for books on data science for readers with a variety of backgrounds, including selections for more seasoned data scientists as well as books for beginners.
Date of publication: Since this is one of the more traditional programming languages, we searched for a blend of newer works and classics that would still be helpful to data scientists.
Testimonials from past readers: We analyzed first-hand accounts from our community and from online retailers such as Amazon to get important information about the advantages and disadvantages of each book.
Chosen learning style: We’ve offered a variety of alternatives to assist you choose a data science book that best suits your chosen learning style. Some books are more theoretical, while others are more hands-on with real-world examples.
FAQ
Q: In what practical ways is data science applied?
A: Numerous industries find use for data science, including retail (recommendation systems and market analysis), healthcare (personalized medicine and disease prediction), finance (fraud detection and risk assessment), transportation (predictive maintenance and route optimization), and more.
Q: Where is data science mostly applied?
A: Numerous sectors, including healthcare, banking, marketing, and technology, employ data science for a broad variety of purposes, such as fraud detection, recommendation systems, machine learning, predictive analytics, sentiment analysis, and decision-making.
Q: What makes data science so necessary?
A: It drives everything, including the future of healthcare as well as innovation and the consumer experience. Data science has the ability to change how we work and live. It can also enable people to solve problems, find new developments, make better judgments, and deal with some of the most important global concerns.