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## Must-Read Data Science Books

In the exciting world of data science, the field continues to grow, offering both opportunities and challenges. To help you navigate this world, we’ve put together a list of the “10 Best-Selling Data Science Books of 2024.” These books are carefully chosen to give you the most up-to-date and useful insights.

Whether you’re already experienced in data science or just starting out, these books are here to guide you through the exciting world of data in the coming year.

## Data Science Books 2024

### 1. **EVERYBODY LIES**

This book is very similar to *Freakonomics* in the age of data science. It’s not a technical book. Every chapter will include a peculiar story that has a data science concept. Whereas, one chapter is about Google searches, another about news, another about image data, etc.

We can see stories of people being creative and finding patterns in the most random things because these random things can reveal a lot. We can lie about the food and the books we read. But, that doesn’t work for the search history.

It’s a book intended for people who are curious about what data science is and what it can do especially in the field of social data. The author has ended this book by stating the next Freud will be a data scientist, the next Foucault will be a data scientist, and the next Marx will be a data scientist.

### 2. **NAKED STATISTICS**

This book will provide several examples of how statistical concepts will apply in the real world. Wheelan hasn’t discussed more about the theory, even though he has some pretty interesting examples and a kind of dry sense of humor. This statistics book will make people laugh.

**3. WEAPONS OF MATH DESTRUCTION**

This book consists of several stories of algorithms’ real-world applications, some of them are about people who have been classified as unworthy by an algorithm. Such as someone purchased an item at a particular shop and automatically got their credit card limit lowered, or a college student couldn’t get a job at a local grocery store because the algorithm said so.

She doesn’t just say “boo hoo, bad algorithm, bad machine!” though — she makes an effort to explain the mechanisms that might make an algorithm racist, for instance. So, why is a policing algorithm sending officers to Black neighborhoods more often?

Well, what happened in that case is that the algorithm was fed data on previous police patrols, which were more often in Black neighborhoods. So the algorithm learned that those neighborhoods are the ones that receive more patrols. The algorithm simply reproduced what it was taught. The book makes you think a lot about how you can design algorithms and data science practices to deal with that.

### 4.**ALGORITHMS OF OPPRESSION**

This book has a few stories, with very simple “data,” which the author explores in depth. The author discusses the percent qualitative, telling stories based on “small data” with a lot of context.

The author, Safiya Noble, was organizing a party for her niece and other children, so, she searched “Black girls” on Google. Instead of getting the pictures of children.

She discovered websites like “HOT BLACK SINGLES IN YOUR AREA.” For other search terms, like “Latina girls” and “Asian girls,” This occurred due to the Google revenue model. The algorithm will serve whichever ad pays the most. And it has turned into a troubling situation as Google is an advertising company,

### 5. **AN INTRODUCTION TO STATISTICAL LEARNING**

This book will be in-depth on theory and doesn’t have any application side. This book doesn’t analyze the deep statistics as a lot of other books, but it would provide knowledge to become a data scientist and includes the key machine learning algorithms.

One of the issues people face with data science is the algorithms that have black boxes where you will put data in and you get data out and you have no idea what happens in the middle. This book has statistical knowledge to understand what’s going on in that black box.

This person is meant for those who don’t have any programming or statistics background. Even if you’re a seasoned data scientist, it’s common to forget certain statistical concepts over time. In real work situations, you may not use every algorithm regularly, and you might become comfortable with your usual methods. This book serves as a helpful reminder, encouraging you to consider trying out different algorithms.

### 6. **DATA SCIENCE FROM SCRATCH**

This book focuses on teaching how to write data science algorithms using Python. It’s a unique blend, somewhat like a mix between a textbook and a regular book, making it ideal for beginners. Whether you’re a layperson or just starting out, this book provides a great entry point.

For example, if you want to learn the Naive Bayes machine learning algorithm, the book takes a hands-on approach. It guides you through programming Naive Bayes from scratch, starting with the math and then building the code using only Python.

While having a basic understanding of Python and some knowledge of statistics can be helpful, the book assumes minimal prior knowledge.

### 7. **HANDS-ON MACHINE LEARNING WITH SCIKIT**

This book will provide us with how to run predictive analytics. In the data science world, there are two main programming languages: Python and R. However, this book covers mostly the Python. Scikit-Learn, Keras, and TensorFlow are all libraries of machine learning and deep learning functions within Python programming languages.

One should have good knowledge about these libraries to be a data scientist. This book is very helpful for beginners as it goes deep into explaining how each algorithm works. The author explains what those different knobs and levers are for beginners to understand, but someone with more experience can appreciate the level of detail that he goes into.

### 8. THINK STATS

Data science consists of three different disciplines. One is programming and computer science; and another is linear algebra, stats, and very math-heavy analytics; and then one is machine learning and algorithms. The data scientist is good at all of them. But that doesn’t always happen, so this book mostly focuses on building out the analytics, math, and stats side of your data science knowledge.

It’s textbook-y. It will `merge the statistical analysis with how you would write it in Python. This book is very beneficial in making statistics into a program.*(Miller)*

### 9. **GROKKING DEEP LEARNING **

This book is an introductory textbook designed for beginners who want to move beyond just using deep learning tools and gain an understanding of how they work. While deep learning often involves complex mathematics, the author emphasizes that a basic understanding of high school math and Python is sufficient.

The book covers essential concepts like gradient descent, backpropagation, and regularization, which are fundamental for advanced tools.

It addresses the risk of implementing sophisticated algorithms without understanding their workings, a common issue in the age of online tutorials. The book aims to prevent misuse of powerful algorithms, ensuring programmers comprehend their impact on decisions and resource use.

### 10. “AL SUPERPOWERS: CHINA, SILICON VALLEY, AND THE NEW WORLD ORDER” BY KAI -FU LEE

According to this book, it says AI will take over all the repetitive jobs. Kai-Fu Lee mentions the global perspective on the intersection of AI and data. Through navigating the dynamics between China and Silicon Valley, this AI ‘Superpowers: China, Silicon Valley, and the New World Order’ can give more information on data-driven technologies and their geopolitical implications.

### 11. **LINEAR ALGEBRA DONE RIGHT **

This book is used as an undergraduate math textbook. It’s designed for a mid-level linear algebra course, that every data scientist can use. It’s not machine learning, it’s not flash programming.

The ability to take a matrix or a high-dimensional space and think about it. This book will allow you to learn about matrices, vector space, and how to do pure math about high-dimensional spaces. This is mainly for a 200- or 300-level course. *(Miller)*

## Conclusion

Summing up, these 10 data science books will allow you to learn about the basics of data science and its applications. These books are beneficial for a beginner. Moreover, some books are about data science learning tools, programming, algebra and algorithms used in data science.

## Best Data Science Books – FAQs

#### Q1. Which book is best for learn data science?

Ans. Natural Language Processing with Python.

Foundations of Statistical Natural Language Processing.

Speech and Language Processing.

Business Analytics- The Science of Data-driven Decision Making.

An Introduction to Probability Theory and its Applications.

#### Q2. Can I learn data science from books?

Ans. Books offer valuable insights into the theory and principles of machine learning and computer science. However, to truly master data science, practical application to real-world problems is essential.

#### Q3. Who can read data science?

Ans. Anyone who belongs to a STEM background (science, technology, engineering, or mathematics ) can read data science. This STEM background provides the foundational knowledge and skills necessary for individuals to excel in the data science domain.

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together