The Best Data Science Books for Beginners and Experts in 2021
Want to become a data scientist? There’s no better time to become a data scientist than now.
The data science industry is rapidly growing, and a data scientist’s role is one of the most in-demand jobs. On LinkedIn, Data Science is among the top fastest-growing jobs. On the Glassdoor website, Data Scientist is the number one job.
According to New York Times, Data Science is a “hot new field that promises to transform industries from business to government, health care to academia.”
Many organizations are looking out for data scientists who can craft data-driven insights and value for business products and services. All this makes Data Science a very attractive field of study that's highly employable with many growth opportunities.
Nevertheless, a data scientist must have several skills and possess a wide array of expertise in statistics, programming, maths, and business logic. The good news — you can learn these skills. One of the best resources for learning data science is books. But, what books should you read?
This article lists some of the best books for data science. If you are a beginner, the outline comprises some of the most ideal books for beginners and will give you a holistic view and data skills. If you are at an intermediate or an expert level, the list has some of the best books to take your existing skills to exciting new heights.
Authored by Joel Grus, a software engineer at Google, Data Science from Scratch is an introductory book to data science and machine learning. The book targets intermediate programmers interested in getting started in data science and machine learning.
If you are new to Python, Chapter 2 covers a python crash course. Also, the author doesn’t assume you have any machine learning math background, so several chapters cover mathematical concepts. Essentially, the book provides an excellent overview of practical Python code examples and gives an excellent overview of mathematical concepts and statistics required for data science.
This book is for you if you want to get started with machine learning algorithms. It will take you from a beginner programmer to implement machine learning algorithms to address various data science problems.
This is another excellent book when it comes to Data Science. The author, Charles Wheelan, makes statistics so interesting by taking you through the fundamentals of statistical concepts and their applications using real-world examples in an engaging and conversational style.
He clarifies concepts including correlation, inference, and regression analysis and reveals how biased or carelessness can misrepresent or manipulate data.
Specifically, the examples used to explain the concepts bring everything together and make it easy to understand and retain.
Whether you are just starting or require to remind yourself of some data science concepts, this highly recommended book should be on your reading list!
In this book, Authors Andreas Müller and Sarah Guido provide you with a fantastic introduction to machine learning in Python in a very user-friendly way.
It is well written, well organized, and easy for readers to follow as it is loaded with hands-on examples. If you want to know how to implement ML algorithms, this book will guide you on using the various machine learning algorithms and provide insights on how they work. That means it will help you grasp ML concepts and build your models.
However, the book does not cover the in-depth mathematical details required to program the algorithms from scratch. That means you’ll flow and understand the concepts in the book if you have no previous math or programming knowledge,
This book is ideal for beginners in data science and machine learning and will not be sufficient for deeper ML and coding.
For anyone looking to get started in Data Science and ML, this is one of the best books because it provides a clear, concise, and engaging introduction to the field. It’s authored by Rachel Schutt, Senior VP of Data Science at News Corp, and Cathy O’Neil, a senior data scientist at Johnson Research Labs.
The book is based on a course that Rachel taught at Columbia University to which Cathy contributed. One of the best things about this book is its collection of real-world examples contributed by the guest lecturers to Rachel’s course who share methods, algorithms, and models by presenting case studies and the code they use.
And no, you don’t need to have a Ph.D. to read this book, but a bit of python knowledge will help. After reading this book, you’ll be able to create ML models on your own. Another strength of this book is that you get comprehensive coverage on what approach or algorithm to use against a given data science task at hand. This way, you feel more secure, eager, and ready to embark on any data science project.
I enjoyed this book and highly recommend it to any aspiring data scientist or anyone new to Python. It is written by Jake VanderPlas with Jupyter Notebooks, so it is easy to follow along and try code from the book in your notebook.
I only had but little experience with Python before reading the book. However, I was able to pick it up quickly when I read it. Before long, you’ll be plotting distributions of real-time statistics and predictive modeling.
The best thing about the Python Data Science Handbook is that you can use it for quick reference while doing important tasks or projects. If you are breaking into Data Science/ Machine Learning in Python, you can go for it, it’s one of the best books about data processing, analysis, and visualization and will keep coming in handy long after you finish it.
Data Science Programming for Dummies by John Paul Mueller and Luca Massaron is a great start for students and professionals who want a quick intro to the data science field.
You will learn fundamental concepts in data science in a friendly way. Reading this book will help you understand the technologies, programming languages, and mathematical concepts and then delve into actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation models.
For example, it will help you decide which programming languages are best for particular data science needs. It also gives you the guidelines you need to tell your data story and build your projects to solve real-time problems.
Whether you’re just getting started or already mid-career in data science, the book will see you get a great introduction to the field of Data Science.
Statistical skills and methods are crucial in Data Science. This book is highly recommended to learn practical statistics for data science, so go ahead and get yourself a copy.
It’s a fantastic book written by Peter Bruce, Andrew Bruce, and Peter Gedeck for intermediate and advanced data scientists and so well written in a way that beats tedious and overly difficult textbooks. Content-wise, it covers a wide range of examples in Python and offers practical guidelines on how to apply statistical methods to data science, including techniques that “learn” from data, unsupervised learning approaches for extracting meaning from unlabeled data, and more.
There are even guidelines on how to avoid misusing methods, and advice on exactly is essential. Furthermore, this book explores R and Python codes, which makes it easy to compare the two and understand the syntax for one language better if you are familiar with the other. The book is handy for beginners in Data Science too! You can combine it with youtube videos and other data science online materials.
If you want to understand how big data and data science can fit in your business and how you can leverage it for a competitive advantage, then this book by data science experts Foster Provost and Tom Fawcett has got you covered!
“Data is the foundation of new waves of productivity growth, innovation, and more affluent customer insight. The authors’ deep applied experience makes this a must-read — a window into your competitor’s strategy “, says Alan Murray, the Serial Entrepreneur; Partner at Coriolis Ventures.
Data Science for Business does a fantastic job at introducing the basic principles of data science in the business world and provides real-world examples to illustrate these principles. As such, it will provide you with helpful knowledge and methods that can support decision-making and value from your company data. Not only that, this book guides you on how to participate in business data science projects brilliantly. It’s a must-have book that should be on the shelf of anyone who wants to work in the data science field.
In this book, John W. Foreman, Chief Data Scientist at MailChimp.com, takes you through fundamental easy to read data science approaches, how they work, how to use them, and how they can benefit your business, whether small or large.
The book has been reviewed as one of the best practical guides to business analytics as it’s all about processing raw data into actionable and valuable insights quickly and effortlessly.
John makes complex data science concepts very simple and demonstrates them with real-world applicabilities. Getting your hands on this book is worth all the effort, and reading it could be the next big you do — it will provide you with a hands-on practical guide in Data Science.
Practical Data Science with R by Nina Zumel and John Mount is an excellent book with good content, clear text, and great examples of data science methods using R. This programming language is widely used for data science applications.
This is the first book offering significant practical and technical advice to data science procedures, modeling, and programming.
If you have basic programming and statistics knowledge, this book will provide you with a valuable and advanced introduction to data science.
It will guide you to apply the R programming language to statistical analysis techniques and covers carefully explained examples in real-world use cases based on marketing, business intelligence, and decision support.
Thank you for reading! Which Data Science Book would you add to the list? Let me know in the comments.
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