As the best-selling authors of Ace the Data Science Interview and creators of SQL interview platform DataLemur, we've read a TON of Data Science books over the years. Here's the absolute 13 best books for Data Scientists that want to take their career to the next level. While many of these books are directly about Data Science and Machine Learning, we also threw in some of our favorite business and product management books for Data Scientists. Let's face it: our field is insanely interdisciplinary, and as such, it's beneficial to read broadly.

The 3 best books to learn Data Science are Advancing Into Analytics for people completely new to data science, R for Data Science for a practical introduction to Data Science in R, and Data Science for Business for an introduction to how Data Science is applied to solve real-world business problems.

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The 3 best books for Data Scientists to learn Machine Learning are Intro to Statistical Learning for the hard-core theory behind ML, the Hundred-Page Machine Learning book for a quicker crash-course into the math and concepts behind ML, and Hands-On Machine Learning with Scikit-Learn and TensorFlow for a practical tutorial on building ML models.

Intro to Statistical Learning (& it's even harder cousin, Elements of Statistical Learning) are both free & amazing resources for learning machine learning theory. For Data Science & Machine Learning practitioners, it's never a waste of time to brush up on your fundamentals! While hailed as the bible of ML, be warned: it's challenging to read and most people give up after a few chapters! If you need a more compact intro, check out the next ML book suggestion.

For a lighter introduction to the fundamentals of machine learning, this 100 page book (well...137 pages but who's counting) strikes the right balance between enough math to explain the central ideas in ML, without overwhelming the reader.

True to its name, this book is the best hands-on introduction to Machine Learning. Hands-On Machine Learning is rich in concrete examples, and light on theory, making it the perfect read for someone who is already familiar with the fundamentals of Data Science and ML but is now hungry to tangibly apply what they know.

The 3 best books for Data Scientists who are trying to succeed in their career and land data science jobs are Ace the Data Science Interview for interview prep, the Data Science Handbook for career and life insights from top Data Scientists, and So Good They Can't Ignore You to help you more broadly design a successful career.

If you're looking for the eBook of Ace the Data Science Interview, we're sorry to announce that there aren't any online PDF or Kindle downloads of Ace the Data Science Interview available. However, you'll find many of the SQL interview tips from the book on DataLemur's 6000-word guide to SQL interview prep. On DataLemur, you'll also find 100+ SQL Interview Questions from FAANG and plenty more Machine Learning Interview questions too!

This light-read interviewed 25 leaders in Data Science - both Data Science thought leaders like DJ Patil, as well as Data Science practitioners who are leading the most innovative data teams at companies like Airbnb, Netflix, and Facebook. It has a mix of career advice for Data Scientists, perspectives on the field, and general life advice.

Luckily, we compiled this list of data science books to help you further your knowledge base, ranging from introductory overviews to more advanced content on deep learning, bias in algorithms and more. With recommendations from experts and our own personal picks, here are the data science books to pick up to learn more about the subject.

This book will teach you how to run predictive analytics. In the data science world, there are two main programming languages: Python and R. There are pros and cons to both, but this book is specifically for Python. Scikit-Learn, Keras and TensorFlow are all libraries of machine learning and deep learning functions within the Python programming languages.

Data science can require having mathematical knowledge in linear algebra, calculus and statistics, though the amount of math realistically used will depend on the role and specific task needing to be accomplished.

In the past few years, public interest in data science has surged. What had been a fairly esoteric field is now a common topic in the news, in politics and international law, and in our social media feeds. Data literacy is becoming a highly desired skill in every industry, and consumers enter data points into massive business intelligence systems every day.

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 visualisation, acting as a practical go-to technical resource.

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.

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 book also explores broad overviews of topics like data engineering, programming languages like R and Python, machine learning, algorithms, artificial intelligence and data visualisation 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.

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.

Disclaimer: Tableau does not officially endorse or profit from any products, or opinions therein, listed in this article and as such this page does not engage with any affiliate link programs. This article is intended purely for educational purposes and the above information about products and publications is made available so that readers can make informed decisions for themselves.

Data science can be learned independently, yes. Because of the variety of internet resources, including free and paid courses, tutorials, books, and blogs, anyone with the discipline and commitment may master data science abilities. However, the method needs significant commitment because learning data science on your own may be challenging and time-consuming.

To start learning data science on your own, build a strong foundation in the principles of programming, statistical concepts, and data manipulation. Many online resources and platform courses, such as Analytics Vidhya and Coursera, can help you with this.

This data science handbook offers a strong foundational grasp of Python, data analysis, and machine learning for those who are completely new to the field. Each book offers tutorials and step-by-step instructions on how to use the well-liked Python programming language to build neural networks, interact with data, and learn the fundamentals.

Knowledge of Machine Learning is critical for a data scientist. This book by Andreas C. Mller and Sarah Guido helps you cover the basics of Machine Learning. If you practice with the book for a substantial time, you can build machine learning models on your own. This book has all the examples with Python, but even if you do not have prior knowledge of Python programming language, you will be able to learn it through this book that very well serves as a python data science handbook. This book is for beginners to understand the basics of ML and Python.

This book also teaches the reader how to dig beyond standard tools to get to the essence of their data. It also discusses the topic of using your data to create a captivating and informative narrative. This book can be a compelling read for those interested in data science for business.

This book by Andriy Burkov is amazing. I struggled to find a book that could quickly convey challenging subjects and equations after reading many books that attempted to teach machine learning from numerous approaches and perspectives until Andriy Burkov managed to do it in roughly 100 pages. It is elegantly written, simple to comprehend, and has received the support of influential thinkers like Peter Norvig. Must I say more? Every data scientist, regardless of experience level, needs to read this book.

EMC education service has published a book titled Data Science and Big Data Analytics. One of the top data science books available on Amazon, it covers the range of techniques, approaches, and equipment data scientists employ. The book focuses on principles, concepts, and real-world examples. It applies to any industry, technological setting, and educational process. It supports and explains concepts with examples that readers can replicate using open-source software.

Dawn Griffiths is the author of the book Head First Statistics. The author makes this often dull subject come to life by teaching you everything you need to know about statistics through readings packed with riddles, narratives, quizzes, and real-life illustrations. You can learn statistics from this book and utilize them to comprehend and support important issues. The book also covers the use of graphs and charts to visually demonstrate data. Last but not least, the book demonstrates how to compute probability, expectation, etc.

Python is yet another popular programming language in data analytics. Moreover, data science relies on analytics. So, this book by Wes McKinney serves as a comprehensive introduction to data science for those learning the fundamentals of Data Analytics using Python. The book maintains a fast-paced yet simple style. It brilliantly organizes and arranges content for readers, offering a glimpse into the world of data scientists and analysts and their work types. 152ee80cbc

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