Must-read Books for Data Scientists
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There are countless books in the field of data science. In this article, we will introduce you to the books that have helped us on our way to becoming data scientists.
To get started in the world of data science, we recommend the book “Numsense! Data Science for the Layman: No Math Added” by Annalyn Ng and Kenneth Soo. The book provides a comprehensive introduction to the fundamentals and applications of Data Science. The authors dispense with mathematical details. It teaches the concepts, methods and techniques needed to extract valuable insights from data. The authors use practical examples and case studies to enhance understanding. Read this book if you are still a newbie!
Data Science Basics
“The Elements of Statistical Learning: Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
The book includes a variety of statistical learning methods. It covers important concepts and methods, including decision tree algorithms, linear regression, support vector machines and neural networks. The book provides a detailed insight into the theoretical concepts and practical applications of statistical learning. It is a good resource for students and professionals.
This book taught us the basics of machine learning. The authors describe the individual concepts clearly and comprehensibly. We still use the book as a reference today.
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
The book deals with the basics and advanced concepts of deep learning. It includes topics such as neural networks, deep learning architectures and optimization techniques, and provides a solid introduction to the practical application of deep learning algorithms. The book is very mathematical, which helps you to understand the concepts comprehensively.
In our view, this is the only book you need to read to fully understand Deep Learning. This book is the bible of Deep Learning. If you are into Deep Learning, this is a must-read book.
Practical Data Science
“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems” by Aurélien Géron
This book gives you an intuitive understanding of the concepts and tools through concrete examples, minimal theory and production-ready Python frameworks (Scikit-Learn, Keras and TensorFlow).
You will learn how to apply machine learning algorithms. The book supports you in implementing everything from simple linear regression to deep neural networks. Furthermore, complex methods such as generative adversarial networks, autoencoders, diffusion models, and transformers are also addressed in this book. You can then apply what you have learned directly in exercises.
Programming experience is advantageous for the book. If you have no programming experience, we can recommend the book Python Crash Course by Eric Matthes. Python Crash Course is the best-selling programming book in the world, with over 1,500,000 copies sold!
We can recommend this book to newcomers and experts in Machine Learning. The authors explain the individual concepts intuitively, and the practical exercises motivate us to continue learning.
“Dive into Deep Learning” by Aston Zhang, Zachary C.Lipton, Alexander J. Smola and others
This book is an insider tip for all Deep Learning enthusiasts. The book is being developed as open source and is available in many languages. Leading experts in the field of Deep Learning continuously improve the book.
The authors explain the theoretical basics of Deep Learning in combination with practical examples. Each section contains an executable Jupyter Notebook. You can adapt the code and directly gain practical experience. This interactive learning experience motivates you to dive deeper and deeper into the subject matter. You can read the book for free.
We use this book regularly to read up on concepts. This book is very detailed and contains the experiences of a wide range of experts. Check out the book if you don’t know it yet.
Link to the book: Dive into Deep Learning
Data Science for Business
“Data Science for Business: What you need to know about data mining and data-analytic thinking” by Foster Provost and Tom Fawcett
The book is based on an MBA course by Provost taught at New York University. The authors introduce you to the fundamentals and applications of data science in the business context. They explain the concepts and techniques that help companies collect and analyze data. You can use these techniques and concepts to gain business insights.
The book provides a practical perspective on data science and helps managers and executives make data-driven decisions. The knowledge you learn will make you fit for a data-driven future.
“Storytelling with Data: Let’s Practice!” by Cole Nussbaumer Knaflic
In this book, you will learn how to tell stories with data. You learn a lot about language and mathematics at school. In language, you learn how to make sentences from words. In mathematics, you learn how numbers work. But rarely do you learn how to tell stories with numbers.
The book contains many exercises with real-life examples. The exercises are designed to get you thinking. Each exercise has detailed step-by-step instructions with explanations.
Good data visualization makes the difference between success and failure. Few people are well-trained in data visualization. Many people are not aware of this. Read this book to tell stories with data.
Conclusion
Continuous learning is essential for personal and professional growth. Today, there are countless learning materials, and it isn’t easy to find the right ones. We prefer text-based learning in the form of books. The featured books have helped us in our journey through the world of data science. Hopefully, the list will be helpful for you too. Happy reading!
👉🏽 On our books page you will find an up-to-date list of the best data science and coding books.
Thanks so much for reading. Have a great day!
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