5 Data Science Books you should read | Tangent Works - Advanced Forecasting

5 Data Science Books you should read

You’re into data science and you want to keep learning about AI & ML? Our data scientists and machine learning experts selected these books for you. We hope that you enjoy this list!




1. Data Science for Business by Provost and Fawcett

Must read according to our Machine Learning Expert Elke.

Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. In this book, you will learn:

  • To understand how data science fits in your organization—and how you can use it for competitive advantage,
  • To treat data as a business asset that requires careful investment if you’re to gain real value,
  • To approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way,
  • General concepts for actually extracting knowledge from data,
  • To apply data science principles when interviewing data science job candidates.



2. The Master Algorithm by Pedro Domingos

Must read according to our Client Success Manager Philip.

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own.

In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner–the Master Algorithm–and discusses what it will mean for business, science, and society. If data-ism is today’s philosophy, this book is its bible.



3. AI Crash Course by Hadelin De Ponteves

Must read according to our Machine Learning Expert Elke.

Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch.

AI Crash Course teaches everyone to build an AI to work in their applications. Once you’ve read this book, you’re only limited by your imagination.



4. Principles of Econometrics by Hill, Griffiths and Lim

Must read according to our Machine Learning Expert Elke.

Principles of Econometrics is an introductory book that will help you to gain a working knowledge of basic econometrics so you can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field.



5. Artificial Intelligence: A Modern Approach by Stuar J. Russell & Peter Norvig

Must read according to our Data Scientist Jakub.

The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). Readers will get up to date on the latest technologies, discover interesting concepts in a unified manner, and learn the ins and outs of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.


(book descriptions based on Amazon.com / visuals based on Amazon.com & Bol.com)


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