The Top 10 Data Science Books You Need to Read

• Read original article here

Data science is quickly becoming one of the world’s most important and lucrative fields. Sifting through and creating a sense of large data sets can be life-changing, helping people do everything from better understanding consumer trends to boosting their productivity. Whether you’re just starting your journey to becoming a data scientist or looking to brush up on the latest industry knowledge, the following 10 books are essential for those interested in data science.

This is a compilation of the work and insight of six data analysis experts. They discuss how they learned the basics and some personal lessons on becoming better analysts. The information in this book is geared toward people who want to know more about data analysis but don’t have a statistics background.

Topics include finding value in data, working with others, storytelling with data, and avoiding common mistakes.

In the book How Not To Be Wrong: The Power Of Mathematical Thinking, author Jordan Ellenberg explores how mathematics can help people make better decisions and avoid common mistakes. He presents many examples of how math can be used in everyday life. This book is an exploration of the power and simplicity of math. Being wrong in this book does not mean a flaw but something familiar for us all. In fact, one mathematician said:

Machine Learning for Dummies is an excellent book for those new to data science. It provides an overview of the field and shares some basic techniques you can use immediately. The author does not consider any prior knowledge about machine learning, so it’s an excellent place to start if you want to learn more about this topic. In addition, many exercises help readers test their understanding through the book. It also includes free code on GitHub, where readers can experiment with real-world machine-learning algorithms. This is a fantastic read for those looking to start data science or simply brush up on previously learned topics!

Python for Data Analysis is a comprehensive and beautifully written introduction to data wrangling with Python. It provides extensive coverage of the tools needed for practical data analysis, from basic NumPy and Pandas operations to database interactions.

The book’s emphasis on clear, detailed explanations and practical examples will ensure that readers understand these critical tools thoroughly, whether they are new to programming or not. More experienced programmers who wish to use Python as a simple tool for all types of data analysis should find the book an indispensable reference.

Programming Collective Intelligence is one of the best books on data science that covers the breadth of the topic, from data collection and processing to machine learning and social networks. It provides an in-depth technical overview for those familiar with programming techniques and includes practical examples for non-technical readers.

The book will teach you how to use data mining algorithms like clustering, classification, and regression analysis, as well as more advanced topics such as neural networks, genetic programming, and support vector machines.

Dan Ariely discusses his experiments and experiences with decision-making in his book to discover how and why people make irrational decisions. He shares how he has used this knowledge, for example, in designing a better market for movies.

This book is filled with compelling examples and experiments that will leave you both amused and thinking about your own decisions. Reading this book is an absolute must if you’re curious about what drives people’s irrational behavior.

Superintelligence was written by Nick Bostrom, a Swedish philosopher specializing in artificial intelligence and existential risk. The book deals with the possible future outcomes of AI development and how these outcomes could relate to human beings.

It discusses what we can do now to better prepare for these scenarios and what steps we can take alone or as communities to prevent an unfavorable outcome.

Bostrom examines some of the many ways that superintelligence might emerge from our current position, including through machine learning algorithms trained on big data sets; self-improving software based on algorithms developed using deep neural networks; autonomous robots capable of engineering solutions to unforeseen problems; as well as through developments in quantum computing.

In his book The Signal And The Noise: Why So Many Predictions Fail — But Some Don’t, author Nate Silver distinguishes between the signal and the noise. He argues that while prediction is difficult, it’s not impossible. To succeed in our predictions, we need to know what information is useful and which isn’t (signal) and be willing to admit when we’re wrong (noise). It’s an excellent read for anyone looking for insights into how data science works. Silver tackles some of the most complicated aspects of forecasting, like regression analysis and predicting baseball players’ performance. His discussions are clear, concise, and fascinating.

R Programming Cookbook is an all-inclusive guide packed with recipes for solving common data science problems. The book covers data types and structures, plotting, regression analysis, statistical tests, time series forecasting, univariate and multivariate data analysis, and advanced topics like high-dimensional data analysis and Bayesian methods. With over 500 pages and dozens of code examples and case studies, this comprehensive guide will be the perfect companion for anyone interested in data science.

SQL For Marketers Who Know Excel (Hands-On Series) is the first book in the series and teaches readers how to use SQL in Excel. The book was written by Joe Mako, a seasoned marketing professional and data scientist using SQL for over a decade.

In his book, Joe walks readers through how he uses Excel as a powerful tool for extracting insights from databases and other data sources like web server logs. He then shows readers how they can automate this process with SQL. The hands-on exercises are designed so anyone with basic knowledge of Excel can follow along at their own pace, learning as they go.