In LinkedIn’s 2020 Emerging Careers Report, Artificial Intelligence Specialists and Data Scientists are ranked among the top 1st and 3rd rising jobs in the United States, respectively.
We may hope to see more Data Science and AI job posts and wages in 2021 as corporations realize the utility of data further than the hype. The average compensation for a data scientist is $100,560 annually, as per the Bureau of Labor Statistics. That compensation will rise in tandem with the demand.
Data science experts and hobbyists alike, irrespective of their experience or skill level, must constantly hone the saw. This article will aim to compile a list of some of the most useful books for improving your data science skills.
Top 8 Data Science Books
The following books give a high-level overview of the Data Science methodology as well as some of the numerous commercial applications.
1. Predictive Analytics
By Eric Siegel
This book is a thorough yet approachable resource for anybody interested in learning how data analysis functions, delving into a variety of real-world applications such as loan risk, terrorist acts, crime forecasting, and politics, to mention a few.
2. Data Smart
By John Foreman
The way this book teaches data science ideas using none other than Microsoft Excel is fascinating. Overall, the book serves as an excellent example of how data science is fundamentally tool agnostic.
The concepts and arithmetic underlying the algorithms are the same regardless of what language, platform, or program you choose to accomplish your data science.
By Roger D. Peng and Elizabeth Matsui
This book gives a thorough explanation of the data analysis process. Furthermore, in spite of the availability of several technologies, data analysis is basically an art, along with an iterative procedure in which knowledge is learned at each stage.
By Charles Wheelan
Statistics may be a difficult subject to grasp at times. Furthermore, concentrating on the specifics might obfuscate the meaning of the measures we use at the job. Author Charles Wheelan explains fundamental topics like inference, association, and regression analysis in an entertaining and less intimidating approach in this book.
By David Spiegelhalter
The Art of Statistics, written by famous statistician David Spiegelhalter, demonstrates how we may gain knowledge from existing data and use statistics to solve a range of issues.
By Claus O. Wilke
This book explains the fundamentals of data visualization while contrasting excellent and terrible examples. It is just a book that may educate you on how to comprehend the logic behind good visualizations and how to create more effective plots that convey the proper information.
By Cole Nussbaumer Knaflic
Anyone who wishes to improve their ability to communicate data in a clear, succinct, and pictorial manner should read this book. With several concrete examples, this book tells you the principles of data visualization and how to successfully communicate with data.
By Andy Kriebel
This book is a follow-up to the #MakeOverMonday initiative, in which individuals of the data visualization community discuss how they enhanced current charts and data. It underlines that, while there is a lot of variation in how visualizations are designed, there are several fundamental approaches you can use to make sure your chart stands out.
The aforementioned is a list of books that we believe you must read this year. So, whether you’re searching for the greatest all-around book, the best for newcomers, or the best value, we believe there’s a book out there for every budding data scientist. Just as data science is an evolving science, the internet too is changing at a massive rate which includes the latest iteration of the internet known as web 3.0. To learn more about what web 3.0 is, click here.
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