There are a number of points one must keep in mind while trying to master data science. Especially for those who are surrounding themselves with numbers and mountains of information. What’s more, the top organizations around the world are constantly in need of data science experts and specialists. So, it helps to continuously refresh your knowledge of the basics of data science.
And if you’re just a beginner and have a dream of developing your potential in order to offer your knowledge to the world of science, you will require the following skills:
- Accuracy in labor;
- Fast calculation of the data;
- Understanding of all mathematical principles;
- The mathematical creativity (capability of noticing consistent patterns)
Of course, there are many other skills that are necessary for being a data scientist. If you want to gain more insight into it, then you can find them here at this scientific portal.
A good data scientist should always bring one’s skills to perfection. Constant acquisition of new knowledge is a part of growth upon oneself. Reading – one of the most efficient and easiest means of gaining new abilities and the Internet is the place where you can find a portion of decent books on math.
Continue reading the article to get the list of the best free must-read books which are of high value for data science as they can build strong fundamental knowledge in the field and grant the answers to all the questions which a person might have.
Top 8 Best Books on Statistics and Mathematics
1. Pattern Classification — Richard O Duda
This is a real classic of mathematics study books. Although it was printed in 1973 and updated only in 2000, all the material that is introduced in this manual is still actively used in data science. The best thing about this book is the formation of the text in the shape of patterns. That definitely makes the recognition and memorizing of the algorithms faster and more productive.
2. Practical Statistics for Data Scientists: 50 Essential Concepts — Peter Bruce and Andrew Bruce
It introduces the methods of statistics through the prism of Data Science exclusively. Such an approach makes the book extremely valuable and important for all experts in the field. It will teach the readers about the importance of data analysis and methods of its application for solving various situations. You will also be introduced to basic techniques which are used for data prediction and programming of the machine. Additionally, you can find more similar books on programming with practical statistics online.
3. Naked Statistics: Stripping the Dread from the Data — Charles Wheelan
This is truly the best book on statistics as it introduces the information in a rather simple shape. Most people came to think that work with numbers is an incredibly difficult thing but this piece of writing is devoted to breaking the stereotype as it shows the statistics science in a totally different color. The author describes effective tools for the organization of the process and which of them is more appropriate for a peculiar situation. After reading the book that was written in such a friendly manner you will get rid of all the fears about statistics.
4. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data — Hadley Wickham and Garrett Grolemund
It is about R programming language and its capability to transform that pointless number of figures in the source of knowledge and inspiration. The authors describe the fundamental R working packages which would be efficient for data organization and make the scientific process run faster and smoother. Learn to design data in the fastest way with the R language.
5. Introduction to Linear Algebra — Gilbert Strang
This one of the highly appreciated books which presents Linear Algebra in the easiest shape. It is written in a manner that slowly leads the reader to progress and helps to understand the topic better. With this book, a data scientist will either acquire or improve the knowledge of vectors, eigenvalues, equations and other elements of linear algebra that are richly used in machine learning.
6. Introduction of Math of Neural Networks — Jeff Heaton
Neural networks are the future of data science. This book will awaken or warm up your interest in the subject. The writer has a wonderful capability to simplify the topic that seems hard to apprehend. The author introduces a vast spectrum of means which are applied for the development of neural networks of all types.
7. Advanced Engineering Mathematics — Erwin Kreyszig
This is a renowned book in the field of machine learning. The book would be equally effective both for pros and for college students who face some difficulties with the subject. It is dedicated to developing new math skills and understanding of all the basic branches of mathematics science.
8. Elements of Statistical Learning — Trevor Hastie and Rob Tibshirani
It is devoted to people experienced in machine learning and who wish to get some extra knowledge in the field. The book offers the algorithms of a higher class and difficulty including neural networks and kernel methods. It is also filled with an abundance of examples for a better understanding.
Hopefully, you will find this information helpful for lean manufacturing, income calculation, prediction of various events and other fields where data science participates.