Nowadays the world has become a Data-Driven, so Data Science is like the 6th sense of Humanity. As we all know Data Science has become the highest-paid and most famous field in current markets and also has the potential to grow more and more in upcoming years and challenges in the future. According to these trends, there will be more and more opportunities for Data Science with a handsome amount of salary. It is very necessary and crucial to be updated and upskill in the market due to higher competition in the market. The easiest and efficient way to update the skills and get ready for Data Science is the available Data Science books to read. The following books will not only help with the Problem-Solving but also with mathematics, probability, statistics, programming, machine learning and projects on Data Science.
Following are the top 10 books you must read to learn Data Science:-
1) Practical Statistics for Data Scientists
The main part of Data Science is Statistics and most of the Data Scientists do not have formal knowledge of statistics. Courses and books having basics of Statistics do not include statistics for Data Science. The second edition of this practical guide also includes practical statistics examples for python and R. Many Data Scientists use Statistics for Data Science but have a lack of a deeper perspective. If you are familiar with R and Python Languages but want a brief knowledge in Statistics for Data Science then this reference is the best bridge for learning Statistics for Data Science. Even in the updated edition, you can also learn Exploratory data analysis, Data and sampling distributions, Statistical experiments and significance testing, Regression and prediction, Classification, Statistical machine learning, and Unsupervised learning.
2) Python Data Science Handbook
You can easily learn the main components for data science like Python, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools with Python Data Science Handbook. In short, it has a complete reference for scientific computing with python. With this handbook you can learn Jupyter as a computational environment for python, manipulation with arrays in python using Numpy, Dataframe with tabular data using Pandas, Matplotlibe for visualizations and Scikit-learn for Machine learning models.
3) Data Science from Scratch: First Principles of Python
In this book, you will learn how to implement data science tools from scratch and learn how algorithms in the tool work. If you have a good background in aptitude in mathematics this book will help to get powerful knowledge for statistics and maths for Data Science. It will boost your Data Science skills to become a Data Scientist. Learners may get or find courses online for Data Science with the collection, scraping, cleaning and visualizing outputs after that training different machine learning models but they will not teach you what is internal statistical knowledge for the same this book provides the internal process of algorithms.
4) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data
Data Science and Big Data Analytics are about the power of data for new insights. The book covers all the activities and tools that Data Scientists do and needs. The book mainly focuses on concepts, principles and practical applications that are applicable to any industry or technological environment. All the examples explained can be replicated with any open-source software. This book will also help to implement the data science life cycle and also explain tools to use Big Data.
5)Naked Statistics: Stripping The Dread From Data
Naked Statistics by Charles Wheelan is all about using statistical problems for any statistical questions. This book is a lifesaver for many statisticians than Stats 101. The book strips away the technical details and focusses on the aspects and intuitions of the statistical analysis. Book refers to some key concepts such as inference, correlation, and regression analysis reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
6) Introduction to Statistical Learning
An Introduction to Statistical Learning will give you an overview of the field of Statistical learning. It is essential for making sense of vast and complex data that emerged in the fields of biology, finance, marketing and astrophysics from the past 20 years. This book includes some of the modeling and physical techniques along with its applications. The book includes topics like linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
7) Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
This book offers fascinating data about insights into everything like economics, ethics, sports, race, sex, gender and more, all drawn from the world of big data. How many white people voted for Obama? , Does School affects success in your life? , Do parent favors boy child than girl child? , Do some films affect Crime rates? , How to beat Stock Market? After investigating these questions Seth Stephens-Davidowitz helps us understand ourselves and our lives better. In this way, he explains how the world is a lab and to apply data Science to these problems.
8)Big Data – A Revolution that Will Transform How We Live, Work, and Think
Big data refers to our ability to collect vast data and analyzing them instantly. It also can derive some surprising conclusions from it. Big data will change the way we think about business, economics, health, politics, and education in the upcoming years. Book also gives information about the threat to privacy in this Big Data world. It also shows how Big Data can predict the near future. In this book, two people brilliantly teach us what is big data, how it will change our lives and how to be protected from its causes.
9) Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
Bayesian methods are extremely powerful and natural. Most of the Bayesian methods are about Strong mathematics background and have a complex mathematical analysis which is harder for people without mathematics background. The book explains Bayesian methods in computational reference. The book refers to Bayesian methods with probabilistic programming with PyMc language which is closely related to Python and its libraries like NumPy, pandas or matplotlib. It also has some algorithms for domains ranging from finance to marketing
10) Business Analytics – A Data-Driven Decision-Making Approach for Business
This book refers to models based on fact-based data and uses this data for future outcomes in Business. It will also help in Visualizing and predicting future outcomes and performance. In the booming trend for Data Science in Business, this book is very informative for Business analysis. It has many terms, tools, and methods of analytics together. Book mainly includes the predictive models and neural network for Business intelligence for the Business future performance and outcomes prediction.