For the next ten years, a rewarding and well-liked profession in data science is expected to stay exciting and demanding. Businesses are using data science applications more and more, which is driving up demand for qualified personnel. Data science, which has its roots in computer science, statistics, and predictive analytics, is widely used in the fields of policy work, marketing, finance, and healthcare.
To help people on their data science journey, this blog presents the best books for beginners in data science. Now let’s get started!
Data Science for Beginners
Author: Andrew Park
This four-book set is ideal for those who are new to data science as it covers the fundamentals of machine learning, data analysis, and Python. With each book, you will gain a firm grasp of these concepts and receive step-by-step instructions and tutorials on how to use the widely performed Python programming language to create neural networks, manipulate data, and master other topics. The set includes:
- Python for Beginners
- Python for Data Analysis
- Python Machine Learning
- Python Data Science
Data Science for Dummies
Authors: Lillian Pierson (Author) and Jake Porway (Foreword)
Edition: 2nd Edition
For those who are just getting started, Data Science for Beginners provides an introduction to the field. Lillian Pierson explores the fundamentals of data science, including artificial intelligence, big data analytics, Hadoop, Spark, machine learning, NoSQL databases, MPP platforms, and MapReduce. While not a how-to book, the book provides an overview of data science. IT experts and students studying technology will find this book to be more comprehensible and accessible due to its complexity.
Introduction to Probability
Authors: Joseph K. Blitzstein and Jessica Hwang
Edition: 2nd Edition
One of the greatest books for learning about probability is this one because it provides practical applications along with concise explanations. It offers a solid grounding in fundamental ideas, terminology, and methods for comprehending statistics, uncertainty, and unpredictability. Numerous case studies and applications are included in the book. The print book version comes with a code that can be used to access the eBook version for free.
R for Data Science
Authors: Hadley Wickham & Garrett Grolemund
Anyone who is enthusiastic about using the R programming language for data science projects should read this book. It is appropriate for individuals wishing to learn a new language for data science or delve into intriguing and uncommon facets of the discipline because it offers concise explanations.
Data Science from Scratch
Author: Joel Grus
This book covers a wide range of topics including data visualisation, probability, hypothesis testing, linear algebra, statistics, machine learning, neural networks, recommender systems, network analysis, and more. It begins with an intensive introduction to Python. It provides a thorough introduction to data science and is suggested for individuals seeking a comprehensive manual.
Frequently Asked Questions
1. Is a career in data science promising?
Yes, among the most in-demand and fastest-growing career categories in technology are those in data science.
2. Is data science a difficult field?
Yes, it might be challenging to get into a data science degree because it requires a strong foundation in math, statistics, and computer programming. But anyone who puts in the necessary time and effort can learn the skills and information needed to succeed in this field.