Data science has become one of the IT industry’s most paid and recommended domains. As many companies are adopting the new way of data science applications to solve their business problems, there is a requirement for skilled data science professionals. If you want to be an expert in data science, this article will help you with some top data science books that are readily available and help you learn data science from the beginning.
- Data Science from Scratch (2nd Edition)
Written by Joel Grus, The Data Science from Scratch is another exciting book for beginners to study data science. This book was first released in April 2019 and highlighted the different data science tools and algorithms necessary to implement machine learning algorithms.
If you have prior experience in mathematics and programming language, this book helps you quickly start with statistics in data science. While reading this book, you can also learn the hacking skills needed to be a data scientist. The book guides you on K-nearest neighbors, decision trees, logistic regression, clustering models, and linear regression. It also exposes you to network analysis, natural language processing, and more.
Link to the book: Data Science from Scratch
- Approaching (Almost) Any Machine Learning Problem
Written by Abhishek Thakur, Approaching (Almost) Any Machine Learning Problem is one of the best books for people with theoretical knowledge of machine learning and deep learning algorithms. This book focuses more on solving machine learning and deep learning real-life problems rather than explaining the basics of it.
If you want to implement deep learning and machine learning algorithms with code, this book is for you. With this book, you can explore topics like supervised learning, unsupervised learning, cross-validation, evaluation metrics, feature engineering, feature selection, arranging machine learning projects, image classification, text classification, regression, stacking, and model serving.
Link to the book: Approaching (Almost) Any Machine Learning Problem
- Elements of Statistical Learning
The Elements of Statistics Learning book by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, is the complete book for machine learning fundamentals. It covers everything from supervised machine learning methods, graphical models, unsupervised machine learning methods, and high-dimensional problems. This book includes chapters on neural networks, support vector machines, classification trees, random forests, ensemble methods, spectral clustering, and non-negative matrix factorization.
Link to the book: Elements of Statistical Learning
- Introduction to Data Science: Practical Approach with R and Python
Introduction to Data Science: Practical Approach with R and Python, by B. Uma Maheshwari and R. Sujatha, covers all the basic data science concepts. It guides you on the insights and golden rules needed in analyzing data. This book is used as a practical guide by many engineering/science/MBA students interested in data science.
The first author of this book, Dr. B. Uma Maheshwari, is an Associate Professor of the Decision Stream at PSG Institute of Management, Tamil Nadu, India. Whereas Dr. Sujatha, the second author of this book, is an Associate Professor at PSG college of Technology in PSG Institute of Technology, Tamil Nadu, India.
Link to the book: Python Machine Learning By Example
- Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition
Written by Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition covers the practical aspects of machine learning with efficient tools. It consists of concrete examples, minimal theory, and two production-ready Python frameworks like TensorFlow, Scikit-Learn, and Keras. With this book, you can learn techniques from simple linear regression to deep neural networks.
You need basic Python programming language knowledge to implement the algorithms included in this book. While reading this book, you will also get exposed to the topics like net architectures consisting of convolutional nets, recurrent nets, and reinforcement learning. You can also explore several training models, such as support vector machines, random forests, ensemble methods, and decision trees.
Link to the book: Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow
- Deep Learning From Scratch: Building with Python From First Principles
The Deep Learning From Scratch: Building with Python From First Principles by Seth Weidman gives you a comprehensive introduction to data science with deep learning algorithms. With this book, you can start with the basics of deep learning and then move to advanced architectures, implementing everything from scratch.
The author explains how neural networks work using the first principles approach. You can also learn to apply multilayer neural networks, recurrent neural networks, and convolutional neural networks from scratch. While reading this book, you can understand how neural networks work mathematically, conceptually, and computationally on real-life deep learning projects.
This book explains all neural networks, code examples, and mathematical expressions in simple language. With this book, you can implement neural networks using the popular PyTorch framework.
Link to the book: Deep Learning From Scratch: Building with Python From First Principles
- R for Data Science
Written by Garrett Grolemund and Hadley Wickham, R for Data science is an exciting book for beginners to learn data science with R. With this book, you can learn to get your data into R, transform it, visualize it, and model it accordingly. The book also explains the concepts of statistics with other data science processes like data cleaning and filtering. It also guides you in data transformation with concepts like median, standard deviation, average, and more.
The book helps you understand the raw data and how it is processed. You can also learn the different methods for transforming data and processing it to gain meaningful insights using R with the help of this book.
Link to the book: R for Data Science
- Introduction to Machine Learning with Python: A Guide for Data Scientists
Introduction to Machine Learning with Python, written by Andreas Muller and Sarah Guido, was released in October 2016. This book is freely available on the O’Reilly platform with a 10-day free trial.
While reading this book, you can learn different practical ways to build machine learning models. This book focuses on the basics of machine learning applications with Python and the Scikit-learn library. The authors of this book have prioritized more on practical aspects of machine learning rather than the mathematics behind it. You need to be familiar with the Python libraries like NumPy and matplotlib to understand the concepts in the book more easily.
Link to the book: Introduction to Machine Learning with Python
- Practical Statistics for Data Scientists
The Practical Statistics for Data Scientists book by Andrew Bruce and Peter Bruce is a good option if you are a beginner and want to master data science. This book was released in May 2017, and you can read it now on the O’Reilly platform with the 10-day free trial.
This book provides practical guidance on applying various statistical methods to data science and how to avoid their misuse. If you are familiar with the R programming language and have some statistics knowledge, you can quickly learn the concepts included in this book.
The book explains important concepts like randomization, distribution, sample bias, and sampling. It explains how these concepts are relevant in data science with examples.
Link to the book: Practical Statistics for Data Scientists
- Business Data Science: Combining Machine Learning and Economics to Optimize, automate, and Accelerate Business Decisions
The Business Data Science book by Matt Taddy explains the basic data science concepts from the business perspective. With this book, you can learn how data science can bring value to organizations. This book explains the fundamentals of machine learning and how to use it to solve business problems and implement it with R programming language.
Matt Taddy, the developer of the Big Data Curriculum at the University of Chicago Booth School of Business, realized that many books had the basic concepts and statistics for machine learning. Still, they lacked the proper description of machine learning concepts for solving business problems. Therefore, he introduced different tools and machine learning techniques used in businesses to solve real-life problems.
This book is available online on Amazon. It is the best option for organizations that want machine learning concepts for their business strategy.
Link to the book: Business Data Science
