Best Books to Learn Data Science for Beginners and Experts
Data Science is the most revolutionary field in the tech industry these days! All companies, whether they are smaller businesses or tech giants, use data to understand market trends and retain their competitive edge. And since more and more companies are using data science, there is an increasing demand for data scientists! This article covers the best books to learn Data Science, both for people who are new to the field and those who just want to refresh their memory!
Each of these books is extremely popular and considered a gold standard in data science. First, you can check out the books for beginners that provide basic information about Data science in R and Python. There are also some books for experts that go deep into specific sub-categories in data science such as deep learning, data mining, etc. So without further ado, let’s see these books now!
Data Science Books for Beginners
Want to learn how to manipulate, process, clean, and crunch datasets in Python. This book is a great option for you! It has many different case studies that demonstrate how to solve a broad set of data analysis problems effectively. You will also learn basic and advanced features in NumPy, perform data analysis with tools in Pandas library, create visualizations with matplotlib, slice, dice, and summarize datasets using the groupby function in Pandas, and also use the IPython shell and Jupyter notebook for exploratory computing. Python for Data Analysis is a perfect introduction to the world of data analysis using the various data science tools available in Python.
This book is a perfect introduction to R for data science. It will teach you about RStudio, and the tidyverse, which is a collection of R packages that are caret to work with data science together. This book focuses on data wrangling, data exploration, and data modeling with R while also providing different R tools for solving data problems more quickly and easily. So pick up R for data science if you want a complete, big-picture understanding of the data science along with knowledge of all the basic tools you will need to implement this understanding. This book is divided into various sections and each section tackles different aspects of data science along with exercises that help you to further understand whatever you have studied in these sections.
Statistics is very important in data science, but very few data scientists actually have any statistics training. This book aims to rectify that by teaching statistics in a manner that is helpful for data science. Practical Statistics for Data Scientists is very useful in learning how exploratory data analysis is an important step in data science and random sampling can reduce bias even while using big data. This book also focuses on the statistical machine learning methods that can “learn” from the data and then apply that learning to future data sets. All in all, this book is a great option for learning statistics for data science, especially If you’re familiar with R programming language, and know some basic statistics.
This book is a great option for Machine Learning with Predictive Data Analytics, Machine Learning can be used to create predictive models by extracting patterns from large datasets. And this application of ML using Predictive Data Analytics is analyzed in detail in this book using both theoretical concepts and practical applications. This book describes the Predictive Data Analytics trajectory in detail: from data to insight to decision. It also describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning, each with a non-technical conceptual explanation followed by mathematical models and algorithms illustrated by detailed worked examples. Buy Fundamentals of Machine Learning for Predictive Data Analytics Book
Machine Learning and Data Science are both hot topics with multiple applications that are only restricted by your imagination! So Introduction to Machine Learning with Python is a guide for data scientists that tries to expand your imagination by teaching you methods to create your own machine learning solutions using Python and the scikit-learn library. This book starts with the fundamentals of Machine Learning and then moves on to the advantages and disadvantages of various machine learning algorithms. Then it deals with various advanced topics such as data processing, model evaluation, pipelines, etc. In short, you will essentially use Python to improve your Machine Learning and Data Science skills. Buy Introduction to Machine Learning with Python Book
Data Science Books for Experts
This is a classic handbook for data science in Python. It will introduce you to various tools in Python like IPython and Jupyter for computational environments, NumPy for manipulation of numbers, Pandas for efficient storage and manipulation of data, matplotlib for data visualizations, Scikit-Learn for efficient Python implementations of ML algorithms, etc. All these features of the Python Data Science Handbook make it ideal for manipulating, transforming, visualizing, and cleaning data with the help of all the Python tools mentioned. It is a comprehensive desk reference that can be used by both beginners to Data Science and also experienced professionals to keep their memories refreshed.
Deep Learning is a subset of Machine Learning. It is based on learning by example, just like humans do, using Artificial Neural Networks. These Artificial Neural Networks are created to mimic the neurons in the human brain so that Deep Learning algorithms can learn much more efficiently. This book offers a comprehensive and “deep” look into deep learning. It covers the mathematical and conceptual background including topics like linear algebra, numerical computation, probability theory and information theory, machine learning, etc. It also teaches the various deep learning techniques that are currently used in the tech industry including deep feedforward networks, convolutional networks, optimization algorithms, regularization, sequence modeling, etc. This book demonstrated why deep learning is so popular by delving into speech recognition, image recognition, natural language processing, computer vision, bioinformatics, online recommendation systems, etc.
This book, as the name suggests, is focused on mining massive datasets in machine learning. These massive datasets have become a part of the data science scene as there is a lot of data generated because of the internet and online commerce. Mining of Massive Datasets focuses on the algorithms that can be used to solve some of the problems in data mining. These algorithms include can even be applied to very large datasets. The book also mentions algorithms like the locality-sensitive hashing and stream-processing algorithms that can be used to mine data that is too fast to use conventional exhaustive processing. It also covers other aspects of data mining such as PageRank and other methods of organizing the Web, clustering, issues with finding frequent itemsets, etc.
In conclusion, all these books are a great option for learning data science in detail. Whether you are a beginner or an expert in this field, you can always learn more and also refresh your knowledge using these books. Some of these books are also focused on particular specializations in Data Science while others are introductory books for beginners. So check them out and select the one you think is right for you. Enjoy reading!