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Regardless of the fact that Data Science is one of the greatest and most recognized industries today, it's also worth noting that it will remain innovative and demanding for another decade or more. There will be plenty of data science employment opportunities available that will pay well and provide prospects for advancement.
To that end, there's no better way to get started than by reading data science books.
Data science is not just about computing; it also encompasses mathematics, probability, statistics, programming, machine learning, and much more. Learning data science through books can help you gain a comprehensive picture of data science.
Today, Analytics Insight presents you with the top 10 books to learn statistics in data science.
The theory behind most of the major machine learning algorithms employed by data scientists today is covered in this book. It also covers both Bayesian and Frequentist statistical inference approaches in detail. The book is best suited to individuals who have already learned the fundamentals of data analysis statistics and are acquainted with certain statistical notation.
The tone of this book, like that of other Headfirst books, is warm and conversational, making it the finest book for data science beginners. The book covers a wide range of statistics, beginning with descriptive statistics such as mean, median, mode, and standard deviation before moving on to probability and inferential statistics such as correlation and regression. You'll find some interesting actual examples to keep you interested in the book. Altogether, this is a fantastic book to start your data science adventure with.
Rather than focusing on data scientists or programmers, this book provides a wide range of statistical techniques. It is, nevertheless, written in a very straightforward manner, and it covers a large range and depth of statistical topics in a way that is very easy to comprehend. It's a great book to read if you're new to data science and don't have a math degree.
This book highlights the elegance of statistics and brings them to life. The tone is casual and amusing. This is not a book that will tire you. With real-life examples, the author illustrates all basic and advanced statistical concepts. While the book does a fantastic job of explaining the basics, several of these courses will benefit from some prior knowledge of statistics so that you can dive right into the book.
Bayesian approaches can be complex and difficult to comprehend. This book is geared toward programmers. There are coding examples throughout the book, and the Github repository that houses the chapters has a significant number of notes. As a result, it's a fantastic hands-on introduction to the subject.
This is a book that practicing data scientists should read. The main emphasis is on bridging the gap between statistics and machine learning. As a result, you'll become familiar with all of the most prominent supervised and unsupervised machine learning algorithms. Because the practical features of algorithms have been illustrated using R, R users will have an edge. In addition to theory, this book emphasizes the use of machine learning algorithms in real-world scenarios.
It's yet another book that focuses solely on data science principles and includes several code examples, this time written in Python. It is primarily geared for programmers, and it relies on them to grasp the essential statistical principles presented. As a result, this book is best suited to people who already have a basic understanding of Python.
To begin with statistics, this book provides excellent content that delves deeply into its subjects. In addition, the statistical notion is explained using R, which makes it even more useful. It provides a step-by-step explanation with intriguing practice examples to go along with it.
Statistics is a wide field, and only a small portion of it applies to data science. This book does an excellent job of focusing on data science-related topics. So, if you're seeking a book that can swiftly provide you with just enough knowledge to be able to apply data science, this is the book for you.
The author gets right in and demonstrates how to use raw data to solve real-world problems, emphasizing on mathematical ideas and connections. This book is a fantastic supplement to your data science journey since it teaches how to think like statisticians and utilize data to solve real-world problems.
