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Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.
Are you interested in learning the Data Science and looking for some excellent book that will help you skyrocket your Data science expertise? Then you have come to the right place.
Here is a curated list of the best books to learn Data Science for beginners. These books are highly recommended by Data Science experts and are helpful for students to grasp the programming fundamentals. These resources will guide you to build your career in this promising field and make you a better Data Analyist. Read more…
Best Data Science Books for Beginners
|Book Title||Author Name||Latest Edition||Publisher||Ratings||Link|
|Data Science from Scratch||Joel Grus||2nd edition||O′Reilly||Learn More|
|Data Science For Dummies||Lillian Pierson||1st edition||John Wiley & Sons||Learn More|
|Designing Data-Intensive Applications||Martin Kleppmann||1st edition||O’Reilly Media||Learn More|
|Big Data||Viktor Mayer-Schönberger||Reprint edition||Harper Business||Learn More|
|Storytelling with Data||Cole Nussbaumer Knaflic||1st edition||Wiley||Learn More|
Data Science from Scratch is a book written by Joel Gurus. It is one of the best data science book that helps you to learn math and statistics that is at the core of data science. You will also learn hacking skills you need to get started as a data scientist.
The books include topics like implement k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, and clustering models. You will be also able to explore natural language processing, network analysis, etc.
Data Science For Dummies is a book written by Lillian Pierson. This book is ideal for IT professionals and students who want a quick primer covering all areas of the expansive data science space.
The book covers topics like big data, data science, and data engineering, and how all of these areas are combined to which offers great value. You will also learn about technologies, programming languages, and mathematical methods.
Designing Data-Intensive Applications is a book written by Martin Kleppmann. It is one of the best books for data science which helps learn the benefits and drawbacks of various technologies for processing and storing data. This book also helps software engineers and architects to learn about how to make full use of data in modern applications.
The book helps you to make informed decisions by identifying the strengths and weaknesses of different tools and navigate the trade-offs around consistency, scalability, fault tolerance, and complexity.
Big Data is a book written by Viktor Mayer-Schonberger and Kenneth Cukier. The book talks about the optimistic and practical look at the Big Data revolution. The authors of this book also talk about how Big data technology able to change our lives and what we can do to protect ourselves from its hazards.
Storytelling with data is a book written by Cole Nussbaumer Knaflic. In this book, you will learn the fundamentals of data visualization and how to communicate effectively with data. The lessons in this book are mostly in theory and offer many real-world examples ready for immediate application to your next graph or presentation.
This book also teaches the reader about how they can go beyond predictable tools to reach the root of your data. It also includes a topic of how to use your data to create an engaging and informative story.
Practical Statistics for Data Scientists is a book written by Peter Bruce (Author), Andrew Bruce. This book explains how to apply various statistical methods to data science, and gives you advice on what’s important and what’s not.
This book is an easy-to-use data science reference book if you’re familiar with the R programming and have some knowledge of statistics.
Data Science and Big Data Analytics is a book published by EMC education service. It is one of the best amazon data science books which covers the breadth of activities and methods and tools that data scientists use. The book focuses on concepts, principles, and practical applications.
It applies to any industry and technology environment, and the learning. It is supported and explained with examples that you can replicate using open-source software.
Data Science for business is a book written by known data science experts Foster Provost and Tom Fawcett. This Data science study book introduces the fundamental principles of data science. This study book for data science projects helps you understand many data-mining techniques in use today.
You’ll also learn how to improve communication between business stakeholders and data scientists. It also helps you understand the data-analytical process and how data science methods able to support business decision-making.
Head First Statistics is a book written by Dawn Griffiths. The writer brings this typically dry subject to life, teaching you everything you want and need to know about statistics through a material that is full of puzzles, stories, quizzes, and real-world examples.This book helps you to learn statistics so you can understand key points and use them. The book also covers how to present data visually with charts and plots. Lastly, the book also teaches how you can calculate probability and expectation, etc.
R for Data Science is a book written by Hadley Wickham. It is designed to get you doing data science as quickly as possible.
The book guides you through the steps of importing, exploring, and modeling your data and communicating the results.
In this book, you will get a complete, big-picture understanding of the data science cycle. Apart from the basic tools, you need to manage the details. Each section of this book is paired with exercises to help you practice what you’ve learned along the way.
Hands-On Machine Learning is a Data Science book written by Aurélien Géron. The book helps you learn the concepts and tools for building intelligent systems. You’ll learn also learn various techniques, like simple linear regression and progressing to deep neural networks. Each chapter of this book helps you apply what you’ve learned; all you need is programming experience.
Python for Data Analysis is a book written by Wes McKinney. This reference book is full of case studies showing how to solve many commonly faced data analysis problems. In this Python Data science book, you will learn the latest versions of pandas, NumPy, IPython, and Jupyter.
This reference book is a practical, modern introduction to data science tools in Python. It’s an ideal book for analysts new to Python and Python programmers.
Machine learning with Python is a book written by Andreas C. Müller (Author), Sarah Guido (Author). In this book, you will learn the steps necessary to create a successful machine-learning application with Python and the sci-kit-learn library.
In this book, you will learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. This study material also introduces you to NumPy and matplotlib libraries.
Practical Data Science with R is a book written by Nina Zumel (Author), John Mount (Author), and Jim Porzak. The book explains basic principles without lengthy theoretical details. You will provide the real use cases you’ll face as you collect, curate, and analyze the data.
You’ll able to apply the R programming language and statistical analysis techniques. The book carefully explained examples based on marketing, BI, and decision support system. This data science textbook also covers topic like how to design experiments which is build on predictive models.
Thinking with data is a book written by Max Sharon. It helps you learn techniques for turning data into knowledge you can use. In this book, you will discover a framework for defining your project. It also includes data you want to collect and how you intend to approach and analyze its results.
This Data Science book also helps you to explore data-specific patterns of reasoning and learn how to build more useful arguments.
The Data Science Handbook is written by Field Cady. It is an ideal reference book for data analysis methodology and big data software tools. The book is ideal for people who want to practice data science but lack the required skill sets.
This Data science book is also an ideal study material for researchers as well as entry-level graduate students. They require to learn real-world analytics and expand their skill set.
An Introduction to Statistical Learning is a book written by a group of authors like Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshira. This Data Science book presents useful modeling and prediction techniques, along with relevant applications.
It is one of the best books on data science which offers color graphics and real-world examples used to illustrate the methods presented. Each chapter of this book contains a tutorial on implementing the analyses and methods presented in the R language.
❓ What is Data Science?
Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.
⚡ Which are the best Data Science books?
Following are some of the best Data Science for Beginners and Advanced Data Scientist
- Data Science from Scratch: First Principles with Python
- Data Science For Dummies
- Designing Data-Intensive Applications
- Big Data: A Revolution That Will Transform How We Live, Work, and Think
- Storytelling with Data: A Data Visualization Guide for Business Professionals
✅ How can I learn Data Science?
Here are the steps that you can perform to start learning data science:
- Step 1) First, you need to have some interest in learning about data
- Step 2) Start from learning basic concepts of data science
- Step 3) Next, start learning Python
- Step 4) Learn data analysis, manipulation and visualization
- Step 5) Now, start to learn machine learning
- Step 6) Constantly practice all the aspects that you have learnt so far
- Step 7) You can also join physical classes, online classes or you can refer any good data science book from the above-given list