Most Recommended Data Science Books for Beginners and Professionals
In this article, I will provide the list of the Most Recommended Data Science Books for Students, Beginners, and Professional Software Developers. If you want to start your carrier in Data Science then writing code using Data Science might be confusing for a beginner. Books are the best friend of students as well as developers and the first mode of learning new languages, and technologies and nothing can beat books when it comes to educating. It is the reason most experienced Data Science recommend reading books for learning Data Science.
Combining the best Data Science books along with articles, tutorials, and videos, you will get an excellent path to learn Data Science. Some of the books just give an overview of various Data Science concepts, while some other jQuery books go into the depth of each Data Science concept.
There are hundreds and thousands of Data Science books available on Amazon or Internet or any other e-commerce site. And as a beginner, you might be confused to choose the right book to start learning Data Science. Here, we are giving you the list of Data Science Books based on the experience of Learners and Professionals. If you still haven’t put together your reading list for 2021, we’re here to help with our choice of the best-recommended books for Data Science.
To really learn data science, you should not only master the tools—data Science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from scratch shows you how these tools and algorithms work by implementing them from scratch.<Br> if you have an Aptitude for Mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. <Br> <beget a crash course in Python
- Learn the basics of linear algebra, statistics, and Probability how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as K-Nearest neighbors, naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommended systems, natural language processing, network analysis, produce, and databases
Buy This Book: https://amzn.to/3DyjN0O
2. Data Science Uncovering the Reality: IITians uncover how Data Science is transforming some of the world’s biggest companies
Data Science has become a popular field of work today. However, a good resource to understand applied Data Science is still missing. In Data Science Uncovering the Reality, a group of IITians unravels how Data Science is done in the industry. They have interviewed Data Science and technology leaders at top companies in India and presented their learnings here. This book will give you honest answers to questions such as: How to build a career in Data Science? How A.I. is used in the world’s most successful companies. How Data Science leaders actually work and the challenges they face
Buy This Book: https://amzn.to/3mKliSK
3. Data Science: The Ultimate Guide to Data Analytics, Data Mining, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, Big Data for Business and Machine Learning for Beginners
Do you want to expand your skills from being a basic Data Scientist to becoming an expert Data Scientist ready to solve real-world data-centric issues?
Exploring this book could be a step in the right direction. 2 comprehensive manuscripts in 1 book
Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data – That You Don’t
Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners
Part one of this book will cover topics such as:
- What Data Science is
- What it takes to become an expert in Data Science
- Best Data Mining techniques to apply in data
- Data visualization
- Logistic regression
- Data engineering
- Machine Learning
- Big Data Analytics
- And much more!
Part 2 of this book will discuss the following topics:
- How Big Data works and why it is so important
- How to do an explorative data analysis
- Working with data mining
- How to mine text to get the data
- Some amazing machine learning algorithms to help with data science
- How to do data modeling
- Data visualization
- How to use data science to help your business grow
- Tips to help you get started with data science
- And much, much more!
Buy This Book: https://amzn.to/3avXLzc
Python is a general-purpose programming language that is popular with data scientists. It is free, as are a number of open-source libraries that help acquire, organize, and process information. This book is designed for beginners in data analysis and covers the basics of Python data analysis programming and statistics. The book covers the Python fundamentals that are necessary for data analysis, including objects, functions, modules, and libraries. The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
Buy This Book: https://amzn.to/3mPHlYf
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you’ll learn how to use:
- IPython and Jupyter: provide computational environments for data scientists using Python
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms.
Buy This Book: https://amzn.to/3oQLnCh
Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it.
Buy This Book: https://amzn.to/2X3yuZZ
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book * Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data * Get beyond the theory and implement real-world projects in data science using R and Python * Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn * Learn and understand the installation procedure and environment required for R and Python on various platforms * Prepare data for analysis by implementing various data science concepts such as acquisition, cleaning, and munging through R and Python * Build a predictive model and an exploratory model * Analyze the results of your model and create reports on the acquired data * Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis-R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
Buy This Book: https://amzn.to/3oVp5za
Unlock your potential as an AI and ML professional! This book covers basic to advanced level topics required to master Machine Learning concepts. There are a lot of programs implemented that go with the explanation – that’s why we call it Learn and Practice. Book uses Scikit-learn (formerly scikits. learn and also known as sklearn) is the most popular package and also a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Happy Coding in Python
Buy This Book: https://amzn.to/3p2GSEy
9. Hands-On Data Science and Python Machine Learning: Perform data mining and machine learning efficiently using Python and Spark
This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book * Take your first steps in the world of data science by understanding the tools and techniques of data analysis * Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods * Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don’t need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn * Learn how to clean your data and ready it for analysis * Implement the popular clustering and regression methods in Python * Train efficient machine learning models using decision trees and random forests * Visualize the results of your analysis using Python’s Matplotlib library * Use Apache Spark’s MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning give you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empower you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.
Buy This Book: https://amzn.to/2YFpjQv
This book appeals to the reader’s desire to explore the world of data science in a manner that is not too technical and not too plain, but somewhere in between. This book targets this sweet spot and provides comprehensive yet brief explanations of concepts that might be otherwise misunderstood or easily ignored by the reader due to their inherent complexity.
This book covers the very key and fundamental concepts towards systematically understanding data science by drawing a well-defined road map addressing each topic in such a way that every section of every chapter reinforces the concepts and information laid out in the previous chapters. The main focus of this book is to give the reader insight into the processes involved in data science projects and shed light on some of the most common aspects of data science, including big data and how it impacts the world. This book attempts to build a solid foundation of the concepts pertaining to data science. It will prove to be the infrastructure that will lead you to one day become a data science expert. In short, this book has all the necessary information a beginner-level data scientist would have along with setting up for future improvement by reinforcing this knowledge with the intermediate and expert level books of the data science series.
Buy This Book: https://amzn.to/3oV4J9l
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Buy This Book: https://amzn.to/3BzuGPh
The book has been designed keeping in mind the needs of the beginners of this subject area while having no prior knowledge in this field. It is aimed to be used as a textbook for undergraduate and postgraduate students. However, it can also be used by research scholars and professionals. The text introduces the concepts of R programming language in a lucid way and enables the reader to use these to perform data science and machine learning applications for solving real-world problems. Every chapter in this book contains multiple programming exercises and examples that enhance the understanding of the subject.
- discussion on key topics such as ggplot2 package, sentiment analysis, web scraping, etc.
- Easy explanation of concepts along with programming applications
- wide variety of pedagogical elements such as br>Chapter highlights, review exercises, MCQs, programming exercises, etc.
- teaching aids such as ‘programming tips’ to help students remember important concepts and identify some typical errors
- additional appendices and case studies as online resources.
Buy This Book: https://amzn.to/3mPfNSy
Here, in this article, I provided the list of Most Recommended Data Science Books for Beginners and Professional and I hope this Most Recommended Data Science Books for Beginners and Professional article will help you with your needs and you enjoy this Most Recommended Data Science Books for Beginners and Professional article.
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.