Data Scientist is the sexiest job of the 21st century – As said by Harvard Business Reviews. It has become a buzzword that almost everyone is talking about these days.Nowadays, it is one of the top-ranking professions in any analytics organization. According to the glassdoor.com, “Data Scientists” profession ranks among the top ten best jobs in America and the United Kingdom. Moreover, our survey from glassdoor.com revealed that data scientist is a highly paid profession, which ranks third in America and sixth in the UK with the median-based salary of $107,801 and £ 45,188, respectively. However, being a Data Scientist isn’t a cakewalk. You need the appropriate resources for becoming a Data Scientist. The most conventional option is to learn through Data Science books.
Keep reading. It’s one of the most marvellous adventures anyone can have
– Lloyd Alexander.
But, choosing the right book for the same is where the real difficulty lies. Data Science aspirants are always in a dilemma about which book to choose. Here are some of the best data science books which will succor your Data Science journey.
- Practical Statistics for Data Scientists
- Introduction to Probability
- Data Science for Beginners: A Complete Overview on Python, Data Analysis and Machine Learning
- Python Data Science Handbook: Essential Tools for Working with Data
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Hands-On Machine Learning
- Deep Learning with Python
- R for Data Science
- Data Science and Big Data Analytics
- Data Science for Business
Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. This practical guide explains how to apply various statistical methods to data science, and gives you advice on what’s important and what’s not.
With this book, you’ll learn:
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- To use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that “learn” from data
- Unsupervised learning methods for extracting meaning from unlabeled data
This book covers all the topics that are needed for data science. It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.
Developed from celebrated Harvard statistics lectures, Introduction to Probability is the best book to learn about the probability. It provides essential language and tools for understanding statistics, randomness, and uncertainty. The authors present the material pretty neat and resemble real-life problems. If you have studied probability in school, this book is a must-have to further your knowledge of the basic concepts.
If you are going to learn probability for the first time – this book can help you build a strong foundation in the core concepts, though you will have to work for a little longer with the book.
Data Science for Beginners is the perfect place to start learning everything you need to succeed. A bundle of four units contains the methods, concepts, and important practical examples to help build foundation for excelling at the area. The data science book will also teach Python from scratch including the basic operations.
This book is aimed for programmers, software engineers, project managers and those who just want to keep up with technology. With these books in your hands, you will:
- Learn Python from scratch including the basic operations, how to install it, data structures and functions, and conditional loops
- Build upon the fundamentals with advanced techniques like Object-Oriented Programming (OOP), Inheritance, and Polymorphism
- Discover the importance of Data Science and how to use it in real-world situations
- Learn the 5 steps of Data Analysis so you can comprehend and analyze data sitting right in front of you
- Increase your income by learning a new, valuable skill that only a select handful of people take the time to learn
- Discover how companies can improve their business through practical examples and explanations
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 this data science book, 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
This data science book will get you kick-started on your ML journey with Python. The concepts are explained as if to a layman and with enough examples for a better understanding. ML is quite a complex topic, however, after practicing along with the book, you should be able to build your own ML models. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.
With this book, you’ll learn:
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills
Through the concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow, the author helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the Tensor Flow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
Learn techniques for training and scaling deep neural nets.
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
What you’ll learn?
- Deep learning from first principles
Setting up your own deep-learning environment
- Image-classification models
- Deep learning for text and sequences
- Neural style transfer, text generation, and image generation
Readers need intermediate Python skills. However, it is not required to have any previous experience with Keras, Tensor Flow, or machine learning.
This is another data science book for the beginners who want to learn data science using R. The book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Along with the basic concepts of statistics, you will be also introduced to the real life datasets.
Authors will guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You’ll learn how to:
- Wrangle—transform your datasets into a form convenient for analysis
- Program—learn powerful R tools for solving data problems with greater clarity and ease
- Explore—examine your data, generate hypotheses, and quickly test them
- Model—provide a low-dimensional summary that captures true “signals” in your dataset
- Communicate—learn R Markdown for integrating prose, code, and results
This data science book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.
This book will help you:
- Become a contributor on a data science team
- Deploy a structured lifecycle approach to data analytics problems
- Apply appropriate analytic techniques and tools to analyzing big data
- Learn how to tell a compelling story with data to drive business action
- Prepare for EMC Proven Professional Data Science Certification
This data science book will not only improve the communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates.
You’ll find hundreds of books related to data analytics and data science, but don’t get overwhelmed with the huge chunk of books. You don’t need to read them all. As we have made enough efforts to carefully select these most essential books, which could assist you to get in-depth knowledge of data science. Also, if you are newbie to this field, here is the layman’s book, with no Math Added, this will help you to go easy with the concepts.