Best data science books

Data science has been a pretty fast growing concept over this decade. By the help of data science, you can turn raw data into insights and can make effective decisions. So, here are some books that will surely fit your need for learning data science.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Author: Foster Provost,Tom Fawcett
Published at: 19/08/2013
ISBN: 1449361323

In Data Science for Business, renowned data scientists Foster Provost and Tom Fawcett introduce the fundamental concepts of data science. And walk you through the data-analytic thinking necessary for extracting useful knowledge and business value from your collected data. This guide also makes you understand the different data-mining techniques are being used today.

What you'll learn:

  • How data science fits in your organization
  • Ways to use it for competitive advantage
  • Why data is treated  as a business asset that requires careful investment if you’re to gain real value
  • How to approach business problems data-analytically
  • Using the data-mining process to gather valuable data in the most appropriate way
  • Essential concepts for extracting knowledge from data 
  • How to Apply data science principles when interviewing data science job candidates

Data Science from Scratch: First Principles with Python
Author: Joel Grus
Published at: 30/04/2015
ISBN: 149190142X

Data science Experts use many tools such as libraries, frameworks, modules, and toolkits for doing data science. You can also use them to dive into the discipline without actually understanding data science. Data Science from Scratch teaches you how many of the most basic data science tools and algorithms operate by implementing them from scratch.

What you'll learn:

  • A basic course in Python
  • Fundamentals of linear algebra, statistics, and probability-- and how and when they're used in data science
  • How to explore, collect, clean, and manipulate data to extract meaningful value
  • Essential basics of machine learning
  • How to Implement these models-- k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Recommender systems and natural language processing technologies
  • Network analysis, MapReduce, and databases

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Author: Hadley Wickham,Garrett Grolemund
Published at: 05/01/2017
ISBN: 1491910399

R is a very effective language to turn raw data into insight, knowledge, and understanding. R for Data Science introduces you to the collection of R packages including R, RStudio, and the tiny verse designed to work together to make data science fast, fluent, and fun. No previous programming experience is required, this book is devised to get you doing data science as quickly as possible.

What You’ll learn:

  • How to transform your datasets into a form convenient for analysis
  • Implementing powerful R tools for solving data problems with greater clarity and ease
  • analyze your data, generate hypotheses, and quickly test them
  • How to implement a low-dimensional summary that captures true "signals" in your dataset
  • R Markdown for integrating prose, code, and results

Naked Statistics: Stripping the Dread from the Data
Author: Charles Wheelan
Published at: 13/01/2014
ISBN: 039334777X

For those who think Statistics is something boring and useless, this book is a surprise. With the wit, accessibility, and absolute fun, Charles Wheelan challenges the odds yet again by bringing another crucial, formerly unglamorous discipline to life. Wheelan removes the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. 

What you'll learn:

  • Key concepts such as inference, correlation, and regression analysis
  • How biased or careless parties can manipulate or misrepresent data
  • The way how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions
  • Catching schools that cheat on standardized tests
  • How does Netflix know which movies you’ll like?
  • What is causing the rising incidence of autism?  


Data Smart: Using Data Science to Transform Information into Insight
Author: John W. Foreman
Published at: 04/11/2013
ISBN: 111866146X

How can you exactly do data science for your business? Do you need to hire one of the experts of this dark art, the "data scientist," to extract the gold from your data? Absolutely not.

Data science is little more than using simple steps to process raw data into actionable insight. And in Data Smart, you will learn how that's done within the familiar environment of a spreadsheet.

What you'll learn:

  • Everything you ever need to know about spreadsheets 
  • Using K models to segment your customer base
  • How to optimize modeling
  • Network graphs and community detection
  • How to ensemble models
  • Outlier detection
  • Moving from Spreadsheets into R

What Is Data Science?
Author: Mike Loukides
Published at: 10/04/2011

Statistics will be the next sexy job--Hal Varian, Googles' chief economists

  • We've all heard it but what does this statement mean?
  • Why do we suddenly care about statistics and about data?

This report explores the many sides of data science -- the technologies, the companies, and the unique skill sets everything you need to understand the hot topic of this decade.

Storytelling with Data: A Data Visualization Guide for Business Professionals
Author: Cole Nussbaumer Knaflic
Published at: 02/11/2015
ISBN: 1119002257
Storytelling with Data introduces you to the basic principles of data visualization and how to communicate effectively with data. The text helps you to explore the power of storytelling and how to make data a pivotal point in your story. Though the lessons in this book are grounded in theory, numerous real-world examples make it accessible for the readers.

What you'll learn: 
  • What is the importance of context and audience is
  • Determining the type of graph that fits best for your situation
  • How to identify and reduce the clutter clouding your information
  • Most effective ways to direct your audience's attention to the most important parts of your data
  • Acting as a designer and utilizing concepts of design in data visualization
  • How the power of storytelling makes your message resonate with your audience 

Data Science For Dummies
Author: Lillian Pierson
Published at: 09/03/2015
ISBN: 1118841557

Numerous jobs in data science out there, but few people have skills needed to fill these increasingly important roles. Data Science For Dummies is the ideal starting point for students and IT professionals who want a quick guideline covering all areas of the widespread data science space. Focusing on business cases, the book teaches you topics in big data, data science, data engineering and how you can combine these three areas to generate tremendous value.

What you'll learn:

  • Fundamentals of big data and data engineering
  • Implementation of big data frameworks and applications like Spark, Hadoop, MPP platforms, MapReduce, and NoSQL.
  • Machine learning and many of its algorithms
  • Artificial intelligence and the evolution of the Internet of Things.
  • Detailed data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Author: Aurélien Géron
Published at: 09/04/2017
ISBN: 1491962291

Due to a bunch of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who have a little knowledge about this technology can use simple, effective tools to write programs capable of learning from data. This practical book shows you how to do these miracles.

What you'll learn:

  • Understanding the machine learning landscape, especially neural nets
  • Using scikit-learning, an example machine-learning project end-to-end
  • Various training models, including  decision trees, random forests, support vector machines, and ensemble methods
  • How to use the TensorFlow library to develop and train neural nets
  • Different neural net architectures, including  recurrent nets, convolutional nets, and deep reinforcement learning
  • Effective techniques for training and scaling deep neural nets
  • How to write practical code examples without acquiring excessive theory or algorithm details on machine learning

Thanks for reading this post. If you have any opinion don't hesitate to comment here. Also please subscribe our newsletter to get more updates.