McKinsey Global Institute report highlights that the U.S. economy will witness a shortage 250,000 data scientists by 2024 as the demand for data scientists continues to grow at a pace of 12% every year. The best indicator defining the demand and supply crunch for a data scientist job role is the increase in average salary of a data scientist by 16% from 2012 or 2014.There are multiple options to gain data science education- data science degrees, data science MOOC’s ,free data science tutorials and data science books. However, the data science educational option that is exciting to most of the people is the MOOC’s through sites like Coursera, DeZyre, Udacity and others. You might not get an official data science degree by taking these online MOOC’s but these online data science courses will provide you a lot of practical knowledge that is required to land a top gig as a data scientist. People enrolling for data science courses, often have this question on *“What are the best books for data science?” or “Which data science books they must read while pursuing an online data science course?”*

With the help DeZyre Industry Experts, we have put together a list of best data science books that every aspiring data scientist must read for doing data science.

As it is a well-known fact that Data Science is a multidisciplinary in nature and to become a great enterprise data scientist, one must have knowledge of statistics, mathematics, machine learning and hands-on experience working with popular data science programming languages like Python and R.

For people who do not hail from a Statistics background, this is the first data science book they should start reading. It goes right from the basic concepts of statistics with detailed introduction to regression and other statistical methods. The book contains several real world examples and every concept is explained through an example so that one can easily relate theory with the practical applications. The only drawback with this book is that it is not mathematically demanding i.e. there are no mathematical proof’s for equations.

CLICK HERE to get the 2016 data scientist salary report delivered to your inbox!

There is no better book than this to understand the introductory concepts of statistics. With practical explanations of all the available machine learning methods, when to use them, anyone who wants to analyse complex datasets must read this book. The book highlights practical issues encountered when building regression and classification models. For people from a less mathematical background, the formulas are explained in a simple and easy manner to help understand the concepts. This book provides excellent R programming examples to demonstrate mathematical and theoretical basis for machine learning and ensures that the understanding of the concepts has seeped in. The best thing about this book is the practices exercises which test your understanding of the concepts once you have read it.

This book requires basic knowledge of Python programming to learn the concepts of probability and statistics. The book is based on a Python libraries for probability distributions. Bayesian statistics is an important concept for data science and many books do not cover this but Think Stats emphasizes on Bayesian Statistics being too important for data science. The best thing about the book is that it follows a case study throughout with a dataset from National Institutes of Health to explain the entire data analysis process, encouraging the readers to work on projects and develop understanding of the probability and statistic concepts.

Referred to as the roadmap to data science, this book is more breadth than depth with some math and code. It has contributor’s chapters on Hadoop, Machine Learning, Finance modelling, Recommendation systems, Statistical inferences, and more, the book is worth a read for all aspiring data scientists. Doing Data Science does not act as a source of learning data science but is more like a historical account of the maturing data science industry. Readers will learn about the technologies that have built the data science arena which is very much required if you want to become a data scientist.

**question**

*“A unique and important addition to any data scientist’s library.”- *From the Foreword by Jim Porzak, Cofounder Bay Area R Users Group

This book lives up to its name by providing examples on using data science methods in real world based on open source software’s like RStudio, Squirrel, SQL, H2 DB ,etc. . This data science book makes use of examples from BI, decision support and marketing to help readers understand how to design A/B tests, build efficient predictive models and present insights to audience at different levels. Readers must have

- As they say that data scientists spend 90% of their time in data preparation, this book focusses on explaining these steps in detail.
- The book has some free codes and data to produce all the graphs and analysis present in the book.
- Focuses on helping readers understand how to collect and load non-trivial datasets.

This book explores various features of R programming language highlighting the reasons on why it was built for data science. Readers of the book will learn the entire data science workflow right from data mining to analysis and data visualization. This book highlights the best practices for cleaning data and visualizing it through graphs and plots. R for data science will help readers learn about literate programming, reproducible research and understand grammar of graphics which will help them save time when doing data science.

If you are an R junkie then this a must read data science book for you. This book is definitely not for R beginners but if you have already taken up an online data science course in R and have some knowledge about R programming then you will find this to be one of the best books for data science with R programming. You can refer to this book as the “The Big Big Data Book for R nerds”. The book focusses on the practicality aspects of machine learning instead of focussing on why. With high-level algorithm descriptions and worked examples in R, the best thing about the book is the boxes which summarize the usage information about the machine learning algorithms and other important data science techniques.

This recipe guide style data science book provides step by step process on tackling various data problems practically. The sections in the book are organized in the form of recipes with a problem, solution and discussion about the same. If Google lets you down when doing data science with R, then this book can be your great companion. You will find some discussions and introductions to statistical topics but do not expect it to contain detailed information on statistical analysis. For people looking for a data science book in R based on more visualizations this might not be a perfect reading partner.

**question**

There is no way to get away with math and equations when it comes to data science and this book makes it perfectly easy to follow the equations for people who do not have a strong mathematical background. Most of the examples in the book are explained in the Sci-Kit learn library but it does contain implementations of few machine learning algorithms that are not part of SciKit learn. This book is a complete bible for machine learning and contains everything needed for real-world problem solving like- dealing with missing data, evaluating models via cross-validations, transforming data into desired formats, extracting features and others.

Having learnt the basics of Python, reading this book will lift up your data science skills to the next level by helping you understand the data analysis capabilities provided by the Pandas library. Thought this book does not explain in detail on how to do data analysis but emphasizes on how to use the data analysis library Pandas. The book presents sophisticated execution of analysis from various data sources like Yahoo Finance, USA.gov, USDA and Federal Election Commission. The most interesting and engaging parts of this data science book are the examples of the Pandas code used to analyse US baby names and examples from the financial sector.

**question**

Disclaimer – This is not an exhaustive list of popular data science books and there are many more that data scientists can add to their reading list. Reading the list of ten of the best data science books mentioned in this article will set you on the path to gain further knowledge about a growing field like data science.

With increasing demand for data scientists and shortage in talent, this is the best time to retool your skillset with data science skills. Regardless of your skill level, put these best data science books on your reading list to find some guiding in principles on doing data science the best way.

*“Keep reading books, but remember that a book is only a book, and you should learn to think for yourself.”-Maxim Gorky*

If you know any other good free data science educational resources like free data science tutorials, blogs or free data science books, let us know in comments to help the data science community at large.

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

- Top 100 Hadoop Interview Questions and Answers 2017
- Pig Interview Questions and Answers
- Hive Interview Questions and Answers
- HBase Interview Questions and Answers
- MapReduce Interview Questions and Answers
- HDFS Interview Questions and Answers
- Real-Time Hadoop Interview Questions and Answers
- Hadoop Admin Interview Questions and Answers
- Basic Hadoop Interview Questions and Answers
- Apache Spark Interview Questions and Answers
- Data Analyst Interview Questions and Answers
- 100 Data Science Interview Questions and Answers (General)
- 100 Data Science in R Interview Questions and Answers
- 100 Data Science in Python Interview Questions and Answers
- Data Cleaning in Python
- Python Pandas Dataframe Tutorials
- Recap of Hadoop News for September 2018
- Introduction to TensorFlow for Deep Learning
- Recap of Hadoop News for August 2018
- AWS vs Azure-Who is the big winner in the cloud war?
- Top 5 Reasons to Learn AWS
- Top 50 AWS Interview Questions and Answers for 2018
- Recap of Hadoop News for July 2018
- Top 10 Machine Learning Projects for Beginners

- Hadoop Online Tutorial – Hadoop HDFS Commands Guide
- MapReduce Tutorial–Learn to implement Hadoop WordCount Example
- Hadoop Hive Tutorial-Usage of Hive Commands in HQL
- Hive Tutorial-Getting Started with Hive Installation on Ubuntu
- Learn Java for Hadoop Tutorial: Inheritance and Interfaces
- Learn Java for Hadoop Tutorial: Classes and Objects
- Learn Java for Hadoop Tutorial: Arrays
- Apache Spark Tutorial–Run your First Spark Program
- PySpark Tutorial-Learn to use Apache Spark with Python
- R Tutorial- Learn Data Visualization with R using GGVIS
- Neural Network Training Tutorial
- Python List Tutorial
- MatPlotLib Tutorial
- Decision Tree Tutorial
- Neural Network Tutorial
- Performance Metrics for Machine Learning Algorithms
- R Tutorial: Data.Table
- SciPy Tutorial
- Step-by-Step Apache Spark Installation Tutorial
- Introduction to Apache Spark Tutorial
- R Tutorial: Importing Data from Web
- R Tutorial: Importing Data from Relational Database
- R Tutorial: Importing Data from Excel
- Introduction to Machine Learning Tutorial
- Machine Learning Tutorial: Linear Regression
- Machine Learning Tutorial: Logistic Regression
- Support Vector Machine Tutorial (SVM)
- K-Means Clustering Tutorial
- dplyr Manipulation Verbs
- Introduction to dplyr package
- Importing Data from Flat Files in R
- Principal Component Analysis Tutorial
- Pandas Tutorial Part-3
- Pandas Tutorial Part-2
- Pandas Tutorial Part-1
- Tutorial- Hadoop Multinode Cluster Setup on Ubuntu
- Data Visualizations Tools in R
- R Statistical and Language tutorial
- Introduction to Data Science with R
- Apache Pig Tutorial: User Defined Function Example
- Apache Pig Tutorial Example: Web Log Server Analytics
- Impala Case Study: Web Traffic
- Impala Case Study: Flight Data Analysis
- Hadoop Impala Tutorial
- Apache Hive Tutorial: Tables
- Flume Hadoop Tutorial: Twitter Data Extraction
- Flume Hadoop Tutorial: Website Log Aggregation
- Hadoop Sqoop Tutorial: Example Data Export
- Hadoop Sqoop Tutorial: Example of Data Aggregation
- Apache Zookepeer Tutorial: Example of Watch Notification
- Apache Zookepeer Tutorial: Centralized Configuration Management
- Hadoop Zookeeper Tutorial
- Hadoop Sqoop Tutorial
- Hadoop PIG Tutorial
- Hadoop Oozie Tutorial
- Hadoop NoSQL Database Tutorial
- Hadoop Hive Tutorial
- Hadoop HDFS Tutorial
- Hadoop hBase Tutorial
- Hadoop Flume Tutorial
- Hadoop 2.0 YARN Tutorial
- Hadoop MapReduce Tutorial
- Big Data Hadoop Tutorial for Beginners- Hadoop Installation