Table Of Contents

- Some of the Free And Best Data Science Books For Beginners, Intermediate and Advanced Enthusiast (Our Favorite from the List)
- 100+ Free Statistics, Data Mining, Python, Mathematics, Data Visualization, SQL & Data Analytics Books Are As Follows
- Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code
- An Introduction to Statistical Learning, 2nd Edition
- Data Science at the Command Line, 2nd Edition
- R Graphics Cookbook: Practical Recipes for Visualizing Data, 2nd Edition
- GGPlot2: Elegant Graphics for Data Analysis, 2nd Edition
- R Cookbook: Proven Recipes for Data Analysis, Statistics and Graphics, 2nd Edition
- Think Bayes, Second Edition
- Building Secure and Reliable Systems – Best Practices for Designing, Implementing, and Maintaining Systems
- Mastering Shiny
- Probability, Statistics, and Data: A Fresh Approach Using R
- A Beginner’s Guide to Clean Data: Practical advice to spot and avoid data quality problems
- Data Science Desktop Survival Guide
- Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edition
- Data Science in Julia for Hackers
- Principles and Techniques of Data Science
- Introduction to Probability for Data Science
- Fundamentals of Data Visualization
- The Data Science Handbook
- Python Data Science Handbook
- Introduction to Probability
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Computer Age Statistical Inference
- Data-Intensive Text Processing with MapReduce
- Statistical Inference Via Data Science: A ModernDive Into R and the Tidyverse
- Happy Git and GitHub for the useR
- Agile Data Science with R: A workflow
- Spatial Modelling for Data Scientists
- Geocomputation with R
- Spatial Data Science: With applications in R
- Efficient R Programming: A Practical Guide to Smarter Programming
- Data Science In A Box
- Introduction to Modern Statistics
- The Elements of Statistical Learning: Data Mining, Inference, etc
- Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling
- Supervised Machine Learning for Text Analysis in R
- Interactive web-based data visualization with R, plotly, and shiny
- Best Coding Practices for R
- The Hitchhiker’s Guide to Python
- Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition
- Text Mining with R: A Tidy Approach
- Model-Based Clustering and Classification for Data Science
- Statistics in Plain English, Third Edition
- Exploring, Visualizing, and Modeling Big Data with R
- Modern Data Science with R, 2nd edition
- Mastering Spark with R
- Think Stats: Exploratory Data Analysis in Python
- Foundations of Data Science
- Data Mining and Analysis: Fundamental Concepts and Algorithms
- Mastering Software Development in R
- Genetic Algorithms in Search, Optimization, and Machine Learning
- Social Media Mining: An Introduction
- Advanced R
- Open Data Structures – An Introduction
- Think Python: How to Think Like a Computer Scientist
- R for Excel Users
- 21 Recipes for Mining Twitter Data with rtweet
- Automate the Boring Stuff with Python: Practical Programming for Total Beginners
- Introduction to Information Retrival
- D3 Tips and Tricks
- Statistical Learning with Sparsity: The Lasso and Generalizations
- Data Visualization: A Practical Introduction
- Modeling with Data: Tools and Techniques for Scientific Computing
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
- Data Mining: Practical Machine Learning Tools and Techniques, Third Edition
- Advanced Statistics From an Elementary Point of View
- Introduction to Data Science: Data Analysis and Prediction Algorithms with R
- A Programmer’s Guide to Data Mining
- The Data Science Design Manual
- Oracle Database Notes for Professionals
- The Tidyverse Cookbook
- SQL Notes for Professionals
- Ethics and Data Science
- MySQL Notes For Professionals
- PostgreSQL Notes for Professionals
- Linear Regression Using R: An Introduction to Data Modeling
- Statistical Inference for Data Science
- The Element of Data Analytic Style
- Causal Inference: What if
- Data Science: Theories, Models, Algorithms, and Analytics
- Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
- An Introduction to Data Science
- Data Jujitsu: The Art of Turning Data into Product
- The Art of Data Science
- Data Driven: Creating a Data Culture
- R Programming for Data Science
- Executive Data Science – A Guide to Training and Managing the Best Data Scientists
- Exploratory Data Analysis with R
- OpenIntro Statistics, 4th Edition
- Theory and Applications for Advanced Text Mining
- Data Science: An Introduction WikiBook
- Disruptive Possibilities: How Big Data Changes Everything
- Introduction to R – Notes on R, A Programming Environment for Data Analysis and Graphics
- Fundamental Numerical Methods and Data Analysis
- Introduction to Social Network Methods
- Analyzing Linguistic Data: A Practical Introduction to Statistics
- Introduction to Statistical Thought
- Applied Data Science
- Data Mining and Knowledge Discovery in Real Life Applications
- The SysAdmin Handbook
- Knowledge-Oriented Applications in Data Mining
- R and Data Mining: Examples and Case Studies
- Conversations On Data Science
- Advanced Linear Models for Data Science
- Big Data, Data Mining, and Machine Learning
- Inductive Logic Programming: Techniques and Applications
- The Field Guide of Data Science
- Modern Data Science for Modern Biology
- Crash Course on Basic Statistics
- Hands-on Machine Learning and Big Data
- Mathematics of Data Science
- Scipy Lecture Notes
- Statistics With Julia
- A Genetic Algorithm Tutorial
- Exploring Data Science with Python
- Understanding Databases
- Exploring Streaming Data Analysis
- Exploring Data Science
- Exploring the Data Jungle
- Exploring Math for Programmers and Data Scientists
- Advances in Evolutionary Algorithms
- Genetic Programming: New Approaches and Successful Applications
- Global Optimization Algorithms: Theory and Application
- Algorithms Notes for Professionals
- Regression Models for Data Science in R
- Think Data Structures
- Data Visualization in Society
- SQL Server Backup and Restore
- Making Sense of Stream Processing: Behind Apache Kafka
- Machine Learning for Data Streams: Practical Examples in MOA
- Just Enough R: Learn Data Analysis with R in a Day
- Data Blending For Dummies
- Data Mining Applications in Engineering and Medicine
- Understanding Big Data: Analytics for Hadoop and Streaming Data
- Applied Spatial Data Analysis with R

Have you checked **100+ Free Machine Learning and Artificial Intelligence Books**? If you haven’t yet, make sure you spend 2 minutes to check that collection. In this post, You’ll see 100+ free data science books for beginners, intermediate and experts. The eBooks are available in pdf or html format.

Note: All the books listed below are open sourced and are in a mixed order. And One more thing i.e. if you think any free data science book is not included in the below given list, Please share it with us on any of our social media account (@TheInsaneApp).

List is very big. So, We recommend you to check the Table Of Content first and go through all the book titles.

## Some of the Free And Best Data Science Books For Beginners, Intermediate and Advanced Enthusiast (Our Favorite from the List)

- Data Science at the Command Line, 2nd Edition
- R Graphics Cookbook, 2nd Edition
- Think Bayes, Second Edition
- An Introduction to Statistical Learning, Second Edition
- R Cookbook, 2nd Edition
- Building Secure and Reliable Systems by Google
- Python Data Science Handbook
- Statistics in Plain English, Third Edition
- Probability, Statistics, and Data: A Fresh Approach Using R
- Fundamentals of Data Visualization

## 100+ Free Statistics, Data Mining, Python, Mathematics, Data Visualization, SQL & Data Analytics Books Are As Follows

### Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code

Authors: Jack Dougherty And Ilya Ilyankou

About Hands-On Data Visualization PDF:

Hands-On Data Visualization takes you step-by-step through tutorials, real-world examples, and online resources. This book is ideal for students, educators, community activists, non-profit organizations, small business owners, local governments, journalists, researchers, or anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required.

### An Introduction to Statistical Learning, 2nd Edition

Authors: Gareth James, Daniela Witten, Trevor Hastie & Rob Tibshirani

About An Introduction to Statistical Learning, 2nd Edition PDF:

An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. The second edition explores topics like Deep learning, Survival analysis, Multiple testing, Naive Bayes and generalized linear models, Bayesian additive regression trees and Matrix completion in detail.

### Data Science at the Command Line, 2nd Edition

Author: Jeroen Janssens

About Data Science at the Command Line, 2nd Edition PDF:

This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

### R Graphics Cookbook: Practical Recipes for Visualizing Data, 2nd Edition

Author: Winston Chang

About R Graphics Cookbook, 2nd Edition PDF:

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

### GGPlot2: Elegant Graphics for Data Analysis, 2nd Edition

Author: Hadley Wickham

About ggplot2 book pdf:

This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you’ll learn everything you need in the book. After reading this book you’ll be able to produce graphics customized precisely for your problems, and you’ll find it easy to get graphics out of your head and on to the screen or page. This is considered as one of the best free data analytics and data science books for beginners, You can download and learn more about this pdf from the below given link.

### R Cookbook: Proven Recipes for Data Analysis, Statistics and Graphics, 2nd Edition

Author: James Long and Paul Teeter

About R Cookbook, 2nd Edition PDF:

This book is full of how-to recipes, each of which solves a specific problem. The recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. We know these recipes are useful and we know they work, because we use them ourselves. If you are a beginner, then this book will get you started faster. If you are an intermediate user, this book is useful for expanding your horizons and jogging your memory.

### Think Bayes, Second Edition

Author: Allen B. Downey

About Think Bayes 2e PDF:

With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.

### Building Secure and Reliable Systems – Best Practices for Designing, Implementing, and Maintaining Systems

Author: Heather Adkins, Ana Oprea, Paul Blankinship, Piotr Lewandowski, Adam Stubblefield, Betsy Beyer

About Building Secure and Reliable Systems PDF:

In this book, experts from Google share best practices to help your organization design scalable and reliable systems that are fundamentally secure. In this latest guide, the authors offer insights into system design, implementation, and maintenance from practitioners who specialize in security and reliability. They also discuss how building and adopting their recommended best practices requires a culture that’s supportive of such change.

### Mastering Shiny

Author: by Hadley Wickham

About Mastering Shiny PDF:

This book is designed to take you from knowing nothing about Shiny to being an expert developer who can write large complex apps that are still maintainable and performant. You’ll gain a deep understanding of the reactive programming model that underlies Shiny, as well as building a tool box of useful techniques to solve common app challenges.

### Probability, Statistics, and Data: A Fresh Approach Using R

Authors: Darrin Speegle and Bryan Clair

About Probability, Statistics, and Data: A Fresh Approach Using R PDF:

This book represents a fundamental rethinking of a calculus based first course in probability and statistics. This book is an excellent choice for students studying data science, statistics, engineering, computer science, mathematics, science, business, or for any student wanting a practical course grounded in simulations.

### A Beginner’s Guide to Clean Data: Practical advice to spot and avoid data quality problems

Author: Benjamin Greve

About A Beginner’s Guide to Clean Data PDF:

This book will help you to become a better data scientist by showing you the things that can go wrong when working with data – particularly low-quality data. A key difference between a junior and a senior data scientist is the awareness of potential pitfalls. After reading this book, you will be able to spot data quality problems and deal with them before they can break your work, saving yourself a lot of time.

### Data Science Desktop Survival Guide

Author: Graham Williams

About Data Science Desktop Survival Guide PDF:

The aim of this book is to gently guide the novice along the pathway to Data Science, from data processing through Machine Learning and to AI. This book provides a guide to the many different regions of the R platform, with a focus on doing what is required of the Data Scientist. It is comprehensive, beginning with basic support for the novice Data Scientist, moving into recipes for the variety of analyses we may find ourselves needing.

### Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edition

Authors: Ani Adhikari, John DeNero, and David Wagner

About Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edition PDF:

This eBook was originally developed for the UC Berkeley course Data 8: Foundations of Data Science. In this book, You’ll learn about introduction to data science, programming in python, classifications, predictions, data types, visualization, and more.

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### Data Science in Julia for Hackers

Authors: Federico Carrone, Herman Obst Demaestri and Mariano Nicolini

About Data Science in Julia for Hackers PDF:

It is in this sense that this book is meant for hackers: it will lead you down a road with a results-driven perspective, slowly growing intuition about the inner workings of many problems involving data and what they all have in common, with an emphasis on application. Familiarity with a high-level language like Python, taking derivatives and some function analysis should be enough for the reader to follow through with the book.

### Principles and Techniques of Data Science

Authors: Sam Lau, Joey Gonzalez, and Deb Nolan

About Principles and Techniques of Data Science PDF:

This book covers topics from multiple disciplines. Unfortunately, some of these disciplines use the same notation to describe different concepts. For clarity, we have devised notation that may differ slightly from the notation used in your discipline. In this book, we assume the reader is familiar with Tabular data manipulation: selection, filtering, grouping, joining, Basic probability concepts, Sampling, empirical distributions of statistics and more.

### Introduction to Probability for Data Science

Author: Stanley Chan

About Introduction to Probability for Data Science PDF:

This is one of the best introductory books on probability that we have seen. It is rigorous, yet intuitive. It is full of beautiful illustrations and easy-to-understand code samples (in Python and Matlab). Before introducing each new theoretical concept, the author gives reasons for why the material is important in practice, thus providing motivation for learning it. The title focuses on “Data Science” but in fact this book could be used to provide a thorough introduction to probability for any STEM student.

### Fundamentals of Data Visualization

Author: Claus O. Wilke

About Fundamentals of Data Visualization PDF:

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value. Understand the importance of redundant coding to ensure you provide key information in multiple ways. Use the book’s visualizations directory, a graphical guide to commonly used types of data visualization.

### The Data Science Handbook

Authors: William Chen, Henry Wang, Carl Shan, Max Song

About The Data Science Handbook PDF:

- The Data Science Handbook contains candid interviews with 25 of the world’s best data scientists.
- This book contains insight and interviews with data scientists from established companies such as Facebook, LinkedIn, Pandora, Intuit, and The New York Times.
- We also spoke with data scientists at fast-growing startups such as Uber, Airbnb, Mattermark, Quora, Square and Khan Academy

Note: We have set a minimum contribution of FREE and a suggested contribution of $19 to cover the time and investment the four of us put into this book. You can download pdf for free or pay if you want to contribute.

### Python Data Science Handbook

Author: Jake VanderPlas

About Python Data Science Handbook PDF:

With this handbook, you’ll learn how to use:

- Python 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
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

### Introduction to Probability

Author: Charles M. Grinstead, J. Laurie Snell

About Introduction to Probability PDF:

This text is designed for an introductory probability course taken by sophomores, juniors, and seniors in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a form understanding of the subject. This is considered as one of the best free mathematics for data science books from this list. You can download and learn more about this pdf from the below given link.

### R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Author: Garrett Grolemund and Hadley Wickham

About R for Data Science PDF:

Learn how to use R to turn raw data into insight, knowledge, and understanding. This 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. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. This is considered as one of the best free data science books from this list.

### Computer Age Statistical Inference

Authors: Bradley Efron and Trevor Hastie

About Computer Age Statistical Inference PDF:

This book covers the theory behind most of the popular machine learning algorithms used by data scientists today. It also gives a thorough introduction to both Bayesian and Frequentist statistical inference methodologies.

### Data-Intensive Text Processing with MapReduce

Author: Chris Dyer and Jimmy Lin

About Data-Intensive Text Processing with MapReduce PDF:

This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader “think in MapReduce”, but also discusses limitations of the programming model as well.

### Statistical Inference Via Data Science: A ModernDive Into R and the Tidyverse

Authors: Albert Young-Sun Kim and Chester Ismay

About Statistical Inference via Data Science PDF:

This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding experience. This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would.

### Happy Git and GitHub for the useR

Author: Jim Hester

About Happy Git and GitHub for the useR PDF:

The target reader is someone who uses R for data analysis or who works on R packages, although some of the content may be useful to those working in adjacent areas.

### Agile Data Science with R: A workflow

Author: Edwin Thoen

About Agile Data Science with R: A workflow PDF:

The title of this text has four components: Agile, Data Science, R, and Workflow. If you are interested in all four, you’re obviously in the right place. This text is not for you if you hope to learn about different algorithms and statistical techniques to do data science; more knowledgeable people have written many books and articles on those topics. Also it will not teach you anything about R programming. If you use python rather than R, you will still find this text valuable, especially the first part, which focuses on workflow only and is tool agnostic.

### Spatial Modelling for Data Scientists

Authors: Francisco Rowe and Dani Arribas-Bel

About Spatial Modelling for Data Scientists PDF:

In this eBook, You will learn how to analyse and model different types of spatial data as well as gaining an understanding of the various challenges arising from manipulating such data.

### Geocomputation with R

Authors: Jakub Nowosad, Jannes Muenchow, and Robin Lee Lovelace

About Geocomputation with R PDF:

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications.

### Spatial Data Science: With applications in R

Authors: Edzer Pebesma, Roger Bivand

About Spatial Data Science: With applications in R PDF:

This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis. The book aims at data scientists who want to get a grip on using spatial data in their analysis. To exemplify how to do things, it uses R.

### Efficient R Programming: A Practical Guide to Smarter Programming

Authors: Colin Gillespie and Robin Lovelace

About Efficient R programming PDF:

This hands-on book teaches novices and experienced R users how to write efficient R code. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf.

### Data Science In A Box

Author: Mine Çetinkaya-Rundel

About Data Science In A Box PDF:

This book focuses on how to efficiently teach data science to students with little to no background in computing and statistical thinking. The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results.

### Introduction to Modern Statistics

Authors: Mine Çetinkaya-Rundel and Johanna Hardin

About Introduction to Modern Statistics PDF:

The eBook is divided into six parts

- Introduction to data
- Exploratory data analysis
- Regression modeling
- Foundations for inference
- Statistical inference
- Inferential modeling

Each part contains multiple chapters and ends with a case study. Building on the content covered in the part, the case study uses the tools and techniques to present a high-level overview.

### The Elements of Statistical Learning: Data Mining, Inference, etc

Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman

About The Elements of Statistical Learning PDF:

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. This is considered as one of the best free data science books from this list.

### Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling

Author: Måns Thulin

About Modern Statistics with R PDF:

The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you Data wrangling, Exploratory Data Analysis, Statistical inference, Predictive modelling, Ethics in statistics and R Programming.

### Supervised Machine Learning for Text Analysis in R

Authors: Emil Hvitfeldt and Julia Sigle

About Supervised Machine Learning for Text Analysis in R PDF:

The book is divided into three sections. We make a (perhaps arbitrary) distinction between machine learning methods and deep learning methods by defining deep learning as any kind of multilayer neural network (LSTM, bi-LSTM, CNN) and machine learning as anything else (regularized regression, naive Bayes, SVM, random forest). This book is designed to provide practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate text into their modeling pipelines.

### Interactive web-based data visualization with R, plotly, and shiny

Author: Carson Sievert

About Interactive web-based data visualization with R, plotly, and shiny PDF:

In this book, you’ll gain insight and practical skills for creating interactive and dynamic web graphics for data analysis from R. It makes heavy use of plotly for rendering graphics, but you’ll also learn about other R packages that augment a data science workflow, such as the tidyverse and shiny.

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### Best Coding Practices for R

Author: Vikram Singh Rawat

About Best Coding Practices for R PDF:

Most of the books about R programming language will tell you what are the possible ways to do one thing in R. This book will only tell you one way to do that thing correctly.

### The Hitchhiker’s Guide to Python

Authors: Kenneth Reitz & Tanya Schlusser

About The Hitchhiker’s Guide to Python PDF:

This is an excellent book for all Python developers, both for beginners and more experienced users. It isn’t specific to Data Science. However, it will give you a fantastic grounding in the language and in particular includes recommended best practices and frameworks. It includes everything from installation, development environments, recommended code structure, object-oriented programming and some really excellent chapters on code style.

### Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition

Author: Solomon Kurz

About Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition eBook:

This ebook is based on the second edition of Richard McElreath’s (2020a) text, Statistical rethinking: A Bayesian course with examples in R and Stan. This project is not meant to stand alone. It’s a supplement to the second edition of McElreath’s text. Learn more about this eBook from the given link.

### Text Mining with R: A Tidy Approach

Author: David Robinson and Julia Silge

About Text Mining with R: A Tidy Approach PDF:

With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. This is considered as one of the best free R for data science books from this list.

### Model-Based Clustering and Classification for Data Science

Authors: Charles, Gilles, Brendan and others

About Model-Based Clustering and Classification for Data Science PDF:

This text book focuses on the recent developments in model-based clustering and classification while providing a comprehensive introduction to the field. It is aimed at advanced undergraduates, graduates or first year PhD students in data science, as well as researchers and practitioners.

### Statistics in Plain English, Third Edition

Author: Timothy C. Urdan

About Statistics in Plain English, Third Edition PDF:

The book was originally written for students studying a non-mathematics based course where an understanding of statistics is required, such as the social sciences. It, therefore, covers enough theory to understand the techniques but doesn’t assume an existing mathematical background. It is, therefore, an ideal book to read if you are coming into data science without a math-based degree.

### Exploring, Visualizing, and Modeling Big Data with R

Authors: Okan Bulut And Christopher Desjardins

About Exploring, Visualizing, and Modeling Big Data with R PDF:

Working with BIG DATA requires a particular suite of data analytics tools and advanced techniques, such as machine learning (ML). Many of these tools are readily and freely available in R. This eBook will provide students with a hands-on training on how to use data analytics tools and machine learning methods available in R to explore, visualize, and model big data.

### Modern Data Science with R, 2nd edition

Authors: Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton

About Modern Data Science with R, 2nd edition PDF:

This book is intended for readers who want to develop the appropriate skills to tackle complex data science projects and “think with data” (as coined by Diane Lambert of Google). The desire to solve problems using data is at the heart of our approach. This book was originally conceived to support a one-semester, 13-week undergraduate course in data science.

### Mastering Spark with R

Authors: Javier Luraschi, Kevin Kuo, Edgar Ruiz

About Mastering Spark with R PDF:

In this book you will learn how to use Apache Spark with R. The book intends to take someone unfamiliar with Spark or R and help you become proficient by teaching you a set of tools, skills and practices applicable to large-scale data science.

### Think Stats: Exploratory Data Analysis in Python

Author: Allen B. Downey

About Think Stats: Exploratory Data Analysis in Python PDF:

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.

New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.

### Foundations of Data Science

Author: Avrim Blum, John Hopcroft, and Ravindran Kannan

About Foundations of Data Science PDF:

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, and more. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

### Data Mining and Analysis: Fundamental Concepts and Algorithms

Author: Mohammed J. Zaki

About Data Mining and Analysis: Fundamental Concepts and Algorithms PDF:

This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. This is considered as one of the best free data mining and data science books for intermediate and experts from this list.

### Mastering Software Development in R

Author: Roger D. Peng, Sean Kross, and Brooke Anderson

About Mastering Software Development in R PDF:

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.

### Genetic Algorithms in Search, Optimization, and Machine Learning

Author: David E. Goldberg

About Genetic algorithms in search, optimization, and machine learning pdf:

A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning.

### Social Media Mining: An Introduction

Author: Novel by Huan Liu, Mohammad Ali Abbasi, and Reza Zafarani

About Social Media Mining: An Introduction PDF:

Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. This is considered as one of the best free data science books from this list.

### Advanced R

Author: Hadley Wickham

About Advanced R PDF:

Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does. This is considered as one of the best free R for data science books for beginners, You can download and learn more about this pdf from the below given link.

### Open Data Structures – An Introduction

Author: Pat Morin

About Open Data Structures – An Introduction PDF:

Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code.

### Think Python: How to Think Like a Computer Scientist

Author: Allen B. Downey

About Think Python PDF:

You’ll Learn about following things

- Start with the basics, including language syntax and semantics
- Get a clear definition of each programming concept
- Learn about values, variables, statements, functions, and data structures in a logical progression
- Explore interface design, data structures, and GUI-based programs through case studies

### R for Excel Users

Authors: Julie Lowndes & Allison Horst

About R for Excel Users PDF:

This eBook is for Excel users who want to add or integrate R and RStudio into their existing data analysis toolkit. It is a friendly intro to becoming a modern R user, full of tidyverse, RMarkdown, GitHub, collaboration & reproducibility. This book is written to be used as a reference, to teach, or as self-paced learning. And also, awesomely, it’s created with the same tools and practices we will be talking about: R and RStudio.

### 21 Recipes for Mining Twitter Data with rtweet

The recipes contained in this book use the rtweet package by Michael W. Kearney. As he states in his tome, “this intentionally terse recipe collection provides you with 21 easily adaptable Twitter mining recipes”.

### Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Author: Al Sweigart

About Automate the Boring Stuff with Python PDF:

In Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand—no prior programming experience required. Once you’ve mastered the basics of programming, you’ll create Python programs that effortlessly perform useful and impressive feats of automation to:

- Search for text in a file or across multiple files
- Create, update, move, and rename files and folders
- Search the Web and download online content
- Update and format data in Excel spreadsheets of any size
- And More

This is considered as one of the best free python for data science books for beginners, You can download and learn more about this pdf from the below given link.

### Introduction to Information Retrival

Author: Christopher D. Manning

About Introduction to Information Retrival PDF:

This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You’ll learn about ranking SVMs, XML, DNS, and LSI. You’ll discover the seedy underworld of spam, cloaking, and doorway pages.

### D3 Tips and Tricks

Author: Malcolm Maclean

About D3 Tips and Tricks PDF:

D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization. It’s not written for experts. It’s put together as a guide to get you started if you’re unsure what d3.js can do. It reads more like a story as it leads the reader through the basics of line graphs and on to discover animation, tooltips, tables, interfacing with MySQL databases via PHP, sankey diagrams, force diagrams, maps and more…

### Statistical Learning with Sparsity: The Lasso and Generalizations

Author: A. Martin Wainwright, Robert Tibshirani, and Trevor Hastie

About Statistical Learning with Sparsity: The Lasso and Generalizations PDF:

Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

### Data Visualization: A Practical Introduction

Author: Kieran Healy

About Data Visualization: A Practical Introduction eBook:

Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. This is considered as one of the best free data visualization and data science books for beginners, You can download and learn more about this pdf from the below given link.

### Modeling with Data: Tools and Techniques for Scientific Computing

Author: Ben Klemens

About Modeling with Data: Tools and Techniques for Scientific Computing PDF:

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

### Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Author: Cameron Davidson-Pilon

About Bayesian Methods for Hackers PDF:

Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.

### Data Mining: Practical Machine Learning Tools and Techniques, Third Edition

Author: Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

About Data Mining: Practical Machine Learning Tools and Techniques, Third Edition PDF:

This book offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

### Advanced Statistics From an Elementary Point of View

Author: Michael J. Panik

About Advanced Statistics From an Elementary Point of View PDF:

Advanced Statistics from an Elementary Point of View is a highly readable text that clearly emphasizes the connection between statistics and probability, and helps students concentrate on statistical strategies without being overwhelmed by calculations.

### Introduction to Data Science: Data Analysis and Prediction Algorithms with R

Author: Rafael Irizarry

About Introduction to Data Science: Data Analysis and Prediction Algorithms with R PDF:

This book started out as the class notes used in the HarvardX Data Science Series1. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning.

### A Programmer’s Guide to Data Mining

Author: Ron Zacharski

About A Programmer’s Guide to Data Mining PDF:

This guide follows a learn-by-doing approach. You are encouraged to work through the exercises and experiment with the Python code provided.

The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques. This book is available for download for free under a Creative Commons license.

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### The Data Science Design Manual

Author: Steven Skiena

About The Data Science Design Manual PDF:

The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.

### Oracle Database Notes for Professionals

Author: The StackOverFlow Community

About Oracle Database Notes for Professionals PDF:

This book is the definitive guide to undocumented and partially-documented features of the Oracle Database server. It helps you learn to apply the right solution at the right time, about avoiding risk, about making robust choices related to Oracle databases. It is packed with of experience over decades deep to lay out real-world techniques.

### The Tidyverse Cookbook

Author: Garrett Grolemund

About The Tidyverse Cookbook PDF:

This book collects code recipes for doing data science with R’s tidyverse. Each recipe solves a single common task, with a minimum of discussion.

### SQL Notes for Professionals

Author: The StackOverFlow Community

About SQL Notes for Professionals PDF:

In the SQL Notes for Professionals, experienced SQL developers all over the world share their favorite SQL techniques and features. Learning how to solve real-world problems will give you the skill and confidence to step up in your career. The SQL Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.

### Ethics and Data Science

Author: DJ Patil, Hilary Mason, and Mike Loukides

About Ethics and Data Science PDF:

With this eBook, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day.

### MySQL Notes For Professionals

Author: The StackOverFlow Community

About MySQL Notes for Professionals PDF:

MySQL’s popularity has brought a flood of questions about how to solve specific problems, and that’s where this MySQL Notes for Professionals is essential. When you need quick solutions or techniques, this handy resource provides scores of short, focused pieces of code, hundreds of worked-out examples, and clear, concise explanations for programmers who don’t have the time (or expertise) to solve MySQL problems from scratch.

### PostgreSQL Notes for Professionals

Author: The StackOverFlow Community

About PostgreSQL Notes for Professionals PDF:

This book is the definitive guide to undocumented and partially-documented features of the PostgreSQL server. It helps you learn to apply the right solution at the right time, about avoiding risk, about making robust choices related to PostgreSQL databases. It is packed with of experience over decades deep to lay out real-world techniques. PostgreSQL Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.

### Linear Regression Using R: An Introduction to Data Modeling

Author: David J. Lilja

What’s Special about Linear Regression Using R PDF:

Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.

### Statistical Inference for Data Science

Author: Brian Caffo

About Statistical Inference for Data Science PDF:

This book is written as a companion book to the Statistical Inference Coursera class as part of the Data Science Specialization. However, if you do not take the class, the book mostly stands on its own. A useful component of the book is a series of YouTube videos that comprise the Coursera class.

### The Element of Data Analytic Style

Author: Jeff Leek

About The Element of Data Analytic Style PDF:

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials.

### Causal Inference: What if

Author: James Robins and Miguel Hernán

About In Causal Inference: What if PDF:

The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

### Data Science: Theories, Models, Algorithms, and Analytics

Author: Sanjiv Ranjan Das

About Data Science: Theories, Models, Algorithms, and Analytics PDF:

Table of Contents:

- The Art of Data Science
- The Very Beginning: Got Math?
- Open Source Modeling in R
- More: Data Handling and Other Useful Things
- Being Mean with Variance: Markowitz Optimization
- And More

### Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

Author: Graham J. Williams and Graham Williams

About Data Mining with Rattle and R PDF:

This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built. This is the first step in a journey to data mining and analytics. The book encourages the concept of programming by example and programming with data – more than just pushing data through tools, but learning to live and breathe the data, and sharing the experience so others can copy and build on what has gone before.

### An Introduction to Data Science

Author: Jeffrey M. Stanton and Jeffrey S. Saltz

About An Introduction to Data Science PDF:

An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.

### Data Jujitsu: The Art of Turning Data into Product

Data Jujitsu: The Art of Turning Data into Product

Author: DJ Patil

About Data Jujitsu: The Art of Turning Data into Product PDF:

- Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.
- Learn how to use a problem’s “weight” against itself to:
- Break down seemingly complex data problems into simplified parts
- Use alternative data analysis techniques to examine them
- Use human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problems

### The Art of Data Science

Author: Elizabeth Matsui and Roger D. Peng

About The Art of Data Science PDF:

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

### Data Driven: Creating a Data Culture

Author: DJ Patil and Hilary Mason

About Data Driven: Creating a Data Culture PDF:

You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.

### R Programming for Data Science

Author: Roger D. Peng

About R Programming for Data Science PDF:

Learn how to use R to turn raw data into insight, knowledge, and understanding. This 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. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

### Executive Data Science – A Guide to Training and Managing the Best Data Scientists

Author: Brian Caffo, Roger D. Peng, and Jeffrey Leek

About Executive Data Science PDF:

This book teaches you how to assemble and lead a data science enterprise so that your organization can move towards extracting information from big data. This book is based on the acclaimed Johns Hopkins Executive Data Science Specialization.

### Exploratory Data Analysis with R

Author: Roger D. Peng

About Exploratory Data Analysis with R PDF:

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

### OpenIntro Statistics, 4th Edition

Author: by David Diez, Mine Çetinkaya-Rundel, Christopher Barr

About OpenIntro Statistics, 4th Edition PDF:

There is more than enough material for any introductory statistics course. There are a lot of topics covered. The topics are not covered in great depth; however, as an introductory text, it is appropriate.

### Theory and Applications for Advanced Text Mining

Author: Shigeaki Sakurai

About Theory and Applications for Advanced Text Mining:

This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.

### Data Science: An Introduction WikiBook

Author: Wikibooks

About Data Science: An Introduction WikiBook PDF:

This book is a very basic introduction to data science. It is designed for the advanced high school student or average college freshman with a high school-level understanding of math, science, word processing and spreadsheets. No understanding of computer science is assumed. The main emphasis of this book is to help students think about the world in data science terms. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

### Disruptive Possibilities: How Big Data Changes Everything

Author: Jeffrey Needham

About Disruptive Possibilities: How Big Data Changes Everything PDF:

Disruptive Possibilities provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds. This relentlessly innovative form of computing will soon become standard practice for organizations of any size attempting to derive insight from the tsunami of data engulfing them.

### Introduction to R – Notes on R, A Programming Environment for Data Analysis and Graphics

Author: David M. Smith and William N. Venables

About Introduction to R – Notes on R, A Programming Environment for Data Analysis and Graphics PDF:

This eBook provides a comprehensive introduction to R, a software package for statistical computing and graphics. R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R’s strengths is the ease with which publication-quality plots can be produced in a wide variety of formats. This is a printed edition of the tutorial documentation from the R distribution, with additional examples, notes and corrections.

### Fundamental Numerical Methods and Data Analysis

Author: George W. Collins

About Fundamental Numerical Methods and Data Analysis PDF:

The basic premise of this book is that it can serve as the basis for a wide range of courses that discuss numerical methods used in data analysis and science. It is meant to support a series of lectures, not replace them. To reflect this, the subject matter is wide ranging and perhaps too broad for a single course.

### Introduction to Social Network Methods

Author: Robert Hanneman, Mark Riddle

About Introduction to Social Network Methods PDF:

This textbook introduces many of the basics of formal approaches to the analysis of social networks. The text relies heavily on the work of Freeman, Borgatti, and Everett (the authors of the UCINET software package). The materials here, and their organization, were also very strongly influenced by the text of Wasserman and Faust, and by a graduate seminar conducted by Professor Phillip Bonacich at UCLA. Many other users have also made very helpful comments and suggestions based on the first version.

### Analyzing Linguistic Data: A Practical Introduction to Statistics

Author: R. H. Baayan

About Analyzing Linguistic Data: a practical introduction to statistics PDF:

This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using ‘R’, the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models.

### Introduction to Statistical Thought

Author: Michael Lavine

About Introduction to Statistical Thought PDF:

This free PDF textbook is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking. It is best suited to students with a good knowledge of calculus and the ability to think abstractly. The focus of the text is the ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. Another unusual aspect is the use of statistical software as a pedagogical tool.

### Applied Data Science

Author: Ian Langmore

About Applied Data Science PDF:

“Applied Data Science” is a free data science book that focuses more on the statistics end of things, while also getting readers going on (basic) programming & command line skills. It doesn’t, however, really go into much of the stuff you would expect to see from the machine learning end of things. But that’s OK; there are other book for that. And this book (Applied Data Science) is worth a read for the topics it does cover.

### Data Mining and Knowledge Discovery in Real Life Applications

Author: Julio Ponce

About Data Mining and Knowledge Discovery in Real Life Applications PDF:

This book presents four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social. Twenty six chapters cover different special topics with proposed novel ideas. Each chapter gives an overview of the subjects and some of the chapters have cases with offered data mining solutions. We hope that this book will be a useful aid in showing a right way for the students, researchers and practitioners in their studies.

### The SysAdmin Handbook

Author: Various

About The SysAdmin Handbook PDF:

Authors have brought the best articles together to form The SysAdmin Handbook. With over fifty articles packed into this book, it will be an essential reference for any Systems Administrator, whether you have years of experience or are just starting out.

### Knowledge-Oriented Applications in Data Mining

Author: Kimito Funatsu

About Knowledge-Oriented Applications in Data Mining PDF:

This book is a complete and comprehensive handbook for the application of data mining techniques in marketing and customer relationship management. It combines a technical and a business perspective, bridging the gap between data mining and its use in marketing.

### R and Data Mining: Examples and Case Studies

Author: Yanchang Zhao

About R and Data Mining: Examples and Case Studies PDF:

The book helps researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For the many universities that have courses on data mining, this book is an invaluable reference for students studying data mining and its related subjects.

### Conversations On Data Science

Author: Roger D. Peng and Hilary Parker

About Conversations On Data Science PDF:

Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn’t work) in the real world.

### Advanced Linear Models for Data Science

Author: Brian Caffo

About Advanced Linear Models for Data Science PDF:

In this book, Authors give a brief, but rigorous treatment of advanced linear models. It is advanced in the sense that it is of level that an introductory PhD student in statistics or biostatistics would see. The material in this book is standard knowledge for any PhD in statistics or biostatistics.

### Big Data, Data Mining, and Machine Learning

Author: Jared Dean

About Big Data, Data Mining, and Machine Learning PDF:

Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.

### Inductive Logic Programming: Techniques and Applications

Author: Nada Lavrac

About Inductive Logic Programming: Techniques and Applications PDF:

This book is an introduction to inductive logic programming (ILP), a research field at the intersection of machine learning and logic programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs.

### The Field Guide of Data Science

Author: Booz Allen Hamilton

About The Field Guide of Data Science PDF:

The Field Guide to Data Science spells out what data science is, why it matters to organizations, as well as how to create data science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of data science, practitioners will add to their toolboxes, and beginners will find insights to help them start on their data science journey.

### Modern Data Science for Modern Biology

Author: Susan Holmes

About Modern Data Science for Modern Biology PDF:

This book will teach you ‘cooking from scratch’, from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology.

### Crash Course on Basic Statistics

Author: Marina Wahl

About Crash Course on Basic Statistics PDF:

A Crash Course in Statistics by Ryan J. Winter is a short introduction to key statistical methods including descriptive statistics, one-way and two-way ANOVA, the t-test, and Chi Square. Each of the five chapters provides an overview of each method, and then walks readers through a relevant example, using SPSS to highlight how to run the statistics and how to write up the results in APA style. Each chapter ends with a self-quiz so that readers can assess their understanding of each statistical concept. This is considered as one of the best free statistics and data science books for beginners, You can download and learn more about this pdf from the below given link.

### Hands-on Machine Learning and Big Data

Author: Kareem Alkaseer

About Hands-on Machine Learning and Big Data PDF:

You’ll learn about the following things

- 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

### Mathematics of Data Science

Author: Gabriel Peyré

About Mathematical Foundations of Data Science PDF:

This book presents an overview of important mathematical and numerical foundations for modern data sciences. In particular, it covers the basics of signal and image processing (Fourier, Wavelets, and their applications to denoising and compression), imaging sciences (inverse problems, sparsity, compressed sensing) and machine learning (linear regression, logistic classification, deep learning). The focus is on the mathematically-sound exposition of the methodological tools (in particular linear operators, non-linear approximation, convex optimization, optimal transport) and how they can be mapped to efficient computational algorithms.

### Scipy Lecture Notes

Author: Scipy Lectures

About Scipy Lecture Notes PDF:

Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.

### Statistics With Julia

Author: Yoni Nazarathy and Hayden Klok

About Statistics With Julia PDF:

You’ll learn about following things

- Introducing Julia
- Basic Probability
- Probability Distributions
- Processing and Summarizing Data
- Statistical Inference Concepts
- And More

### A Genetic Algorithm Tutorial

Author: Darrell Whitley

About A Genetic Algorithm Tutorial PDF:

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

### Exploring Data Science with Python

Author: Naomi Ceder

About Exploring Data Science with Python PDF:

Exploring Data with Python is a collection of chapters from three Manning books, hand-picked by Naomi Ceder, the chair of the Python Software Foundation. This free eBook starts building your foundation in data science processes with practical Python tips and techniques for working and aspiring data scientists.

### Understanding Databases

Author: David Clinton

About Understanding Databases PDF:

In this book, you’ll learn about database configuration, how to assess database storage, and how and why to move or copy your database. As you look at relational databases and infrastructure design, you’ll also discover the factors to consider when choosing your database architecture and explore illuminating real-world case studies that shine a light on NoSQL databases and the elements that drive NoSQL business solutions. This on-point guide is a great way to start learning about databases, how to use them, and how to choose the right one for your tasks.

### Exploring Streaming Data Analysis

Author: Alexander Dean

About Exploring Streaming Data Analysis PDF:

You’ll learn the algorithmic side of stream processing, focusing on the what and why of streaming analysis algorithms. You’ll cover common constraints, approaches for thinking about time, and techniques for summarization. Finally, you’ll take a look at how the Kafka Streams framework uses local state to extract the maximum amount of information from event streams. This mini ebook provides the well-rounded introduction you need to get up to speed in the basics of streaming data analysis!

### Exploring Data Science

Author: John Mount and Nina Zumel

About Exploring Data Science PDF:

Exploring Data Science is a collection of five hand-picked chapters introducing you to various areas in data science and explaining which methodologies work best for each. John Mount and Nina Zumel, authors of Practical Data Science with R, selected these chapters to give you the big picture of the many data domains. You’ll learn about time series, neural networks, text analytics, and more.

### Exploring the Data Jungle

Author: Brian Godsey

About Exploring the Data Jungle PDF:

Exploring the Data Jungle: Finding, Preparing, and Using Real-World Data is a collection of three hand-picked chapters introducing you to the often-overlooked art of putting unfamiliar data to good use. Brian Godsey, author of Think Like a Data Scientist, has selected these chapters to help you navigate data in the wild, identify and prepare raw data for analysis, modeling, machine learning, or visualization. As you explore the data jungle you’ll discover real-world examples in Python, R, and other languages suitable for data science.

### Exploring Math for Programmers and Data Scientists

Author: Paul Orland

About Exploring Math for Programmers and Data Scientists PDF:

You’ll start with a look at the nearest neighbor search problem, common with multidimensional data, and walk through a real-world solution for tackling it. Next, you’ll delve into a set of methods and techniques integral to Principal Component Analysis (PCA), an underlying technique in Latent Semantic Analysis (LSA) for document retrieval. In the last chapter, you’ll work with digital audio data, using mathematical functions in different and interesting ways.

### Advances in Evolutionary Algorithms

Author: Witold Kosinski

About Advances in Evolutionary Algorithms PDF:

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

### Genetic Programming: New Approaches and Successful Applications

Author: Sebastian Ventura

About Genetic Programming: New Approaches and Successful Applications PDF:

The purpose of this book is to show recent advances in the field of GP, both the development of new theoretical approaches and the emergence of applications that have successfully solved different real world problems. The volume is primarily aimed at postgraduates, researchers and academics, although it is hoped that it may be useful to undergraduates who wish to learn about the leading techniques in GP.

### Global Optimization Algorithms: Theory and Application

Author: Thomas Weise

About Global Optimization Algorithms: Theory and Application PDF:

This book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by dis- cussing evolutionary algorithms, genetic algorithms, Genetic Programming, Learning Classi- fier Systems, Evolution Strategy, Differential Evolution, Particle Swarm Optimization, and Ant Colony Optimization. It also elaborates on other metaheuristics like Simulated An- nealing, Extremal Optimization, Tabu Search, and Random Optimization.

### Algorithms Notes for Professionals

Author: The Stack Overflow Community

About Algorithms Notes for Professionals PDF:

The Algorithms Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. This is considered as one of the best free data science books for beginners, You can download and learn more about this pdf from the below given link.

### Regression Models for Data Science in R

Author: Brian Caffo

About Regression Models for Data Science in R PDF:

The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in “Statistical inference for data science”. The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations.

### Think Data Structures

Author: Allen Downey

About Think Data Structures PDF:

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.

### Data Visualization in Society

Author: Martin Engebretsen, Helen Kennedy

About Data Visualization in Society PDF:

In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do ‘good’ or ‘bad’? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives

### SQL Server Backup and Restore

Author: Shawn McGehee

About SQL Server Backup and Restore PDF:

In this book, you’ll discover how to perform each of these backup and restore operations using SQL Server Management Studio (SSMS), basic T-SQL scripts and Red Gate’s SQL Backup tool. Capturing backups using SSMS or simple scripts is perfectly fine for one-off backup operations, but any backups that form part of the recovery strategy for any given database must be automated and you’ll also want to build in some checks that, for example, alert the responsible DBA immediately if a problem arises. The tool of choice in this book for backup automation is Red Gate SQL Backup. Building your own automated solution will take a lot of work, but we do offer some advice on possible options, such as PowerShell scripting, T-SQL scripts and SQL Server Agent jobs.

### Making Sense of Stream Processing: Behind Apache Kafka

Author: Martin Kleppmann

About Making Sense of Stream Processing: Behind Apache Kafka PDF:

This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events isn’t new, but with the advent of open source projects such as Apache Kafka and Apache Samza, stream processing is finally coming of age.

### Machine Learning for Data Streams: Practical Examples in MOA

Author: Geoff Holmes, Ricard Gavaldà, Albert Bifet, Bernhard Pfahringer

About Machine Learning for Data Streams PDF:

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

### Just Enough R: Learn Data Analysis with R in a Day

Author: S. Raman

About Just Enough R: Learn Data Analysis with R in a Day PDF:

Learn R programming for data analysis in a single day. The book aims to teach data analysis using R within a single day to anyone who already knows some programming in any other language.

This book has been crafted in a step-by-step manner which we feel is the best way for you to learn a new subject, one step at a time. It also includes various images to give you assurance you are going in the right direction, as well as having exercises where you can proudly practice your newly attained skills.

### Data Blending For Dummies

Author: Michael Wessler

About Data Blending For Dummies PDF:

This book helps you understand the benefits of data blending, and see how to build the data set you need to meet your organization’s analytical needs, without writing scripts or waiting on other departments.

Read this book to learn how to:

- Access, cleanse, and join data in any format from your hard drive, data warehouses, social media, and more
- Prepare data for reports, presentations, visualization, or export to feed downstream processes
- Create an intuitive workflow to document and automate data manipulation tasks

### Data Mining Applications in Engineering and Medicine

Author: Adem Karahoca

About Data Mining Applications in Engineering and Medicine PDF:

In this book, most of the areas are covered by describing different applications. This is why you will find here why and how Data Mining can also be applied to the improvement of project management. Since Data Mining has been widely used in a medical field, this book contains different chapters reffering to some aspects and importance of its use in the mentioned field: Incorporating Domain Knowledge into Medical Image Mining, Data Mining Techniques in Pharmacovigilance, Electronic Documentation of Clinical Pharmacy Interventions in Hospitals etc.

### Understanding Big Data: Analytics for Hadoop and Streaming Data

Author: Chris Eaton and Paul C. Zikopoulos

About Understanding Big Data: Analytics for Hadoop and Streaming Data PDF:

The three defining characteristics of Big Data–volume, variety, and velocity–are discussed. You’ll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide.

### Applied Spatial Data Analysis with R

Author: Edzer J. Pebesma, Roger Bivand, and Virgilio Gomez-Rubio

About Applied Spatial Data Analysis with R PDF:

This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.

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