In the previous post we’ve covered 100+ Free Machine Learning and Artificial Intelligence Books. If you haven’t checked make sure you spend 2 minutes after checking this post. In this post, You’ll see 100+ free data science books for beginners, intermediate and experts. The eBooks are updated in 2023 and 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.
Table Of Contents 👉
- Best Data Science Books For Beginners, Intermediate and Advanced Enthusiast
- Top 5 Free Data Science Books For Beginners And Experts
- Free Statistics, Data Mining, Python Data Science, 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 PDF
- Data Science at the Command Line, 2nd Edition
- R Graphics Cookbook, 2nd Edition PDF
- GGPlot2: Elegant Graphics for Data Analysis, 2nd Edition
- R Cookbook: Proven Recipes for Data Analysis, Statistics and Graphics, 2nd Edition
- Think Bayes, 2E PDF
- Building Secure and Reliable Systems PDF
- 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 PDF
- Fundamentals of Data Visualization
- The Data Science Handbook
- Python Data Science Handbook
- Introduction to Probability PDF
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Computer Age Statistical Inference PDF
- Data-Intensive Text Processing with MapReduce PDF
- 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 PDF
- 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 PDF
- 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 PDF
- Data Mining and Analysis: Fundamental Concepts and Algorithms PDF
- Mastering Software Development in R
- Genetic Algorithms in Search, Optimization, and Machine Learning PDF
- Social Media Mining: An Introduction PDF
- Advanced R
- Open Data Structures – An Introduction PDF
- Think Python: How to Think Like a Computer Scientist PDF
- 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 PDF
- D3 Tips and Tricks
- Statistical Learning with Sparsity: The Lasso and Generalizations PDF
- Data Visualization: A Practical Introduction
- Modeling with Data: Tools and Techniques for Scientific Computing PDF
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
- Data Mining: Practical Machine Learning Tools and Techniques, Third Edition PDF
- Advanced Statistics From an Elementary Point of View PDF
- Introduction to Data Science: Data Analysis and Prediction Algorithms with R
- A Programmer’s Guide to Data Mining
- The Data Science Design Manual PDF
- Oracle Database Notes for Professionals PDF
- The Tidyverse Cookbook
- SQL Notes for Professionals PDF
- Ethics and Data Science
- MySQL Notes For Professionals PDF
- PostgreSQL Notes for Professionals PDF
- Linear Regression Using R: An Introduction to Data Modeling
- Statistical Inference for Data Science
- The Element of Data Analytic Style PDF
- Causal Inference: What if PDF
- Data Science: Theories, Models, Algorithms, and Analytics PDF
- Data Mining with Rattle and R PDF
- An Introduction to Data Science PDF
- Data Jujitsu: The Art of Turning Data into Product PDF
- The Art of Data Science PDF
- Data Driven: Creating a Data Culture PDF
- R Programming for Data Science PDF
- 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 PDF
- Introduction to R PDF
- Fundamental Numerical Methods and Data Analysis PDF
- Introduction to Social Network Methods PDF
- Analyzing Linguistic Data: A Practical Introduction to Statistics PDF
- Introduction to Statistical Thought PDF
- Applied Data Science PDF
- Data Mining and Knowledge Discovery in Real Life Applications
- The SysAdmin Handbook PDF
- Knowledge-Oriented Applications in Data Mining
- R and Data Mining: Examples and Case Studies PDF
- Conversations On Data Science
- Advanced Linear Models for Data Science
- Inductive Logic Programming: Techniques and Applications PDF
- The Field Guide of Data Science PDF
- Crash Course on Basic Statistics PDF
- Hands-on Machine Learning and Big Data
- Mathematics of Data Science PDF
- Scipy Lecture Notes PDF
- Statistics With Julia
- A Genetic Algorithm Tutorial
- Exploring Data Science with Python PDF
- Understanding Databases
- Exploring Streaming Data Analysis
- Exploring Data Science PDF
- Exploring the Data Jungle PDF
- Exploring Math for Programmers and Data Scientists PDF
- Advances in Evolutionary Algorithms
- Genetic Programming: New Approaches and Successful Applications
- Algorithms Notes for Professionals PDF
- Regression Models for Data Science in R PDF
- Think Data Structures PDF
- Data Visualization in Society
- SQL Server Backup and Restore PDF
- Making Sense of Stream Processing: Behind Apache Kafka PDF
- Machine Learning for Data Streams: Practical Examples in MOA
- Just Enough R: Learn Data Analysis with R in a Day PDF
- Data Blending For Dummies PDF
- Data Mining Applications in Engineering and Medicine
- Understanding Big Data: Analytics for Hadoop and Streaming Data PDF
- Applied Spatial Data Analysis with R PDF
- Frequently Asked Questions:
Best Data Science Books For Beginners, Intermediate and Advanced Enthusiast
- 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
- Statistics in Plain English, Third Edition
- Fundamentals of Data Visualization
Note: We’ve added affiliate links of all the books for you if you want to buy the book. As an Amazon Associate, We may get a small amount of commission for referring you.
Top 5 Free Data Science Books For Beginners And Experts
|Best Books For Data Science||Suitable For||Author||Ratings||Experts Review||Read Or Download Link|
|The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists||Beginners||Henry Wang, William Chen, Carl Shan, Max Song||4.4/5 Star By 56 Readers||Ferris Jumah, LinkedIn Data Scientist: The Data Science Handbook offers practical, sound advice, from the top industry experts who’ve collectively shaped data science into what it is today.||Read|
|2. The Art of Data Science||Beginners||Roger D. Peng and Elizabeth Matsui||4.6/5 Star By 47 Readers||An easy to read (and understand) introduction introduce to the data analysis workflow. This should be required reading for learners and new practitioners alike.||Read|
|3. R for Data Science||Advanced||Hadley Wickham & Garret Grolemund||4.7/5 Star By 1402 Readers||Wickham and Grolemund have produced an excellent book that would help a beginning R user become very efficient in explanatory analysis.||Read|
|4. Introduction to Modern Statistics||Beginner And Intermediate||Mine Çetinkaya-Rundel And Johanna Hardin||4.9/5 Star By 25 Reader||Easy to read, easy to understand, and great data sets. Best intro ever! I recommend this book to everyone!!||Read|
|5. Python Data Science Book||Advanced||4.6/5 Star By 600 Readers||I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science.||Read|
Free Statistics, Data Mining, Python Data Science, Mathematics, Data Visualization, SQL & Data Analytics Books Are As Follows
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. No coding experience is required. To learn more about this data visualization book, visit the below given link
Authors: Gareth James, Daniela Witten, Trevor Hastie & Rob Tibshirani
About An Introduction to Statistical Learning, 2nd Edition Book:
An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. The second edition explores topics like Deep learning, Survival analysis, Multiple testing, Naive Bayes, etc.
Author: Jeroen Janssens
About Data Science at the Command Line, 2nd Edition PDF:
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.
Author: Winston Chang
About R Graphics Cookbook, 2nd Edition Book:
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. To learn more about this R data science book, visit the below given link
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. 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.
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. If you are an intermediate user, this book is useful for expanding your horizons and jogging your memory.
Author: Allen B. Downey
About Think Bayes, Second Edition Book:
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. To learn more about this data science book, visit the below given link
Author: Heather Adkins, Ana Oprea, Paul Blankinship, Piotr Lewandowski, Adam Stubblefield, Betsy Beyer
About Building Secure and Reliable Systems Book:
In this book, experts from Google share best practices to help your organization design scalable and reliable systems that are fundamentally secure. They also discuss how building and adopting their recommended best practices requires a culture that’s supportive of such change.
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.
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.
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. 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.
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.
- Are You Looking For Open Source Data Science GitHub Projects And Repos? If Yes, Then You Must Check Out This Updated List: Best GitHub Repositories For Data Science
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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.
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. 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.
Author: Stanley Chan
About Introduction to Probability for Data Science Book:
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).
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.
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
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
Author: Charles M. Grinstead, J. Laurie Snell
About Introduction to Probability Book:
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. To learn more about this mathematics for data science book, visit the below given link.
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. 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.
Authors: Bradley Efron and Trevor Hastie
About Computer Age Statistical Inference Book:
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.
Author: Chris Dyer and Jimmy Lin
About Data-Intensive Text Processing with MapReduce Book:
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. This book not only intends to help the reader “think in MapReduce”, but also discusses limitations of the programming model as well.
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. Also it will not teach you anything about R programming.
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.
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. 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.
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.
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.
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.
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
About The Elements of Statistical Learning Book:
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.
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.
Authors: Emil Hvitfeldt and Julia Sigle
About Supervised Machine Learning for Text Analysis in R PDF:
The book is divided into three sections. 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.
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.
Authors: Kenneth Reitz & Tanya Schlusser
About The Hitchhiker’s Guide to Python Book:
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.
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.
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.
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.
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.
Exploring, Visualizing, and Modeling Big Data with R
Authors: Okan Bulut And Christopher Desjardins
About Exploring, Visualizing, and Modeling Big Data with R PDF:
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). This book was originally conceived to support a one-semester, 13-week undergraduate course in data science.
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.
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.
Author: Avrim Blum, John Hopcroft, and Ravindran Kannan
About Foundations of Data Science Book:
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.
Author: Mohammed J. Zaki
About Data Mining and Analysis: Fundamental Concepts and Algorithms Book:
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.
Mastering Software Development in R
Author: Roger D. Peng, Sean Kross, and Brooke Anderson
About Mastering Software Development in R PDF:
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.
Author: David E. Goldberg
About Genetic algorithms in search, optimization, and machine learning Book:
A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms.
Author: Novel by Huan Liu, Mohammad Ali Abbasi, and Reza Zafarani
About Social Media Mining: An Introduction Book:
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.
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.
Open Data Structures – An Introduction PDF
Author: Pat Morin
About Open Data Structures – An Introduction Book:
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.
Author: Allen B. Downey
About Think Python Book:
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. 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”.
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. To learn more about this python for data science book, visit the below given link.
Author: Christopher D. Manning
About Introduction to Information Retrival Book:
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.
Author: Malcolm Maclean
About D3 Tips and Tricks PDF:
Author: A. Martin Wainwright, Robert Tibshirani, and Trevor Hastie
About Statistical Learning with Sparsity: The Lasso and Generalizations Book:
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.
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.
Author: Ben Klemens
About Modeling with Data: Tools and Techniques for Scientific Computing Book:
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.
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.
Author: Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
About Data Mining: Practical Machine Learning Tools and Techniques, Third Edition Book:
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. To learn more about this data mining book, visit the below given link.
Author: Michael J. Panik
About Advanced Statistics From an Elementary Point of View Book:
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.
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. To learn more about this data mining book, visit the below given link.
- Short Quotes, Experts Opinions And Best Thoughts About AI, ML, Big Data And Data Science: 100+ Best Quotes On Machine Learning, AI And Data Science
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Author: Steven Skiena
About The Data Science Design Manual Book:
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.
Oracle Database Notes for Professionals PDF
Author: The StackOverFlow Community
About Oracle Database Notes for Professionals Book:
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.
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 PDF
Author: The StackOverFlow Community
About SQL Notes for Professionals Book:
In the SQL Notes for Professionals, experienced SQL developers all over the world share their favorite SQL techniques and features. The SQL Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.
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 PDF
Author: The StackOverFlow Community
About MySQL Notes for Professionals Book:
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. To learn more about this MySQL for data science book, visit the below given link.
PostgreSQL Notes for Professionals PDF
Author: The StackOverFlow Community
About PostgreSQL Notes for Professionals Book:
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.
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.
The Element of Data Analytic Style PDF
Author: Jeff Leek
About The Element of Data Analytic Style Book:
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 PDF
Author: James Robins and Miguel Hernán
About In Causal Inference: What if Book:
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.
Data Science: Theories, Models, Algorithms, and Analytics PDF
Author: Sanjiv Ranjan Das
About Data Science: Theories, Models, Algorithms, and Analytics Book:
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
Author: Graham J. Williams and Graham Williams
About Data Mining with Rattle and R Book:
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 visualized and some models built. This is the first step in a journey to data mining and analytics.
Author: Jeffrey M. Stanton and Jeffrey S. Saltz
About An Introduction to Data Science Book:
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.
Data Jujitsu: The Art of Turning Data into Product
Author: DJ Patil
About Data Jujitsu: The Art of Turning Data into Product Book:
- 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
Author: Elizabeth Matsui and Roger D. Peng
About The Art of Data Science Book:
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.
Data Driven: Creating a Data Culture PDF
Author: DJ Patil and Hilary Mason
About Data Driven: Creating a Data Culture Book:
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.
Author: Roger D. Peng
About R Programming for Data Science Book:
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.
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.
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
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.
Disruptive Possibilities: How Big Data Changes Everything PDF
Author: Jeffrey Needham
About Disruptive Possibilities: How Big Data Changes Everything Book:
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.
Author: David M. Smith and William N. Venables
About Introduction to R – Notes on R, A Programming Environment for Data Analysis and Graphics Book:
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.
Fundamental Numerical Methods and Data Analysis PDF
Author: George W. Collins
About Fundamental Numerical Methods and Data Analysis Book:
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. To learn more about this data analysis book, visit the below given link.
Introduction to Social Network Methods PDF
Author: Robert Hanneman, Mark Riddle
About Introduction to Social Network Methods Book:
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).
Author: R. H. Baayan
About Analyzing Linguistic Data: a practical introduction to statistics Book:
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.
Introduction to Statistical Thought PDF
Author: Michael Lavine
About Introduction to Statistical Thought Book:
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.
Applied Data Science PDF
Author: Ian Langmore
About Applied Data Science Book:
“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.
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.
The SysAdmin Handbook PDF
About The SysAdmin Handbook Book:
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 PDF
Author: Yanchang Zhao
About R and Data Mining: Examples and Case Studies Book:
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 Book:
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 Book:
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.
Inductive Logic Programming: Techniques and Applications PDF
Author: Nada Lavrac
About Inductive Logic Programming: Techniques and Applications Book:
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 PDF
Author: Booz Allen Hamilton
About The Field Guide of Data Science Book:
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.
Crash Course on Basic Statistics PDF
Author: Marina Wahl
About Crash Course on Basic Statistics Book:
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. To learn more about this statistics book, visit 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 PDF
Author: Gabriel Peyré
About Mathematical Foundations of Data Science Book:
This book presents an overview of important mathematical and numerical foundations for modern data sciences. To learn more about this mathematics for data science book, visit the below given link.
Scipy Lecture Notes PDF
Author: Scipy Lectures
About Scipy Lecture Notes Book:
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.
Exploring Data Science with Python PDF
Author: Naomi Ceder
About Exploring Data Science with Python Book:
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. To learn more about this python data science book, visit the below given link.
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. To learn more about this database book, visit the below given link.
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.
Exploring Data Science PDF
Author: John Mount and Nina Zumel
About Exploring Data Science Book:
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.
Exploring the Data Jungle PDF
Author: Brian Godsey
About Exploring the Data Jungle Book:
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.
Exploring Math for Programmers and Data Scientists PDF
Author: Paul Orland
About Exploring Math for Programmers and Data Scientists Book:
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. To learn more about this mathematics for data science book, visit the below given link.
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.
Algorithms Notes for Professionals PDF
Author: The Stack Overflow Community
About Algorithms Notes for Professionals Book:
The Algorithms Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. To learn more about this algorithms for data science book, visit the below given link.
Regression Models for Data Science in R PDF
Author: Brian Caffo
About Regression Models for Data Science in R Book:
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”.
Author: Allen Downey
About Think Data Structures Book:
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.
SQL Server Backup and Restore PDF
Author: Shawn McGehee
About SQL Server Backup and Restore Book:
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. To learn more about this SQL data science book, visit the below given link.
Making Sense of Stream Processing: Behind Apache Kafka PDF
Author: Martin Kleppmann
About Making Sense of Stream Processing: Behind Apache Kafka Book:
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.
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 PDF
Author: S. Raman
About Just Enough R: Learn Data Analysis with R in a Day Book:
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. To learn more about this R for data science book, visit the below given link.
Data Blending For Dummies PDF
Author: Michael Wessler
About Data Blending For Dummies Book:
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.
Understanding Big Data: Analytics for Hadoop and Streaming Data PDF
Author: Chris Eaton and Paul C. Zikopoulos
About Understanding Big Data: Analytics for Hadoop and Streaming Data Book:
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 PDF
Author: Edzer J. Pebesma, Roger Bivand, and Virgilio Gomez-Rubio
About Applied Spatial Data Analysis with R Book:
This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. To learn more about this data analysis book, visit the below given link.
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Frequently Asked Questions:
What Book Should I Read For Data Science?
If you’re new to data science then go with ‘The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists By Henry Wang, William Chen, Carl Shan, Max Song’.
Which Book Is Best For Data Science Beginners?
Best Data Science Books For Beginners Are The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists, Python Data Science Handbook, Fundamental Of Data Visualizations, The Art Of Data Science And Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python.
What Books Should I Read For Statistics?
Three Best Statistics Books You must check and read if you’re a beginner or an expert are Statistics in Plain English, Third Edition, Introduction to Modern Statistics, Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python.
Which Is The Best Book On Statistics For Beginners?
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python is the best book on statistics for beginners.
Can I Teach Myself Data Science?
You can study data science by yourself using free books, forums or YouTube videos / courses. There’s a wealth of resources for learning on the Internet when you’re trying to build a career in the field of data science. Use YouTube Course/Videos for visual learning, blogs and books for reading and forums for doubt solving or help.
What Are Top 3 Skills For Data Scientist?
Top Technical Skills Required to Become a Data Scientist
1. It all begins With the Basics – Programming Language (Python or R) + Database
2. Mathematics for Data Science
3. Data Analysis & Visualization
4. Web Scraping
5. Big Data