Mastering Data Analysis With R Pdf Download
4.2 (350 Ratings)
Artificial Intelligence
Artificial Intelligence A Modern Approach, 1st Edition
Stuart Russell, 1995
Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
4.0 (18 Ratings)
Artificial Intelligence
Learning Deep Architectures for AI
Yoshua Bengio, 2009
Foundations and Trends(r) in Machine Learning.
3.5 (2 Ratings)
Artificial Intelligence
The LION Way: Machine Learning plus Intelligent Optimization
Roberto Battiti & Mauro Brunato, 2013
Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.
3.5 (115 Ratings)
Big Data
Disruptive Possibilities: How Big Data Changes Everything
Jeffrey Needham, 2013
This book 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.
4.2 (107 Ratings)
Computer Science Topics
Computer Vision
Richard Szeliski, 2010
Challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which you can use on you own personal media
Languages: Python
4.1 (468 Ratings)
Computer Science Topics
Natural Language Processing with Python
Steven Bird, 2009
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
Languages: Python
4.0 (50 Ratings)
Computer Science Topics
Programming Computer Vision with Python
Jan Erik Solem, 2012
If you want a basic understanding of computer vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. You'll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, etc
3.7 (172 Ratings)
Data Analysis
The Elements of Data Analytic Style
Jeff Leek
Associate Professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health
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.
Data Mining and Machine Learning
A Course in Machine Learning
Hal Daumé III, 2014
Data Mining and Machine Learning
A First Encounter with Machine Learning
Max Welling, 2011
4.1 (5 Ratings)
Data Mining and Machine Learning
Algorithms for Reinforcement Learning
Csaba Szepesvari , 2009
This book gives a very quick but still thorough introduction to reinforcement learning, and includes algorithms for quite a few methods. This is everything a graduate student could ask for in a text.
Data Mining and Machine Learning
A Programmer's Guide to Data Mining
Ron Zacharski, 2015
A guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. This work is licensed under a Creative Commons license.
4.1 (167 Ratings)
Data Mining and Machine Learning
Bayesian Reasoning and Machine Learning
David Barber, 2014
For final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.
Languages: R
Data Mining and Machine Learning
Data Mining Algorithms In R
Wikibooks, 2014
4.1 (11 Ratings)
Data Mining and Machine Learning
Data Mining and Analysis: Fundamental Concepts and Algorithms
Mohammed J. Zaki & Wagner Meria Jr., 2014
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 many more cutting-edge data mining topics.
3.9 (158 Ratings)
Data Mining and Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten & Eibe Frank, 2005
Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.
Languages: R
4.1 (36 Ratings)
Data Mining and Machine Learning
Data Mining with Rattle and R
Graham Williams, 2011
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.
Data Mining and Machine Learning
Deep Learning
Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
4.2 (86 Ratings)
Data Mining and Machine Learning
Gaussian Processes for Machine Learning
C. E. Rasmussen & C. K. I. Williams, 2006
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
4.5 (409 Ratings)
Data Mining and Machine Learning
Information Theory, Inference, and Learning Algorithms
David J.C. MacKay, 2005
"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions." Choice
Data Mining and Machine Learning
Introduction to Machine Learning
Amnon Shashua, 2008
Data Mining and Machine Learning
Introduction to Machine Learning
Alex Smola & S.V.N. Vishwanathan, 2008
Data Mining and Machine Learning
KB – Neural Data Mining with Python Sources
Roberto Bello, 2013
Data Mining and Machine Learning
Machine Learning
Abdelhamid Mellouk & Abdennacer Chebira
2.9 (1 Ratings)
Data Mining and Machine Learning
Machine Learning, Neural and Statistical Classification
D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999
Data Mining and Machine Learning
Machine Learning – The Complete Guide
Wikipedia
4.3 (24 Ratings)
Data Mining and Machine Learning
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014
Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.
Data Mining and Machine Learning
Modeling With Data
Ben Klemens, 2008
Modeling with Data offers a useful blend of data-driven statistical methods and nuts-and-bolts guidance on implementing those methods. --Pat Hall, founder of Translation Creation
Data Mining and Machine Learning
Neural Networks and Deep Learning
Michael Nielsen, 2015
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you concepts behind neural networks and deep learning.
Languages: Python
4.1 (140 Ratings)
Data Mining and Machine Learning
Probabilistic Programming & Bayesian Methods for Hackers
Cam Davidson-Pilon, 2015
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.
Data Mining and Machine Learning
Real-World Active Learning
Ted Cuzzillo, 2015
Applications and Strategies for Human-in-the-loop Machine Learning.
4.5 (431 Ratings)
Data Mining and Machine Learning
Reinforcement Learning: An Introduction
Richard S. Sutton & Andrew G. Barto, 2012
A clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
4.1 (1 Ratings)
Data Mining and Machine Learning
Social Media Mining An Introduction
Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014
Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining
Data Mining and Machine Learning
Theory and Applications for Advanced Text Mining
Shigeaki Sakurai, 2012
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.
4.2 (81 Ratings)
Data Mining and Machine Learning
Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz, 2014
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
4.0 (3 Ratings)
Data Science in General
An Introduction to Data Science
Jeffrey Stanton, Syracuse University
This book was developed for the Certificate of Data Science pro- gram at Syracuse University's School of Information Studies.
3.8 (208 Ratings)
Data Science in General
Data Jujitsu: The Art of Turning Data into Product
DJ Patil, 2012
DJ is the "Data Scientist in Residence" at Greylock Partners
Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.
Data Science in General
School of Data Handbook
School of Data, 2015
The School of Data Handbook is a companion text to the School of Data. Its function is something like a traditional textbook – it will provide the detail and background theory to support the School of Data courses and challenges.
3.8 (11 Ratings)
Data Science in General
The Art of Data Science
Roger D. Peng & Elizabeth Matsui, 2015
This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience...
Languages: JavaScript
3.9 (8 Ratings)
Data Visualization
D3 Tips and Tricks
Malcolm Maclean, 2015
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.
4.1 (422 Ratings)
Data Visualization
Interactive Data Visualization for the Web
Scott Murray, 2013
Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. It's easy and fun with this practical, hands-on introduction.
4.1 (27 Ratings)
Distributed Computing Tools
Data-Intensive Text Processing with MapReduce
Jimmy Lin & Chris Dyer, 2010
MapReduce [45] is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google...
Distributed Computing Tools
Hadoop Illuminated
Mark Kerzner & Sujee Maniyam, 2014
'Hadoop illuminated' is the open source book about Apache Hadoop™. It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.
Distributed Computing Tools
Hadoop Tutorial as a PDF
Tutorials Point
Online Learning Resource
Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines.
3.6 (52 Ratings)
Distributed Computing Tools
Programming Pig
Alan Gates, 2011
Alan is a member of the Apache Software Foundation and a co-founder of Hortonworks.
This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop.
3.6 (309 Ratings)
Forming Data Science Teams
Building Data Science Teams
DJ Patil
DJ is the "Data Scientist in Residence" at Greylock Partners
In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.
3.8 (343 Ratings)
Forming Data Science Teams
Data Driven: Creating a Data Culture
DJ Patil, Hilary Mason
Hilary Mason is the lead scientist at bit.ly, DJ is the "Data Scientist in Residence" at Greylock Partners
In this O'Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.
4.0 (35 Ratings)
Interviews with Data Scientists
The Data Science Handbook
by Carl Shan (Author), William Chen (Author), Henry Wang (Author), Max Song (Author)
25 Data Scientists contributed
The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice.
Languages: Python
4.1 (23 Ratings)
Learning Languages
A Byte of Python
Swaroop C H, 2003
'A Byte of Python' is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.
Languages: R
4.6 (218 Ratings)
Learning Languages
Advanced R
Hadley Wickham, 2014
Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With ten+ years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.
Languages: R
Learning Languages
A Little Book of R for Time Series
Avril Coghlan, 2015
This is a simple introduction to time series analysis using the R statistics software.
Languages: Python
4.3 (1771 Ratings)
Learning Languages
Automate the Boring Stuff with Python: Practical Programming for Total Beginners
Al Sweigart, 2015
Practical programming for total beginners. 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.
Languages: Python
3.8 (260 Ratings)
Learning Languages
Dive Into Python 3
Mark Pilgrim, 2009
Mark Pilgrim is a developer advocate for open source and open standards
This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end.
Languages: R
4.2 (37 Ratings)
Learning Languages
Ecological Models and Data in R
Benjamin M. Bolker, 2008
The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.
Languages: Python
4.1 (127 Ratings)
Learning Languages
Invent with Python
Albert Sweigart
Albert Sweigart, is a software developer in San Francisco, California
"Invent Your Own Computer Games with Python" teaches you computer programming in the Python programming language. Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.
Languages: R
Learning Languages
Learning Statistics with R
Daniel Navarro, 2015
I (Dani) started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form.
Languages: Python
4.1 (14 Ratings)
Learning Languages
Learning with Python 3
Peter Wentworth, Jeffrey Elkner, Allen B. Downey, & Chris Meyers, 2012
Introduction to computer science using the Python programming language. It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures.
Languages: Python
4.0 (9 Ratings)
Learning Languages
Learn Python, Break Python
Scott Grant, 2014
This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever. After all, everybody has to start somewhere.
Languages: Python
3.9 (136 Ratings)
Learning Languages
Learn Python the Hard Way
Zed A. Shaw, 2013
This is a free sample of Learn Python 2 The Hard Way with 8 exercises and Appendix A available for you to review.
Languages: R
Learning Languages
Practical Regression and Anova using R
Julian J. Faraway, 2002
This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.
Languages: Python
4.3 (405 Ratings)
Learning Languages
Python for Everybody
Dr. Charles R Severance, 2016
This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet.
Languages: Python
Learning Languages
Python for You and Me
Kushal Das, 2015
This is a simple book to learn the Python programming language, it is for the programmers who are new to Python.
Languages: Python
Learning Languages
Python Practice Book
Anand Chitipothu, 2014
Anand conducts Python training classes on a semi-regular basis in Bangalore, India.
This book is prepared from the training notes of Anand Chitipothu.
Languages: Python
Learning Languages
Python Programming
Wikibooks, 2015
This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems. This book describes primarily version 2, but does at times reference changes in version 3.
Languages: R
Learning Languages
R by Example
Ajay Shah, 2005
Languages: R
Learning Languages
R Programming
Wikibooks, 2014
The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i.e. a book that can be used by all.
Languages: R
Learning Languages
R Programming for Data Science
Roger D. Peng
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.
Languages: R
Learning Languages
Spatial Epidemiology Notes: Applications and Vignettes in R
Charles DiMaggio, 2014
My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them.
Languages: R
4.0 (7 Ratings)
Learning Languages
The R Inferno
Patrick Burns, 2011
An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks.
Languages: R
Learning Languages
The R Manuals
R Development Core Team
The R Manuals.
Languages: Python
4.1 (73 Ratings)
Learning Languages
Think Python 2nd Edition
Allen Downey, 2015
Allen Downey is a Professor of Computer Science at Olin College
This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. Updated to Python 3.
3.7 (2 Ratings)
Math Topics
A First Course in Linear Algebra
Robert A Beezer, 2012
This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. It has numerous worked examples, exercises and complete proofs, ideal for independent study.
4.3 (23 Ratings)
Math Topics
Elementary Applied Topology
Robert Ghrist, 2014
This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology.
4.6 (5 Ratings)
Math Topics
Elementary Differential Equations
William F. Trench, 2013
This text has been written in clear and accurate language that students can read and comprehend. The author has minimized the number of explicitly state theorems and definitions, in favor of dealing with concepts in a more conversational manner.
4.3 (13 Ratings)
Math Topics
Introduction to Probability
Charles M. Grinstead & J. Laurie Snell, 1997
This book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science.
Math Topics
Linear Algebra
David Cherney, Tom Denton & Andrew Waldron, 2013
Math Topics
Linear Algebra: An Introduction to Mathematical Discourse
Wikibooks
3.5 (1 Ratings)
Math Topics
Linear Algebra, Theory And Applications
Kenneth Kuttler, 2015
This book gives a self- contained treatment of linear algebra with many of its most important applications. It is very unusual if not unique in being an elementary book which does not neglect arbitrary fields of scalars and the proofs of the theorems
Math Topics
Ordinary Differential Equations
Wikibooks
Math Topics
Probabilistic Models in the Study of Language
R Levy, 2012
Math Topics
Probability and Statistics Cookbook
Matthias Vallentin
The probability and statistics cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.
Languages: Cassandra
SQL, NoSQL, and Databases
Cassandra Tutorial as a PDF
Tutorials Point, 2015
Languages: NoSQL
SQL, NoSQL, and Databases
Extracting Data from NoSQL Databases
Petter Näsholm, 2012
Languages: Graph DB
3.6 (21 Ratings)
SQL, NoSQL, and Databases
Graph Databases
Ian Robinson, Jim Webber, & Emil Eifrem, 2013
Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.
Languages: NoSQL
SQL, NoSQL, and Databases
NoSQL Databases
Christof Strauch
Languages: SQL
SQL, NoSQL, and Databases
SQL for Web Nerds
Philip Greenspun
Languages: SQL
SQL, NoSQL, and Databases
SQL Tutorial as a PDF
Tutorials Point
This tutorial will give you a quick start to SQL. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works.
Languages: MongoDB
SQL, NoSQL, and Databases
The Little MongoDB Book
Karl Seguin, 2011
MongoDB is an open source NoSQL database, easily scalable and high performance. It retains some similarities with relational databases which, in my opinion, makes it a great choice for anyone who is approaching the NoSQL world.
3.1 (11 Ratings)
Statistics
A First Course in Design and Analysis of Experiments
Gary W. Oehlert, 2010
Suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, this book offers a superb balance of both analysis and design.
4.6 (1748 Ratings)
Statistics
An Introduction to Statistical Learning with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.
3.6 (19 Ratings)
Statistics
Artificial Intelligence: Foundations of Computational Agents
David Poole & Alan Mackworth, 2010
This is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents.
3.8 (11 Ratings)
Statistics
Intro Stat with Randomization and Simulation
David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015
The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and...
4.1 (34 Ratings)
Statistics
OpenIntro Statistics
David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015
Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org.
4.4 (267 Ratings)
Statistics
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008
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.
3.9 (48 Ratings)
Statistics
Think Bayes: Bayesian Statistics Made Simple
Allen B. Downey, 2012
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.
Languages: Python
3.6 (343 Ratings)
Statistics
Think Stats: Exploratory Data Analysis in Python
Allen B. Downey, 2014
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
4.3 (1612 Ratings)
Pattern Recognition and Machine Learning
Christopher M. Bishop, 2006
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
Source: https://www.learndatasci.com/free-data-science-books/
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