All | Algorithms | Artificial Intelligence | Bayesian Networks | Big Data Analytics | Business & Strategy | Computer Vision | Data Journalism | Data Mining | Data Scientists | Data Structures | Data Visualization | General Data Science | Hadoop, MapReduce | Information Retrieval | Linear Regression | Linguistic | Machine Learning, Predictive Analytics | Math | Metadata | Natural Language Processing | Network Science | Other Sites with Free Data Science Resources | Probability | Python | R | Singularity/Transhumanism | Statistics | Text Mining

The Ray Kurzweil Reader: A collection of essays by Ray Kurzweil published on KurzweilAI.net 2001-2003.Synthetic Super Intelligence and the Transmutation of Humankind. A roadmap to the singularity and beyond

Breaking Data Science Open. Deliver collaboration, self-service and production deployment with open data science

Spark: The Definitive Guide. A deep dive into how Spark runs on a cluster; detailed examples in SQL, Python and Scala; Structured Streaming and Machine Learning; examples of GraphFrames and Deep Learning with TensorFrames

Artificial Intelligence. Documentation artificial intelligence and machine learning topics

Serving Machine Learning Models. A guide to architecture, stream processing engines, and frameworks

Artificial Intelligence: A Modern Approach.

Computer Vision: Algorithms and Applications.

Think Stats: Exploratory Data Analysis in Python. An introduction to the practical tools of exploratory data analysis

Algorithms for Reinforcement Learning.

Learn SQL The Hard Way. A crash course in the basics of SQL to store, structure, and analyze data

Data Mining: Practical Machine Learning Tools and Techniques.

Understanding the Chief Data Officer. How leading businesses are transforming thÂ?emselves with data

Machine Learning.

R Programming for Data Science. The fundamentals of R programming using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization

Python Programming. This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems

Data Mining Algorithms in R.

Machine Learning, Neural and Statistical Classification.

Introduction to Machine Learning.

D3 Tips and Tricks. Over 600 pages of tips and tricks for using d3.js, one of the leading data visualization tools for the web.

Advanced R. Designed primarily for R users who want to improve their programming skills and understanding of the language

Social Media Mining.

The Data Analytics Handbook. Interviews with data scientists, data analysts, CEOs, managers, and researchers at the cutting edge of the data science industry

Learn Python the Hard Way.

Think Python: How to Think Like a Computer Scientist.

KB - Neural Data Mining with Python sources. Algorithms to extract the knowledge hidden inside data using Python language

SQL Tutorial. 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

A First Course in Design and Analysis of Experiments. This text covers the basic topics in experimental design and analysis

Data Mining with Rattle and R. The art of excavating data for knowledge discovery

The Elements of Data Analytic Style. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks

Machine Learning - The Complete Guide.

R Programming. A practical guide to the R programming language

Automate the Boring Stuff with Python. Practical Python programming for total beginners

Introduction to Machine Learning. Notes surveying many of the important topics in machine learning circa the late 1990s

Understanding Machine Learning: From Theory to Algorithms. An extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms

O'Reilly Free Data Ebooks. The best data insights from O'Reilly editors, authors, and Strata speakers for you in one place

Machine Learning Yearning. The goal of this book is to teach you how to make the numerous decisions needed with organizing a machine learning project

Statistical foundations of machine learning. Statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data

A Deep Learning Tutorial: From Perceptrons to Deep Networks.

Deep Learning for Natural Language Processing. A discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization

Unsupervised Feature Learning and Deep Learning.

Deep Learning: Methods and Applications. An overview of general deep learning methodology and its applications to a variety of signal and information processing tasks

Deep Learning. Resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular

Machine Learning by Andrew Ng. In this course, you'll learn about some of the most widely used and successful machine learning techniques

Neural Networks and Deep Learning. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing

Unsupervised Feature Learning and Deep Learning.

Implementing Big Data Analysis. Learn how to use Windows Azure HDInsight to process big data and to generate results for analysis and reporting with Microsoft data tools

Working with big data on Azure. Explores the impact of big data on businesses, and shows how to deploy Hadoop clusters and run MapReduce on Azure to turn this data into insights

Deep Learning Tutorial.

Data Visualization with JavaScript.

Frontiers in Massive Data Analysis. The promise and perils of massive data

The Free Hive Book. A free electronic book about Apache Hive. The book is geared towards SQL-knowledgeable business users with some advanced tips for devops

Big Data Sourcebook: Your Guide to the Data Revolution. Guide to the enterprise and technology issues of the 'big data' phenomenon

STATISTICS Methods and Applications. A nearly encyclopedic comprehensive presentationof statistical methods and analytic approaches used in science, industry, business, and data mining written from the perspective of the real-life practitioner

No More Secrets with Big Data Analytics. A sound basis for updating or refining your understanding of Big Data Analytics

Harnessing The Power Of Big Data Through Education And Data-Driven Decision Making.

Planning for Big Data. A CIO's Handbook to the Changing Data Landscape

Why Most Big Data Projects Fail. Learning from Common Mistakes to Transform Big Data into Insights

An Introduction to Unsupervised Learning via Scikit Learn.

The Data Science Handbook (Free Pre-Release). Advice and Insights from 25 Amazing Data Scientists

How to Build and Lead a Winning Data Team. Understanding the difference between a traditional analytics team and one that's set up to exploit big data

Data Driven: Creating a Data Culture. The steps needed for your company to be truly data-driven, including the questions you should ask and the methods you should adopt

Big Data Analytics with Twitter. Taught in close collaboration with Twitter, focuses on the tools and algorithms for data analysis as applied to Twitter's data

Natural Language Processing with Python. A book about Natural Language Processing

Data Discovery for Dummies. Data discovery, what it does and how it makes dealing with big data simpler

Introduction to Data Science. A series of data problems of increasing complexity to illustrate the skills and capabilities needed by data scientists

Big Data Analytics Infrastructure for Dummies. The emphasis is on hardware infrastructure - processing, storage, systems software, and internal networks

Time Series Databases: New Ways to Store and Access Data. A new world of time series data calls for new approaches and new tools to store and access it

The Promise and Peril of Big Data. New challenges and questions facing big data: from ethics to scientific methodologies to evolution of knowledge

Data-Intensive Text Processing with MapReduce. Scalable approaches to processing large amounts of text with MapReduce

Fundamental Numerical Methods and Data Analysis. A wide range of courses that discuss numerical methods used in science

Advanced Data Analysis from an Elementary Point of View. Modern methods of data analysis and the considerations which go into choosing the right method for the job at hand

Free Data Science Books from Intech. Lots of Data Science books you can read online. Search for them using keywords like data science, data mining, machine learning, etc

R and Data Mining: Examples and Case Studies. Various data mining functionalities in R and three case studies of real world applications

The Wealth of Networks: How Social Production Transforms Markets and Freedom. How information, knowledge and culture are produced and exchanged in our society

simpleR - Using R for Introductory Statistics. How to use R while learning introductory statistics

Analyzing the Analyzers: An introspective survey of data scientists and their work. Survey of several hundred data science practitioners in mid-2012

Concepts and Applications of Inferential Statistics. A full-length and occasionally interactive statistics textbook.

Bayesian Reasoning and Machine Learning. Unified treatment via graphical models, a marriage between graph and probability theory, facilitating the transference of Machine Learning concepts

OpenIntro Statistics. Foundation of statistical thinking and methods

Think Bayes: Bayesian Statistics Made Simple. Bayesian statistics with Python and discrete approximations

New Fundamental Technologies in Data Mining. In-depth description of novel mining algorithms and many useful applications

Data Journalism. For anyone who thinks that they might be interested in becoming a data journalist, or dabbling in data journalism

A Course in Machine Learning. Covers most major aspects of modern machine learning

Business Models for the Data Economy. Using data to advance your business

Mathematics for Computer Science. Mathematical models and methods to analyze problems that arise in computer science

Learn Data Science. A collection of Data Science materials written in the form of IPython Notebooks

Data Jujitsu: The Art of Turning Data into Product. Using multiple data elements in clever ways to solve iterative problems that, when combined, solve a data problem that might otherwise be intractable

Data Visualization for All. This book helps everyone create interactive charts, maps, and simple web apps to tell stories about your data

Deep Learning by Samy Bengio, Tom Dean and Andrew Ng. Learn about widely used and successful machine learning techniques, with the opportunity to implement these algorithms

Data Science on edX. edX does not have as many free online data science courses as Coursera, but itâ??s still an applaudable collection

School of Data. Teaching people how to gain powerful insights and create compelling stories using data

Interactive Data Visualization for the Web. Visualizing data is the fastest way to communicate it to others

SQL School by Mode Analytics. Made up of 2 modules: a) SQL skill building b) Analytics training - learning to think like an analyst

A Practitioner's Guide to Generalized Linear Models. Written for the practising actuary who would like to understand generalized linear models (GLMs) and use them to analyze insurance data

Business Analytics in Retail for Dummies. Using business analytics to discover insights that, when acted on, drive revenue growth and improve customer relations

Big Data Now: Current Perspective from O'Reilly Media. Top big data posts from late fall 2012 through late fall 2013

Collaborative Statistics. An introductory statistics course for students majoring in fields other than engineering or math. The only prerequisite is intermediate algebra

Data Science: An Introduction by Wikibooks. Basic introduction to data science designed for the advanc ed high school student or average college freshman

What is Data Science?. A book on data science and how it enables the creation of data products

An Introduction to R. R is an integrated suite of software facilities for data manipulation, calculation and graphical display

Modeling with Data: Tools and Techniques for Scientific Computing. Executing computationally intensive analyses on very large data sets

Data Mining and Analysis: Fundamental Concepts and Algorithms. Fundamental algorithms in data mining and analysis that form the basis for the emerging field of data science

Network Science. Concepts of network science and the tools that can be used to study real networks and interpret the obtained results

Applied Data Science. Taking people with strong mathematical / statistical knowledge and teaching them software development fundamentals

Theory and Applications for Advanced Text Mining. Advanced text mining techniques

Analyzing Linguistic Data: A practical introduction to statistics. Statistical analysis of language, designed for linguists with a non-mathematical background

Think Stats: Probability and Statistics for Beginners. Emphasizes the use of statistics and a computational approach to explore large datasets

An Introduction to Information Retrieval. Scientific underpinnings of information retrieval

Statistics with R. Notes author took while discovering and using the statistical environment R

A First Encounter with Machine Learning. A first read to wet the appetite so to speak, a prelude to the more advanced machine learning topics

Mining of Massive Datasets. Data mining of very large amounts of data, that is, data so large it does not fit in main memory

The Field Guide to Data Science. Appreciating the insights data can provide us today

Practical Machine Learning: Innovations in Recommendations. Innovations that make machine learning practical for business production settings

Probabilistic Programming & Bayesian Methods for Hackers. Intro to Bayesian methods and probabilistic programming

Introduction to Probability. Introductory probability course

Information Theory, Inference, and Learning Algorithms. Bayesian data modelling, Monte Carlo methods, variational methods, clustering algorithms, and neural networks

An Introduction to Data Mining. A creative way of learning about data mining

Codecademy: Python. An interactive "Python for Beginners" tutorial that allows you to code while learning the fundamentals

Data Science with CMU's Open Learning Initiative. As of today, the only data science related MOOCs offered by CMU's Open Learning Initiative are those related to statistics

Disruptive Possibilities: How Big Data Changes Everything. Evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds

Data Science and Cognitive Computing Courses. Build Data Science and Cognitive Computing skills for free today

Foundation of Data Science. The book focuses on the mathematical foundations rather than dwell on particular applications that are only briefly described

The Elements of Statistical Learning (Data Mining, Inference and Prediction). Bringing together many new ideas in learning and explaining them in a statistical framework

The Fourth Paradigm: Data-Intensive Scientific Discovery. This book is about a new, fourth paradigm for science based on data-intensive computing

An Introduction to Statistical Learning (with applications in R). For those wishing to use statistical learning tools to analyze their data

Real-Time Big Data Analytics: Emerging Architecture. Using real-time big data analytics (RTBDA) to improve sales, lower costs a ticket to improved sales, higher profits and lower marketing costs

The Evolution of Data Products. Discusses what happens when data becomes a product, specifically, a consumer product, and where these data products headed

Introduction to Social Network Methods. Building an understanding of a social network with complete and rigorous descriptions of social relationship patterns

Field Guide to Hadoop. An introduction to Hadoop, its ecosystem and aligned technologies

Introduction to Metadata. Overview of metadata and its current trends, especially that of metadata created by users

Knowledge-Oriented Applications in Data Mining. In-depth description of novel mining algorithms and many useful applications

Networks, Crowds & Markets: Reasoning about a highly connected world. Links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else

Forecasting: principles and practice. Comprehensive introduction to forecasting methods

Predictive Analytics for Dummies. Providing decision makers and analysts with the capability to make accurate predictions about future events based on complex statistical algorithms

Introduction to Statistical Thought. A focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice

Introduction to Machine Learning. Very comprehensive overview of machine learning

Statistics by Wikibooks.org. Modern statistics and some practical applications of statistics

Gaussian Processes for Machine Learning. One of the most important Bayesian machine learning approaches

Data Mining and Knowledge Discovery in Real Life Applications. Four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social

Data Blending for Dummies. Simplifying data blending for the analyst

A Programmer's Guide to Data Mining. Tool for learning basic data mining techniques

Interactive Data Visualization for the Web. Dynamic, interactive visualizations to empower people to explore the data for themselves

Building Data Science Teams. What data scientists add to an organization, how they fit in, and how to hire and build effective data science teams

Computer Vision: Models, Learning, and Inference. A principled model-based approach to computer vision that unifies disparate algorithms, approaches, and topics under the guiding principles of probabilistic models, learning, and efficient inference algorithms

Data Mining: Concepts and Techniques. Concepts and techniques of data mining

Data Mining with Weka. A collection of machine learning algorithms for data mining tasks that can either be applied directly to a dataset or called from your own Java code

Data Science with MIT Open Courseware. A choke-full of data science courses, from data mining to data visualization to social networks

Data+Design: A simple introduction to preparing and visualizing information. The actual set of steps that need to be accomplished before data can be visualized, from the design of the survey to the collection of the data to ultimately its visualization

Harvard Data Science Course. A fantastic series of lectures pertaining to Data Science