|Intorduction to Probablistic Modeling and Machine Learning from University of Cambridge, UK
||Published 2013, one hour and thirtyone minutes. Professor Ghahramani discussses machine learning and nonparametric bayesian statistics. This link leads to a series of lectures touching upon statistical learning
||Big Data: A Revolution That Will Transform How We Live, Work, and Think written by Viktor Mayer-Schonberger & Kenneth Cukier
||Explores big data in health, business, and politics. Provides highly detailed intro to big data, uncovering some of the most pressing issues related to current and future applications.
||A Very Short History of Data Science
||"How data scientists became sexy." Lists a number of books ranging from 1962 to 2009 exploring data analysis.
||EDX.Inc: Data Science & Machine Learning Essentials
||Data Science and Machine Learning Essentials. Free (or 50$ for verified certificate) 3-4 hrs a week for 5 weeks. An intermediate session learning key concepts of data science &
|R Sessions 1 Statistical Learning Introduction
||Helpful series to aid readers in learning R.
||Automate This: How Algorithms Came to Rule Our World written by Christopher Steiner
||Explains how algorithms are increasingly being used to tackle high-level tasks once only achieved by humans with advanced training. Illustrates how algorithms have far exceeded the expectations of their creators and how the "bolt revolution" is penetrating every aspect of our lives.
||Hands-On Data Science with R<
||Provides free material to support the Data Scientist. Lists drafts of chapters in an upcomming book on R Programming and Data Science. Covers predictive analytics, advnaced analytics, advance R, and descriptive analytics.
||EDX.Inc: The Analytics Edge (MIT)
||How to implement the following methods in R: analytics methods, linear regression, logistic regression, CART, clustering, and data visualization. Also introduces LibreOffice. 10-15 weeks
|Ariel Roken: Statistical learning of human brain structure
||PyData Seattle 2015 allows viewers to learn a set of powerful principles and tools to interpret data from many different domains. This video introduces how statistical learning occurs through our brains.
||The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
||How forecasters are able to overcome biases and unpredictability to uncover accurate, meaningful predicions in a vast sea of noisy data. Shows how without accurate methods, the abundance of data can make predictions go bad, especially when confronted with the imits of human cognition.
||R VS Python for Data Science
||Graphical polls and descriptions displaying the significance between using Python and R. Each has their own pros and cons, selecting one over the other will depend on the use-cases, the cost of learning, and other common tools required.
||Coursera: Introduction to Data Science
||This course covers data manipulation, analytics, communicating results, and special tools. 10-12 weeks
|The Future of Data Science @ Stanford
||This video from July 2015 includes an intelligent panel of Stanford guests discussing the significance of Data Science and what opportunites and challenges are to come.
||Big Data at Work: Dispelling the Myths, Uncovering the Opportunities written by Thomas H. Davenport
||Explains how big data can illuminate decision making, improve customer relationships and streamline organizations. Tells theopportunities, impact and critical factors for successfully using big data in business. For businesses interested in harnessing the power of big data, illustrates how leading organizations are using data science to improve the way they do business
||Revolution Analytics: What is R?<
||Introduction to the importance and significance of R. Includes finance and analytic examples of driven companies like Google, Facebook, and LinkedIn. Involves an applications tab including Data Science touching upon the multi-layer data architectures.
||Stanford University: Statistical Learning
||A free statistical learning course lasting four months and requiring a minimum of 3 hours per week attention. Discusses, nonlinear models, bootstrap model, logistic regression, linear discriminant analysis, R, and much more.
|Data Science Part I Building Predictive Analytics by Derek Kane
||This video is part of a series of machine learning and statistical learning topics to introduce predictice analytics and how ot build/sustain them.
||Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die written by Eric Siegel
||For new data scientists, offering insights into the complex world of data analysis. Tells how institutions are increasingly predicting human behavior highlighting the ways predictive analysis is able to improve healthcare, fight crime, and boost sales.
||The Challenges of Data Quality and Data Quality Assessment in the Big Data Era
||This paper summarizes reviews of data quality research and data characteristics of the big data environment. The results include the theoretical scope of big data and a foundation for the future through establishing an assessment model and studied evaluation algorithms.
|The Data Science Revolution (Jeremy Howard) Exponential Finance 2014
||This video is more of a financial perspective of the impact and potential of data science. Gives real life case studies and examples.
||Privacy in the Age of Big Data: Recognizing Threats, Defending Your Rights, and Protecting Your Family written by Theresa M. Payton and Ted Claypoole
||Describes the ramifications of data collection and surveillance, offering solutions to those who prefer to remain private.
||The 123 Most Influential People in Data Science
||Interesting data and graphical representations of the most influential practitioners, company accounts, media sources, and countries.