http://ocw.mit.edu/index.htm
The list of courses is here
http://ocw.mit.edu/courses/index.htm
Something every one should know and use.
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Month: October 2014
Creating the field of evidence based data analysis – do people know what a p-value looks like?
How to Build Dashboards That Persuade, Inform and Engage …….Tableau Software
Be open to critiquing & iterating your dashboards—making them more impactful. Read how: http://tabsoft.co/1pBRfrb
Few other resources for learning data analytics
Here are some general resources that you may find useful for a further study. This list is by no means complete. We welcome additional suggestions through the appropriate discussion thread.
Some other on-line classes and tutorials in this arena:
- Yaser Abu-Mostafa’s Caltech class “Learning From Data”
- Andrew’s Moore statistics and data mining tutorial
- Bill Howe’s “Introduction to Data Science” class on Coursera
- JHU’s Data Sciences classes on Coursera
- Courses offered at IACS at Harvard
- KDnuggets has a good list
General e-Science / Data Science resources:
- “The Fourth Paradigm” (free book download from Microsoft Research)
- Gregory Piatetsky-Shapiro’s KDNuggets
- Andy Pryke’s The Data Mine
- Fionn Murtagh’s Multivariate Data Analysis Software and Resources
Books on Data Mining and Machine Learning:
- “Data Mining – Concepts and Techniques”, J. Han & M. Kamber, MK
- “Neural Networks for Pattern Recognition”, C.M. Bishop, Oxford University Press
- “Pattern Recognition and Machine Learning”, C.M. Bishop
- “The Elements of Statistical Learning”, T. Hastie, R. Tibshirani J. Friedman, 2009, free download
- “Introduction to Information Retrieval”, Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze, 2008, Cambridge University Press
- “Gaussian Processes for Machine Learning”, Carl Edward Rasmussen and Christopher K. I. Williams, The MIT Press, 2006: free download
- “Tika in Action“, Mattmann & Zitting, Manning Publications, 2011, 256 pages.
Web Services and standalone applications:
- DAME (DAta Mining and Exploration)
- Weka (Data mining software in Java)
- Orange (open source data analysis and visualization)
- VOStat (Virtual Observatory statistics web services): Penn State version, VO India version
Programming Languages and DM Libraries:
- R: Official Manuals, Advanced R programming, R-Inferno (book download), R-studio
- Matlab: Official Documentation, SOM Toolbox, Bayesian Net Toolbox for Matlab, Netlab
- Other Libraries: Sci-kit Learn (Python), FANN, LibSVM
Do You Want to Learn About Learning Analytics? #dalmooc
Last week, I attended the UTA LINK Lab talk presented by Dragan Gasevic (@dgasevic) on learning analytics and research. This discussion shared all the digital traces and learning that can be collected and measured in our various learning environments, and questions how we are best doing some of these analytics within our institutions. Although we have a number of statistics, data, and information on our learners – how can we offer actionable insight, summative feedback, and information about learner progress. Our post-secondary institutions seem to want to only deal with the “R” word = Retention. Often institutions are looking to identify students at risk, provide information about learning success, and understand how to enhance learning – but how can we effectively use data when often times our metrics only focus on single outcomes?
Photo c/o the #dalmooc edX Course Site
Instead, it is the process and context that our education institutions need…
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