Virtual School of Computational Science and Engineering: Data Intensive Summer School

July 8 – 10, 2013

Data Intensive Summer School

When: July 8 – 10, 2013, from 11am to 7pm Where: Discovery Research and Learning Center - DLR 221

The Data Intensive Summer School focuses on the skills needed to manage, process and gain insight from large amounts of data. It is targeted at researchers from the physical, biological, economic and social sciences that are beginning to drown in data. We will cover the nuts and bolts of data intensive computing, common tools and software, predictive analytics algorithms, data management and non-relational database models. Given the short duration of the summer school, the emphasis will be on providing a solid foundation that the attendees can use as a starting point for advanced topics of particular relevance to their work.


  • Experience working in a Linux environment
  • Familiarity with relational data base models
  • Examples and assignments will most likely use R, MATLAB and Weka. We do not require experience in these languages or tools, but you should already have an understand of basic programming concepts (loops, conditionals, functions, arrays, variables, scoping, etc.)


  • Robert Sinkovits, San Diego Supercomputer Center

Course Topics:

  • Nuts and bolts of data intensive computing
  • Computer hardware, storage devices and file systems
  • Cloud storage
  • Data compression
  • Networking and data movement
  • Data management
  • Digital libraries and archives
  • Data management plans
  • Access control, integrity and provenance
  • Introduction to R programming
  • Introduction to Weka Predictive analytics
  • Standard algorithms: k-mean clustering, decision trees, SVM
  • Over-fitting and trusting results
  • Dealing with missing data ETL (Extract, transfer and load)
  • The ETL life cycle
  • ETL tools: from scripts to commercial solutions
  • Non-relational databases
  • Brief refresher on relational model
  • Survey of non-relational models and technologies
  • Visualization Presentation of data for maximum insight
  • R and ggplot package NOTE: Students are required to provide their own laptops.

Originally posted: May 10, 2013