SOCR Big Data & Predictive Analytics

SOCR Big Data & Predictive Analytics


The Statistics Online Computational Resource (SOCR) is an online collection of educational materials and tools for the use of advanced methods in probability and statistics. This faculty research team will develop an enhanced analysis and visualization toolbox for the SOCR with an emphasis on “Big Data”: very large datasets that are difficult to analyze and interpret in meaningful ways with basic probability/statistical methods. The toolbox will be designed to run in a web browser and provide an enhanced visual means to present, and interpret Big Data. The creation of the toolbox to enhance SOCR infrastructure will allow many more researchers (including students) to learn about, appreciate, and apply complex analytics to their work, making Big Data much easier to turn into “big results”.

More Information

2018 Terms and Conditions

Students who successfully match to this faculty research team will be required to sign the following document in January 2018:

Student IP Agreement for Faculty Research Teams

How to Apply

Project Features

  • Skill level All levels
  • Students 5-17 Students
  • Likely Majors CS, , MATH, SI, STATS
  • Course Substitutions MIDAS, CS
  • IP & NDA Required? Yes
  • Summer Opportunity Summer Funding Application
  • Programming Subteam: Charts and Modeler (4 students)

    Preferred Skills: UI/UX design, HTML5, JavaScript, Adobe Illustrator, Canvas

    • Likely Majors: CSE/CS-LSA, Information (SI)
  • Programming Subteam: Framework (4 students)

    Preferred Skills: UI/UX design, JavaScript, HTML5, WebGL

    • Likely Majors: CSE/CS-LSA, Information (SI)
  • Programming Subteam: Data Wrangler (4 students)

    Preferred Skills: back-end server experience, JavaScript, HTML5

    • Likely Majors: CSE/CS-LSA, Information (SI)
  • Stats Subteam: Tools (3 students)

    Preferred Skills: HTML5, R, statistical modeling, algorithm construction

    • Likely Majors: MATH, STAT, CSE/CS-LSA
  • Apprentice Researcher (2 Students)

    Requirements: interest in project material, willingness to develop skills. OPEN TO FRESHMEN AND SOPHOMORES ONLY.

    • Likely Majors: Any
  • Programming Capstone Team

    (CSE/CS-LSA Only: requires EECS 498 enrollment for Capstone/MDE. Prereq: EECS 281)

Faculty Sponsor: Ivo D. DinovMax Shtein
Associate Professor of Nursing, Medicine; Director, SOCR; Associate Director for Education and Training, MIDAS
Dr. Dinov is the director of the Statistics Online Computational Resource (SOCR) and associate director for education and training of the Michigan Institute for Data Science (MIDAS). He develops advanced mathematical models for representation, scientific computing, statistical analysis and interactive visualization of multi-dimensional, multimodal and informatics biomedical data (Big Data). With expertise in human brain imaging, statistical computing and high-throughput distributed data processing, Dr. Dinov approaches biomedical and health science research from the perspective of Big Data applications in nursing informatics, multimodal biomedical image analysis, and distributed genomics computing.