Data-X – An Applied Research Collaboration Project

PI: Ikhlaq Sidhu, IEOR, UC Berkeley (contact)

Data-X is an integrated research and educational project about data and its applications.

Applied Data Science with Venture Applications is a course at UC Berkeley which draws from the Data-X resources.

Today, the world is literally reinventing itself with Data and AI.  However, neither leading companies nor the world’s top students have the complete knowledge set or access to the full networks they need to participate in this newly developing world.  The Data-X project designed to fix this problem.

The approach is to bring together students, faculty, new ventures, and large firms so they can learn from each other in a manner that is both technically deep and yet broad in an application sense.  Each of these segments provides an important part of the understanding of data problems to the other.   And as a result, we have the opportunity to develop a large-scale, holistic, data-related skill base. One that is capable of creating next generation of new data applications.

The Data-X Project also provides the resources, networks, and code samples needed for education in the area of data and computing applications.

The Complimentary Course:

At UC Berkeley, we offer the related course as Applied Data Science with Venture Applications (3 units).

Applied Data Science with Venture Applications
Fall 2017 course information:

  • Undergrad: INDENG 135, Class Number 46371
  • Grad Section: INDENG 290-02 Class# 46662
  • Location: TBD
  • Prerequisite: Interested students should have working knowledge of Python in advance of the class, and also should have completed a fundamental probability or statistics course.

While the course offers instruction on tools and methods for data, the Data-X project includes new problems, industry, social, and venture perspectives on utilizing data for decision making at scale.

Suggestions for Data-X project may be submitted here:

Data-X Breadth Perspectives:
Ref B01: Why you’re not getting value from your data science
Ref B02: http://scet.berkeley.edu/data-strategy-working-hard-enough/