About Data-X



Archive Site: This site is an archive of the older site developed in 2016

Data-X: A Framework for Digital Transformation

McKinsey Dec 2017: A new survey finds that many companies are launching data-focused businesses. But few have achieved significant financial impact, which requires the right combination of strategy, culture, and organization. (link)

Ikhlaq Sidhu, IEOR, UC Berkeley (contact).

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.

Curriculum, Talks, and Programs:

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


Applied Data Science with Venture Applications
Spring course information:

  • Undergrad: INDENG 135, Class Number 41878
  • Grad Section: INDENG 290 LEC 003, 41879
  • Location: TuTh 3:30P-4:59P | 277 Cory
  • Prerequisite: Interested students should have a 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.

What People Say After Experiencing Data-X:

  • Great awesome and amazing tools for data analysis, ML, and processes in general!
  • I think this class is so awesome because it teaches the tools and concepts that are most commonly used in workplace teams that are involved with data science
    and applied machine learning. The vast majority of teams that I’ve applied to within the past year use the tools taught in this class. When I arrived at my
    data science internship this summer, I already knew how to use most of my team’s stack. 
  • 135 has to be my favorite of the ML classes at Berkeley. It covers A TON of content.  The course is very application-focused but takes some time to explain the general idea behind concepts. 
  • I feel like I’m really learning how powerful data science tools can be. When we were brainstorming project ideas, I didn’t think any of the ideas were feasible. However, with each week, I’m learning how pre existing libraries and tools can be easily used and combined to create really powerful products.
  • Lots of practical tools and sample code.
  • I am gaining an understanding of how to use data science libraries in python
  • The algorithms and toolkits I’m learning feel very valuable and tangible given the breakout sessions and direct datasets we work on.

For Executives and Techncal Leaders:
Recent Workshops, Master Class, or other short programs related to Data-X:

  • “Data-X Masterclass for Technical Leaders”, Hong Kong, January 25, 2018
  • “Research Trends in AI with Venture Applications”, at GMIC, Hong Kong, October 2017
  • “Turning Data into Dollars”, CIO Connect, Hong Kong, September 11, 2017
  • “Data at Scale, AI, and Business Models”, Jardines CIO Conference, July 2017
  •  “How Data and AI Affect Business, Government, and Society”, July 2017

Data-X Breadth Perspectives:

Ref B01: Why you’re not getting value from your data science
Ref B02: Fueling growth through data monetization | McKinsey, (original)
Ref B03: http://scet.berkeley.edu/data-strategy-working-hard-enough