Today, the world is literally reinventing itself with Data and AI. However, learning a set of ‘related theories’ and being able to ‘make it work’ are not the same. And, in an area as important as Artificial Intellience, Data, and Blockchain; if we collectively cannot actually implement and create, then we reduce our competitive advantage, economic strength, and even national/global security.
The Data-X framework is designed to bridge the gap between theory and practice, by exposing students to state-of-the-art implementation techniques and mindsets.
Data-X frameworks, materials, code samples are all open source and available at the public site: data-x.blog
As a Sutardja Center Innovation Collider Activity, Data-X creates not only research and prototypes, but we in addition create teams of people that would not otherwise connect. These teams contain within them research insight, business models, new technology ventures, and other innovations.
Pre-Announcement: LinkQuest Project
Challenge Lab: Internet 3
Semesters offered: Fall 2017 (4 units)
INDENG 185 001
The web has come a long way—today, we have search engines that predict our searches, smart refrigerators ordering food, and virtual assistants giving us directions. But the convenience of user-centric technology has given birth to an evil twin—big data solutions that manipulate consumers, deceive voters and determine our credit ratings. In this class, you will be challenged to disrupt the biggest players on the Internet, from search engines to social networks. You will take on the role of a technology entrepreneur as you build real solutions and businesses that take on the biggest companies that abuse big data for their financial gain.
Applied Data Science with Venture Applications
Semesters offered: Fall & Spring (3 units)
INDENG 135 | INDENG 290-02
Data-X a technical course that teaches students to use foundational mathematical concepts and current computer science tools to create data-related applications and systems for real world problems. Computer science tools for this course include Python with NumPy, SciPy, pandas, SQL, NLTK, and TensorFlow. Math concepts include filters, prediction, classification, transforms, Bayesian, maximum likelihood, Markov state space, network graphs, and an introduction to deep learning. The tools will be presented in applications common to data flow organization of Collect, Combine, Store, Use, Analyze, and Visualize.