Applied Data Science with Venture Applications: Data-X
INDENG 135/290 (3 Units)
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 areas as important as Artificial Intelligence, Data Science, and Machine Learning, if we collectively cannot actually implement and create, then we'll 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 as well as academia and industry, by exposing students to state-of-the-art implementation techniques and mindsets.
This highly-applied course surveys a variety of key concepts and tools that are useful for designing and building data science, AI, and Machine Learning applications and systems. The course introduces modern, open source computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python libraries like NumPy for math array functions, Pandas for manipulation of tables, Scikit-learn for machine learning modeling, Tensorflow and Keras for deep learning, and many other topics related to NLP, Neural Networks, Recommender Systems etc.
Students from all majors, both undergraduate and graduate are welcome; however, due to the technical nature of this class, students must have the ability to write code in Python, and have taken a probability or statistics course.
Data-X has an undergraduate section INDENG 135 and a graduate section INDENG 290-005, which is mainly for students in the IEOR MEng program. The maximum enrollment across the two sections is set at 150. If the graduate section does not fill up by the start of the semester, remaining seats will be opened up in 135. If you have questions about enrollment, contact SCET Academic Program Manager, Michelle Lee at firstname.lastname@example.org.
View the Data-X website for more information including a syllabus, class material and previous projects.
Ikhlaq Sidhu, Chief Scientist, and Faculty Director of UC Berkeley’s Sutardja Center for Entrepreneurship & Technology. He is the author of the book "Innovation Engineering" and he holds 75 patents in internet communication technologies. Dr. Sidhu developed the Entrepreneurship & Technology area at the #1 public university in the world, the Berkeley Method of Entrepreneurship (for new ventures), and the Berkeley Method of Innovation Leadership (for technical leaders). In 2018, Sidhu received the IEEE Major Education Innovation Award. He received Berkeley IEOR Emerging Area Professor Award in 2009 at UC Berkeley. In 1999, he received 3Com Corporation’s Inventor of the Year Award. He serves on many advisory boards. He received his bachelor’s degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign, and his masters’ degree and doctorate in Electrical Engineering from Northwestern University.
Arash Nourian is a lecturer at UC Berkeley teaching and doing research on state-of-the-art machine learning and data science. He is also VP of AI/Chief Scientist at FICO leading the Artificial Intelligence (AI) efforts in decision management suits (DMS). His team of engineers and machine learners drive the cutting edge algorithms and large-scale AI-based analytic solutions. His goal is to build theory and tools that make AI more accessible, responsible, robust, and explainable. As an accomplished computer scientist, he has held several roles both in industry and academia. He was one of the inaugural faculty members for the Master of Information and Data Science program at UC Berkeley. Previously, he did his post-doctoral studies at UC Berkeley and MIT in Computer Science and Information Management. Arash has a masters and PhD degree in Computer Science from McGill University and UC Berkeley.
Ed is a startup advisor and a lecturer in Technology Entrepreneurship at UC Berkeley. As a startup executive he’s had two successful exits. The first was a $380 million acquisition within two years of joining a 5 person company, the second (Responsys) grew to IPO & then acquisition by Oracle for $1.5 billion. At Responsys/Oracle Marketing Cloud he was Chief Customer Officer managing a $500M enterprise SaaS business. Previously, Ed was a venture capitalist with Internet Capital Group and a consultant at McKinsey and Company. A former Fulbright Scholar to Australia, Ed received his bachelor’s degree in Mechanical Engineering from Drexel University, and his M.S. in Aerospace Engineering and Ph.D. in Mechanical Engineering and from UCLA.