Data Science and Analytics
Thought Leaders

Course Description

The seminar explores

- Leading-edge trends in Data Science and Analytics at Silicon Valley and tech firms


The speakers will include executives, entrepreneurs, and researchers from leading firms.


The topics covered will include (a subset of):

- Data Analytics and Big Data

- Machine Learning and scalability

- Business Analytics including Online Marketing and Advertising, Financial   Services and Risk Analytics, Operational and Service Analytics

- Information Retrieval (Search)

- Information Extraction

- Social Networks and Social Media

- Healthcare Analytics

- Energy Analytics


The seminar will cover the following aspects:

- The types of problems being addressed in data science and analytics, the component methods and technologies being developed, and fruitful areas for research and entrepreneurial efforts

- This requires attendance and participation in the seminar series and is open to the broader student and faculty community


Prerequisites: None


Units: 2


Course Objectives

1. Learn about and understand landscape of Data Science and Analytics including a subset of

- The impact of Data Analytics on Business Analytics

- Data Analytics including Machine Learning, Data Mining, Text/Image/Video Mining and Analytics, Search - Information Retrieval (IR), Social Networks, Web Analytics, Online Advertising and Computational Marketing

- Implications of Big Data for Analytics


2. Develop

- A perspective on the business needs, state of the art research and technology, gaps, and emerging and novel mathematical and other techniques and approaches to address these

- A report summarizing this perspective, including research approaches, product/service or entrepreneurial opportunities in some detail

- Detail for research would include problem description, model formulation, and developed solution approaches, with validation on real data, where feasible

- Detail for product/service or entrepreneurial opportunities would include the details of a business case, business model, competitive analysis, positioning, market analysis (and study where feasible etc.)

- Weekly bullet point updates, monthly 5 page updates, and a final 20 (15-25) page report


Course Approach to accommodate heterogeneous class

Students can function in one of three modes:

- Prior MS/PhD level training in Machine Learning  (ML)and/or Data Mining (DM) or math/econ/IEOR analytics training

- Students with no prior  ML/DM background, concurrently studying i290: Data Mining and Analytics (several iSchool students+ others are in this category)

- Students with no prior background, and not concurrently studying ML/DM: These students can be provided some Black Box model/software usage training by Jimi Shanahan, to better appreciate the seminar talks, and to be able to work on the report.


Alternate Backgrounds, Goals for Seminar, and Fit

1. General training and exposure, and concurrent i290

2. Deeper machine learning or IEOR background

The goals might then either be more business or entrepreneurship oriented, or more research oriented reports/projects for the course.


Class Report/Project

1. Given a business/data analytics problem, decompose overall problem and solution into standard components, including unsupervised (e.g clustering, Principal Components), supervised (e.g. classification, prediction), optimization (business analytics), Reinforcement Learning etc.

2. Identify in a talk, or in reading a mathematical/technical or white paper, the analytics used to solve a given business  problem, which use the commodity algorithms, versus those which adapt or combine these commodity algorithms in a novel way, or create novel algorithms.

3. For a given industrial research project, or analytic product/service, or entrepreneurial concept/product/service, identify both an analytic approach or methodology gap, and a creative way in which the gap can be met. Also, identify the business model which will result in financial success.



10% class participation, very short bullet point highlights of talks, and interaction; 90% on weekly (15%), monthly (25%), and final submission of reports (40%), and PPT presentations (10%). Crisp, insightful, analytic reports will receive higher points.




ISchool 296A

Spring 2012


Lecture Time: Wednesdays 2-4 PM

Location: 205 South Hall


Instructors and Contact Information

Ram Akella,    Ray Larson,    Industrial Expert: James Shanahan


Telephone: 650-279-3078       Skype: ramakella1

Office: 422 Sutardja Dai Hall

Office Hours: By appointment in the 4-6 pm window (or variants) on Wednesday (or by phone).
Location: 205 South Hall; on
January 25, March 7, either SDH 422 or SH 210
(Sutardja Dai Hall 422 or South Hall 210), please check.