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Data Science and Analytics |
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ISchool 296A Spring 2013 |
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Home Lecture Time: Wednesday, 2-4 pm Location: 205 South Hall Seminar Coordinators Industrial Expert: Jake Flomenberg Contact Information 422 SDH (Sutardja Dai Hall) Email: akella@ischool.berkeley.edu Cell: 650-279-3078 Skype: ramakella1 Office Hours By appointment in the 4-6 pm window (or variants) on Wednesday (or by phone) |
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Course Description (doc) The seminar explores leading-edge trends in Data Science and Analytics at Silicon Valley: Tech and VC firms, and Startups. The speakers will include executives, entrepreneurs, VCs, and researchers from leading firms.
The topics covered will include (a subset of): - Big Data: Landscape - Big Data Architecture, including Streaming, Real-time and CEP - Big Data Analytics Landscape - Data as a product and service, and Data Products - Distributed and Scalable Machine Learning and Statistics - Internet Analytics including: Online Advertising and Marketing: Targeting, Attribution, Exchanges Display Advertising Analytic Search analytics and Information extraction Social Media and Networks Analytics Mobile, Web, App, and IT Analytics Measurement and Audit Analytics - Machine Learning and scalability - Business Analytics including: Financial Services and Risk Analytics Operational and Service Analytics 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: 2-3
Course Objectives 1. Learn about and understand landscape of Data Science and Analytics including a subset of - Implications of Big Data for Analytics - The impact of Data Analytics on Business Analytics - Data Analytics including Machine Learning, Data Mining, Statistics, Text/Image/Video Mining and Analytics, Search - Information Retrieval, Social Networks, Web Analytics, Online Advertising and Computational Marketing
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 demonstrated capability to identify enterprise or consumer needs, and develop solutions to meet there
Expected Diversity of Participants Given the impact of Big Data Analytics, and the range of areas it spans, it is anticipated that participants will include 3 groups, with overlap: - Doctoral and Masters students with strong mathematical/statistical training, such as those in Machine Learning, Control, and Signal Processing (EECS), IEOR, Finance, Marketing, Operations, etc. - Masters or doctoral students who are familiar with software, e.g. CS - Masters of doctoral students who have a more general background, e.g. ISchool, MBA
Projects and Evaluation - Teams of 3 (or smaller if desired) - Research Paper or Project on Big Data Analytics - These can be fairly diverse, depending on the team - Could be (1) Hands on, working with data identified by student or faculty, and/or working with firm or VCs and faculty (2) More theoretical, large-scale data analysis (whether mathematical, technical, political, legal, social, etc.) (3) Or a combination - Reports should address a topic of interest, whether it be an exploration of a particular gap within the Big Data landscape, a business proposal, or something else, such as a research paper for a conference or journal. The goal is to delve deeply into a particular area as opposed to surveying a broad landscape. You should be sure to spend time understanding and articulating the problem/pain that you are addressing to ensure that you are addressing an issue that truly exists. - 2 Units: participation. 3 Units: As below - 10% class participation. Each team writes 4 reports, each summarizing a presentation; monthly progress report (30%): 1. Define problem and background, 1/28 2. Analyze problem, 3/31 3. Analyze problem in depth, 4/30, and final report 5/8 and 5/15 (50%), and PPT presentation (10%). Crisp, insightful, analytic reports will receive higher points.
More Detail on Project - A report summarizing perspective, including: Research approaches, and/or 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 business opportunities would include the details of a business case, business model, competitive analysis, positioning, market analysis (and study where feasible etc.) - Monthly 5 page updates, and a final 20 (15-25) page report and 15 PPT VC presentation (with 15 page Appendix); Team size of 3. - Weekly rotating team coverage of talks and detailed write up
Big Data Analytics Background Resources http://courses.ischool.berkeley.edu/i290-dma/s12/ https://blogs.ischool.berkeley.edu/i290-abdt-s12/author/hearst/ |