Thought Leaders in Data Science and Analytics - Big Data Analytics Ram Akella Cell Skype ID: ramakella1 621C SDH (Sutardja Dai Hall) (Under development) See for now Monday, January 25, 2016

Introduction Technology cycles – Analytics – Architecture/Infrastructure – Interaction A possible cut – Enterprise Analytics: Enterprise databases (DB)and Business Intelligence (BI) – Web Analytics Leading to Hadoop, Spark/Shark, Streaming + Analytics – Internet of Things Continuous sensing and proactive response What is new and different about it?

Today’s Seminar(s) Computational Advertising: Dr. Jimi Shanahan – Web Analytics: NativeX/Adobe + Plus more Entrepreneurial Data Analytics: Dr. Sudhakar Muddu – Developing Data Analytics Products and/or firms – IoT, Security, Big Data – Splunk + Plus more

Next weeks Seminar(s) Web Analytics and Social Media: Eric Rasmussen – Groupon IoT and Enterprise Analytics: Jose Abelenda – Lyft - Challenge problems follow

Data Science and Analytics: What is it? Components – Data collection, storage, and basic processing Architecture and Infrastructure – Analytics – Domain – Business Needs To solve real Big Data problems, need expertise in some or all of these areas Need to form teams!

Seminar and Course Structure Set of broad ranging industry talks – Provide perspectives on Domain and business needs Infrastructure needs Analytics needs State of the art Basic – 2 units: Class participation + 2 team reports on seminar themes (due 3/14 and 5/2) Advanced (subject to review of CVs and approval) – 2 or 3 units: Potential projects, ideas, mentoring (including industry executives, VCs) and possible data (Reports due 2/8, 3/7, 4/11, 5/2) -Either i) Startup product development -OR - ii) Industry problem solution

Projects and Participation These are key to learning Forming teams is critical Need Analytics, infrastructure, business, domain We can help – Faculty – Staff – Other students – Industry executives, managers, researchers and personnel – VCs Requires submission of CV, proposal

Background Basic – An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani) -R or equivalent -Data Mining, linear algebra, statistics, or equivalent -Additional (specialized): Field Experiments (Gerber, Green) -Background courses on next slide Advanced: To discuss - Coursera courses, EDX courses - Campus courses

Background Courses Big Data Analytics Background Resources s12/author/hearst/ s12/author/hearst/ spring14/ spring14/

Action Sign up sheet Set up teams Provide CVs Start determining data sets and projects Meeting times, including Skype (beyond class times) Set up boot camp times for Infrastructure and Machine Learning/Data Mining Use Piazza!

Meeting Times/Office hours Mon: 11-1 pm, (backup: 4/5 -5/6 pm), By appointment Tue/Th: By appointment Skype/tel, in addition to in-person meetings

Course Expectations This is about addressing the unstructured real world and Silicon Valley NOT a structured, course, with an organized, linear flow You are expected to already know or learn data mining and machine learning – Bootcamp for those who need assistance Seminars to provide industry context – Again, thematic, but no evident linear flow structure – executive schedules! Industry and VC mentors for – Entrepreneurial project on data analytics product development