© Heikki Topi Computing and University Education in Analytics ACM Education Council San Francisco, CA November 2, 2013 Heikki Topi, Bentley University

McKinsey Global Institute Report Manyika et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Highly influential in bringing big data, big data analytics and analytics in general to the mainstream conversation Some highlights from the intro to this report: “40% projected growth in global data gathered vs. 5% growth in global IT spending” “140,000 – 190,000 more deep analytical talent positions and 1.5 million more data-savvy managers needed to take full advantage of big data in the U.S.”

McKinsey Global Institute Report  High level of hype but also useful recognition of an area (yet another) that fundamentally depends on computing

Analytics: Simple, but Useful Categorization (Watson, 2013) Descriptive analytics Reporting, OLAP, dashboard, scorecards, data visualization Predictive analytics Regression analysis, factor analysis, neural networks Prescriptive analytics Focuses on system performance optimization Forecasting and mathematical programming Watson, H. (2013) The Business Case for Analytics. BizEd Magazine, July.

Davenport, Barth, & Bean (SMR 2012): Data Scientist Data scientists “understand analytics, but they also are well versed in IT, often having advanced degrees in computer science, computational physics, or biology- or network-oriented social sciences.” “Their upgraded data management skills set – including programming, mathematical and statistical skills, as well as business acumen and the ability to communicate effectively with decision-makers – goes well beyond what was necessary for data analysts in the past”

Advanced Analytics: None of the Disciplinary Requirements Trivial Computer science Algorithms and data structures Machine learning Parallel and distributed computing HCI – data visualization Core technologies (e.g., Hadoop, Cassandra, HDFS, Hbase, Hive,etc.) Statistics Association rule learning Cluster analysis, classification, regression, factor analysis Neural networks Network analysis Information science Advanced natural language processing methods Sentiment analysis

Advanced Analytics: None of the Disciplinary Requirements Trivial Information Systems Organizational data and database management Data quality Requirements analysis – applying computing to a domain Impact analysis and forecasting Information Technology Implementing and managing increasingly complex infrastructure requirements

© Heikki Topi Source: data-analytics/big-data-analytics-masters-degrees-20/

Sample Degrees from the IW list Bentley University, McCallum Graduate School of Business: Master of Science in Business Analytics CMU, Heinz College of Public Policy and Information Systems: Master of Information Systems Management with a concentration in Business Intelligence and Data Analytics Columbia University, The Fu Foundation School of Engineering and Applied Science: Master of Science in Computer Science, concentration in Machine Learning DePaul University, College of Computing and Digital Media: Master of Science in Predictive Analytics Drexel University, LeBow College of Business: Master of Science in Business Analytics

Sample Degrees Harvard University, School of Engineering and Applied Sciences: Master of Science in Computational Science and Engineering Louisiana State University, Ourso College of Business: Master of Science in Analytics NYU, Stern School of Business: MBA, specialization in Business Analytics Stanford University, School of Engineering (CS): Master of Science in Computer Science, Specialization in Information Management and Analytics UC Berkeley, College of Engineering (EE and CS): Master of Engineering, concentration in Data Science and Systems

Sample Degrees University of Illinois at Urbana-Champaign, Graduate College, Department of Statistics: Master of Science in Statistics, Analytics concentration University of Ottawa, Telfer School of Management, School of IT and Engineering and Faculty of Law: Master in Electronic Business Technologies

Advanced Analytics: Multiple Disciplinary Stakeholders Mathematics Statistics Computer Science Information Systems Econometrics Domain expertise (e.g., various medical fields, marketing, manufacturing control, extraction of natural resources, finance, utilities, scientific disciplines)  Intensive competition for control over degree programs

Questions for Computing Education Are we capable of collaborating with all necessary disciplines? How do we manage a number of competing relationships and offer truly integrated degrees? How do we determine which discipline(s) take the leadership role in integrated programs? Computing for the 1.5 million “data-savvy managers”? (MGI)

More Questions for Computing Education How do we manage the competition for talented students? What is our role in analytics curriculum development? Yet another reason to push for more and more advanced computing for everybody?