Advanced Analytics MSIE Concentration
Data Analytics involves the development and application of statistical and quantitative analysis methods and the construction of explanatory and predictive models to drive the decision making process. ISE’s advanced analytics program is focused on five core areas of research and course development: statistics and data analysis, operations research and decision sciences, computing and computational methods, and enterprise and business fundamentals. Specific relevant research areas pursued in ISE include statistical inference methods, clustering and classification, graphical models, and data-driven optimization.
Advanced Analytics (also referred to Data Analytics or simply Analytics) is a relatively young, multidisciplinary field that relates the application of engineering approaches and methods to the analysis and management of data-oriented engineering and business processes. Common problems involve collecting data, cleaning and analyzing data, building decision models based on data and making predictions and decisions.
The Department of ISE in the highly ranked College of Engineering at the University of Illinois, with its cutting-edge research by innovative faculty, diverse highly ranked departments, fantastic facilities and leading-edge laboratories, and national resources such as the NCSA and its recent National Data Center (NDC) initiative, is very well poised to undertake programs to address some of the pressing need for talent.
IE 528 Computing for Data Analytics
IE 529 Stats of Big Data and Clustering
IE 530 Optimization for Data Analytics
IE 531 Algorithms for Data Analytics
IE 532 Analysis of Network Data
IE 533 Big Graphs and Social Networks
GE 450 Decision Analysis I
GE 524 Data-Based Systems Modeling
GE 530 Multiattribute Decision Making
GE 550 Decision Analysis II
IE 400 Design & Analysis of Experiments
IE 410 Stochastic Processes and their Applications
IE 411 Optimization of Large Systems
IE 412 OR Models for Manufacturing Systems
IE 413 Simulation
IE 510 Applied Nonlinear Programming
IE 511 Integer Programming
IE 512 Network Analysis of Systems
IE 521 Convex Optimization
IE 598 CB Special Topics: Clustering and Approx Methods
IE 598 LM Special Topics: Optimization Methods for Large-Scale, Network-Based Systems
IE 598 XC Special Topics: Revenue Management
IE 598 NK Special Topics: Information Theory for Operations Research
Ubiquitous computing, pervasive communication technologies and wireless networks, the proliferation of network enabled devices and instruments with multi-media capabilities, and social networking paradigms and inexpensive global communication capabilities, have enabled large amounts of data to be generated, gathered, archived and distributed by organizations every day.
This data can provide important information about scientific and technological breakthroughs, customers, organizational performance, supply and demand infrastructures, and future trends. A new breed of engineering graduates is required to manage this data and convert them into useful information that can help shape the decisions companies and organizations make; these decisions influence also the strategic directions and policies.
Most Fortune 500 companies are hiring professionals with these skills. However such professionals are in very short supply.
The Institute of Operations Research and Management Science Society (INFORMS) in an article on getting started with Analytics, titled “NBN: Operations Research Saves Billions for the World’s Largest Broadband Project,” describes how the National Broadband Network (NBN) in Australia using Operations Research and Analytics saved an estimated $2.2B in avoided costs over the project’s lifecycle and emphasizes how analytics consistently delivers significant value – strategic to tactical, top-line to bottom-line – to the organizations and executives who use it.
INFORMS established the Analytics section in 2011 and renamed its Spring conference as the INFORMS Conference on Business Analytics and Operations Research, which attracted participants from many companies including IBM, Oracle, HP, Intel, Cisco, Best Buy, Deloitte Consulting, SAS, DELL, Walt Disney Parks and Resorts, McDonald’s Corporation, UPS, Boeing, P&G, Wal-Mart, and Bank of America. It is noteworthy that at the INFORMS Conference on The Business of Big Data, in June 2014, the keynotes by experts in Teradata and Accenture addressed big data in action and emphasized the role of analytics.
“Companies are increasingly turning to analytics to gain a competitive edge. As they do, they must resolve unique demands on their information technology, their structure, their processes, and their culture. Most critical, however, is the challenge posed by analytical talent, the people at all levels who help turn data into better decisions and better business results.” Accenture, Counting on Analytical Talent, March 2010
IDC survey and research in March 2013, published in December 2013 in the white paper “Using Big Data and Analytics as the Ticket to Strategic Relevance,” by Dan Vesset and Henry Morris states:
“Many best practices hinge on the ability of IT (information technology) leaders to articulate and market their core competency and value proposition to other internal stakeholders. By better communicating the value of analytics and IT’s role in realizing it, IT can help lines of business better assess the value of analytics. This helps sharpen the story of what analytics does for an organization and can help stimulate wider analytics deployment.”
McKinsey Global Institute’s Report in June 2011 on “Big Data: The Next Frontier for Innovation, Competition and Productivity” states:
“We estimate that the supply in the United States of deep analytical talent in 2008 was around 150,000 positions.” This total rises to about 300,000 with newer graduates and trends in course selections. However, in a big-data world, they expect demands for deep analytical talent could reach 440,000 to 490,000 positions in 2018, which implies a talent gap in this category alone of 140,000-190,000 positions. In short, the United States will need an additional supply of this class of talent of 50 to 60 percent.”
They say although this analysis was conducted in the United States, they believe that the shortage of deep analytical talent will be a global phenomenon.
In addition to this, they report that in the case of data-savvy managers and analysts in a big-data world, who need enough conceptual knowledge and quantitative skills to be able to frame and interpret analysis in an effective way. They estimate the shortfall by 2018 to be as high as 4 million positions with such skills.