A Course in Data Discovery and Predictive Analytics 16 Nov 2013 A - PDF document

A Course in Data Discovery and Predictive Analytics 16 Nov 2013  A definition of business analytics David M. Levine, Baruch College—CUNY A Course in  Broad categories of business analytics Kathryn A. Szabat, La Salle University Data Discovery What Are We (INFORMS 2010-2011) David F. Stephan, Two Bridges Instructional and Predictive Talking About?  Business analytics continues to become Technology Analytics increasingly important in business and therefore in business education analytics.davidlevinestatistics.com DSI MSMESB session, November 16, 2013 1 2  Addresses a topic of growing interest  Technology use should not hamper students  Introduces methods of problem description and ability to learn concepts decision-making not seen elsewhere in the Course business statistics curriculum  Emphasize application of methods (business Justification Guiding students are the audience)  Assumes a pre-requisite introductory course that and Starting Principles covers descriptive statistics, confidence intervals  Compare and contrast with decision-making and hypothesis testing, and simple linear using traditional methods where possible. Points regression  Capitalize on insights gained teaching related  Presents methods that have antecedents in subjects such as CIS and OR/MS introductory course 3 4 How David  Have sought to make statistics useful to students How Our Levine’s majoring in the functional areas of accounting, Teaching As a team, our varied backgrounds and Teaching economics/finance, management, and marketing Experience interests contribute to shaping our choices Experience  Have changed my focus as changes in Informs Us technology occurred over time Informs Us 5 6 2013 ‐ Levine ‐ Szabat ‐ Stephan ‐ DSI ‐ MEMESB ‐ slides.pdf 1

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Late 1980s/early Early 1980s – 1990s – Started Integrated to focus on  Enabled me to begin focusing on results rather software such software with than calculations as SAS, SPSS, enhanced user Saw how this would make statistical tools more  Helped me realize that students trained to use and Minitab interfaces that accessible to novice students, in particular. statistical programs would have increased replaced older, into opportunities in business programming- introductory oriented course interfaces 7 8 Early 1990s – Late 1990s – Integrated Pondered the Deming’s Total  Through consulting work, learned the use of  Realized Excel needed to be modified for Quality importance of organizational culture and the Microsoft classroom use difficulty of implementing change Management Excel, by then  Crossed paths and discovered shared interests philosophy and  This had limited long term impact as coverage with David Stephan prevalent in practices into the of this topic migrated to operations management business introductory schools course. 9 10 Overarching guiding principle:  Crossed path and discovered shared interests with Kathy Szabat. Statistics plays a role in problem solving and decision making. Current Day –  Realized this is our best opportunity to make Kathryn business statistics critical to the success of Reflected on Szabat’s majors in the functional areas Statistics – the methods that help transform data into analytics Experience useful information for decision makers  Believe this represents an opportunity to  Provides support for gut feeling, intuition, develop new majors in analytics and revise experience majors in business statistics (CIS, et. al. )  Provides opportunity to gain insight 11 12 2013 ‐ Levine ‐ Szabat ‐ Stephan ‐ DSI ‐ MEMESB ‐ slides.pdf 2

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Have consistently emphasized Have used  Without compromising understanding of logic Continual outreach to colleagues in different applications of technology of formulas departments within the school of business to statistics to better understand how statistics is used in the extensively in  Advocating the importance of “using a tool” to various functional areas functional the course generate results areas of business 13 14 Have Have increased, over increased, over time, focus on time, focus on problem- With attention to “formulating the problem” Someone has to tell the story at the end interpretation solving and and decision- communication making 15 16 Have recently been engaged in  Effort as a response to the technology and data- developing a driven changes in business today  Visualization has always been a theme in my new,  Outreach to practitioners to better understand David work and interests interdisciplinary “business analytics” as an emerging field Stephan’s  Context-based learning advocate academic  Developed an introductory presentation on Experience  Witnessed and taught about several generations department, business analytics to be used by all faculty in the of information technology Business introductory statistics course (as well as Systems and introductory IS and operations courses) Analytics 17 18 2013 ‐ Levine ‐ Szabat ‐ Stephan ‐ DSI ‐ MEMESB ‐ slides.pdf 3

A Course in Data Discovery and Predictive Analytics 16 Nov 2013  The story of the textbook that omitted the How things dBASE language  Do you remember the ALU and CU? Relational work versus Accept “Last Name:” to lastname  CP/M or DOS—Which is the better choice? Database Input “Grade:” to grade how to work  When is the last time someone asked you about @5,10 SAY Trim(lastname) + grade PICTURE 99.9 Debate with things the ASCII table?  Should database examples use one relation or two or more? 19 20  If you don’t teach { formulas, computations, fully explain  Simpler things can be used to teach operating Challenge: methods, widgets, whatever }, students will not principles and simulate more complex things understand “anything.” Finding the  Large-scale things can be imagined from small- Lessons from  How many helpful “black boxes” do you already use right level of scale things without explanation? the Debate abstraction to  The Microsoft Excel xls file format  Don’t fuss over technology choices— in the  Don’t try to reveal/decompose all complex systems teach. long-run, your choice will most likely not be  Can end up discussing parts that, at a later time, get use as an future-proof! integrated whole 21 22  “Volume, velocity, and variety” How to address these data characteristics often associated with analytics?  Best Topics to Teach New  Semi-subjective analysis of outputs (e.g., 3D Seeking Challenges to scatterplots or cluster plots)  Best Technology to Use Course “Bests” Address  Examining patterns before testing hypotheses  Best Context to Deliver Instruction  Need to determine when to assign causality (to relationships) as part of the analysis versus testing a hypothesized causality 23 24 2013 ‐ Levine ‐ Szabat ‐ Stephan ‐ DSI ‐ MEMESB ‐ slides.pdf 4

A Course in Data Discovery and Predictive Analytics 16 Nov 2013  Descriptive analytics/data discovery: most likely  Experience teaches us not to be overly to be seen, builds on and extends introductory concerned about choice! descriptive methods. Can be used to raise and  No one program, application, or package is best “Best” “simulate” volume and velocity issues. “Best” Topics in 2013 Technology to  Predictive not prescriptive analytics. The latter to Teach  Best technology combines most accessible with Use brings into play management insight, judgment, what bests illustrates the concept and wisdom. (Predictive combines traditional  Our choice: mix of Microsoft Excel, Tableau statistical analysis with data mining, as defined Public, and JMP earlier.) 25 26  A broad case that represents an enterprise of “Best” Context Course suitable complexity, yet one that can be to Deliver understandable on a casual level Description Instruction In-Depth  Our choice: a theme park with several different parts (“lands”) and an integrated resort hotel 27 28  Introduction (2)  Descriptive Analytics (2)  Preparing for Predictive Analytics (1)  How We Got Here: Evolutionary changes that Topic List have led to more widespread usage of analytics  Multiple regression including residual analysis, dummy variables, interaction terms, and (with  How analytics can change the data analysis and Introduction (2 influence analysis (1.5-2) decision-making processes suggested weeks)  Logistic regression (1) weeks)  Basic vocabulary and taxonomy of analytics  Multiple regression model building including  Technology requirements and orientation transformations, collinearity, stepwise regression, and best subsets (1.5-2)  Predictive Analytics (4-5) 29 30 2013 ‐ Levine ‐ Szabat ‐ Stephan ‐ DSI ‐ MEMESB ‐ slides.pdf 5