Introduction to business analytics syllabus
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Introduction to Business Analytics Fall 2013
Instructor: Stephen Mahar, Ph. D. Associate Professor of Business Analytics Villanova School of Business, Villanova University
Email: email@example.com _____________________________________________________________
Analytics has been defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Analytics is more than just analytical methodologies or techniques used in logical analysis. It is a process of transforming data into actions through analysis and insights in the context of organizational decision making and problem solving. Analytics includes a range of activities, including business intelligence, which is comprised of standard and ad hoc reports, queries and alerts; and quantitative methods, including statistical analysis, forecasting/ extrapolation, predictive modeling (such as data mining), optimization and simulation.
Selected readings and articles ? distributed in class and/or posted online
Excel and Excel add-ins will be the primary software used throughout the course. Both of the add-ins: XLMiner (available at xlminerdata-mining) and @Risk (trials.asp) are available as free 15 day trial versions.
1. To gain an understanding of how managers use business analytics to formulate and solve business problems and to support managerial decision making.
2. To become familiar with the processes needed to develop, report, and analyze business data.
3. To learn how to use and apply Excel and Excel add-ins to solve business problems.
This course stresses the factors that impact the performance of business decision makers and the data management and analysis methods that have value to them. This course includes lectures, presentations, and demonstrations that emphasize discussion and illustration of methods, as well as hands-on, practical exercises that provide both a sound base of learning and an opportunity to test and develop skill. The use of software supports the presentation of the material. Students complete assigned readings, group projects, and participate in exercises and discussions. Groups of about four students will form teams to work on the various presentations.
Student groups will be asked to complete a series of group projects that apply analytics principles and techniques to a business problem. Students are expected to make every effort to attend all classes.
Examination and Grading:
There will be a 1-hour written exam covering all material on Monday, October 14.
3 ECTS in MMI or MBWL
TENTATIVE COURSE OUTLINE*
I. Overview of Business Analytics
? Introduction to Analytics ? Davenport article - "Competing on Analytics" ? LaValle et al. article - "Analytics: The New Path to Value"
II. Visualization/ Data Issues
? Organization/sources of data ? Importance of data quality ? Dealing with missing or incomplete data ? Data Classification ? Davenport and Harris article - "The Dark Side of Customer Analytics"
III. Introduction to Data Mining
? Introduction to Data Mining ? Data Mining Process ? Data mining tool XLMiner (Excel add-in ? free 15 day trial available at
xlminer-data-mining) ? Loveman article ? "Diamonds in the Data Mine" ? Market Basket Analysis ? Shmueli Chapter 13 ? Classification and Regression Trees ? Shmueli Chapter 14
IV. Introduction to Decision Modeling
? Optimization Use of Excel to solve business problems: e.g. marketing mix, capital budgeting, portfolio optimization
? Decision Making under Uncertainty Simulation Introduction to @Risk (Excel add-in ? free 15-day trial available at trials.asp)
Types of problems: inventory management, capital investment analysis, market share estimation, sensitivity analysis
*The material is subject to change. All changes will be announced in class with ample notice.
8th Oct to 11th Oct from 9am-3pm On 14th Oct from 9am-1pm
Date 10/8 10/9
Topic T Intro to Business Analytics
Break Visualization & Data Issues
Break Ethical Issues Lab Work W Data Mining - Intro Data Mining - Market Basket
Break Data Mining - CART
Break Data Mining - CART wrapup Lab Work R Intro to Optimization, LP
Break Optimization - LP continued
Break Optimization - LP + Sensitivity Analysis
Break Lab Work F Intro to Simulation
Break Simulation Wrapup
Break Lab Work Exam Review M Advanced Applications Integer Programming (IP) at Eli Lilly
Break Ranking Sports Teams with NLP
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