THE UNIVERSITY OF CONNECTICUT
SCHOOL OF BUSINESS
MBA PROGRAM
BIG DATA AND STRATEGIC MARKETING
(preliminary version dated 10/21/2015; subject to major changes until 1/18/2016)
Course Number:
Pre-requisite:
Term:
Classroom:
In-Class Hours:
Online Class Hours:
Course Ends On:
Instructor:
Office:
E-mail:
MKTG 5665
MKTG 5115 or (MKTG 5181 and MKTG 5182)
Spring 2016
TBA (Hartford Campus)
Feb 5 (5:00pm to 8:00pm), Feb 6 (9:00am to 5:00pm)
Feb 19 (5:00pm to 8:00pm), Feb 20 (9:00am to 5:00pm)
Mar 4 (5:00pm to 8:00pm), Mar 5 (9:00am to 5:00pm)
Jan 19 through Feb 4 (10 hours asynchronous)
March 21, 2016
Dr. Girish Punj
Professor of Marketing
BUSN 351 (Storrs Campus)
Girish.Punj@business.uconn.edu
Course Objectives:
Data mining is the process of finding “nuggets” of actionable information by “mining”
large quantities of data. The term is metaphorically related to gold mining. Other terms
that are used to describe data mining include knowledge extraction, data analytics (the
preferred term), and knowledge discovery in databases (KDD).
The course materials to be used in this course are intended to give you a hands-on
experience with various data analytic methodologies that can be used to analyze data
from a marketing perspective. Specific data analytic methodologies covered include
decision tree models, neural network models, clustering models, association rules, text
mining, and social network analysis.
Required Software:
IBM SPSS Modeler Premium
The course is part of the Academic Initiative and MAP program sponsored by IBM at
selected universities. Hence the required software will be made available to you by IBM
at no cost. Your personal use license will extend till December 31, 2016. Please note that
the software is enterprise-strength state-of-the art business analytics software. The
capabilities and functionalities of the software can be viewed at the following (and
associated links).
http://www-01.ibm.com/software/analytics/spss/products/modeler/index.html
Computer Requirements:
A laptop with a Windows operating system that can be brought to class is required for
taking the course. If you plan to use a corporate laptop, please ensure that you have been
granted “administrator rights” to it and any corporate firewall(s) have been disabled.
Otherwise, you will not be able to download the required software. Kindly review the
operating system requirements at the following link.
http://www-01.ibm.com/software/analytics/spss/products/modeler/requirements.html
Important Note for Mac users: The required software cannot be used on the Apple
platform.
Required Text:
IBM SPSS Modeler Cookbook (2013) by Keith McCormick, Dean Abbott, Meta S.
Brown, Tom Khabaza and Scott R. Mutchler – required for taking the course.
Please purchase it from your preferred online bookstore as it will not be available for
purchase from the UConn Stamford campus bookstore.
Recommended Texts:
Listed below are five recommended text books that would significantly add to your
learning experience in the course. Please consider purchasing them from your preferred
online bookstore.
Discovering Knowledge in Data (2004) by Daniel Larose – highly recommended
Data Mining Techniques (2011) by Michael J. A. Berry and Gordon S. Linoff – highly
recommended
Analytics at Work: Smarter Decisions Better Results (2010) by Thomas H. Davenport,
Jeanne G. Harris and Robert Morison from Harvard Business Press - recommended
Competing on Analytics (2007) by Thomas H. Davenport and Jeanne G. Harris from
Harvard Business School Press - recommended
Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know (2010) by
Mark Jeffery from Wiley – recommended
Readings on Business Applications
Readings on a wide range of topics that relate to the use of data analytic methodologies
by companies to exploit business opportunities (or solve business challenges) will be
used to supplement the above course materials. The readings cover 16 business
application areas (listed below) that have recently been reported in the business press.
The readings typically describe the best practices used by companies to extract business
or marketing intelligence from the data generated by its interactions with customers and
suppliers.
Consumer Profiling 1
Consumer Profiling 2
Advertising & Media Analytics
Retail Analytics
Big Data 1
Big Data 2
Financial Markets Applications
Artificial Intelligence Applications
Human-Computer Interaction
Human Resources Applications
Education Applications
Social Media Applications
Social Discovery Applications
Insurance Applications
Health Care Applications
Public Safety Applications
The specific readings that comprise a particular application area (e.g., Retail Analytics)
have been collated together and posted on HuskyCT as a single PDF file. Thus, you will
find 16 PDF files, one corresponding to each of the titles listed above, posted on
HuskyCT.
Is this Course Suitable for Me?
You will be expected to independently learn an unfamiliar software package and become
proficient in using it to analyze data for the purpose of extracting marketing intelligence.
The course will draw on concepts and theories from marketing and business intelligence.
Prior experience with a statistical software package (e.g. SPSS, R, SAS, etc.) or
proficiency in the use of Microsoft Excel is a definite plus.
Due to the empirical and analytical nature of the course, an affinity for “number
crunching” and data analysis is essential. Refreshing your knowledge of basic
mathematical and statistical procedures is recommended and can be done through a
“Math-Stat Refresher” posted on HuskyCT.
The course is not suitable for students who are (or plan to be) concurrently enrolled in
more than two MBA courses, because of the heavy workload. Neither is it suitable for
students whose only motivation for taking the course is the flex schedule or simply
satisfying a three credit requirement for a May 2016 graduation.
Evaluation:
The evaluation system is designed to encourage advance reading of all assigned material,
regular attendance in class, well prepared presentations and active participation in class
discussions. Timely completion of all course assignments (described in a subsequent
section) is expected.
For some of the course assignments you will be working in 2-person teams. Hence it is
important to select your teammate (i.e., working partner) with great care. The ideal
situation is to team-up with someone with whom you have had a successful working
relationship in another course(s) and who you can trust will complete their share of the
work in a dedicated and timely manner.
Attendance in all class sessions is required.
Team Formation:
For those of you who need assistance in selecting a teammate there is discussion board on
HuskyCT to facilitate the team formation process. Please use this discussion board to get
acquainted with others in a similar situation--or better still--seek out a teammate with a
background or set of skills that will complement your own expertise. Once you have
finalized your team, please send me an e-mail with the names and contact information of
both team members.
Evaluation, Testing and Grade Determination:
Your final grade in the course will be based on the components described below.
Business Application Presentations: You will be expected to make a presentation on one
of the 16 business applications (listed in the “Readings on Business Applications” section
of the syllabus). The class session during which a particular business application is
scheduled for presentation is listed in the “Schedule of Activities” section of the course
outline. The specific readings that comprise a particular application area have been
collated together and posted on HuskyCT as a single PDF file.
The 16 business application areas available for presentation will be assigned to students
on a first-come, first-served basis. Please sign-up for the business application area you
are interested in presenting on a HuskyCT discussion board that has been set up for this
purpose.
Homework Assignments: Your 2-person team will be expected to work on three
homework assignments during the course. The assignments have been designed to allow
you to achieve proficiency in the use of the data analytics methodologies covered in the
course. Additional details on each assignment will be provided during the class session
on which it is assigned. A printed copy of your completed assignment in WORD format
is to be handed-in in class on the assigned due date and time, followed by an electronic
copy sent later on the same day.
Corporate Proposal: You will be expected to individually prepare a proposal for
presentation in a corporate or job interview setting on how your data analytics knowledge
could be used by your company or organization to make better business decisions. The
corporate presentation should consist of 20-25 slides (with a suggested word density of
25-40 words per slide). An electronic copy of your corporate proposal in PPT format
(with the notes feature turned on) should be sent to me.
Class Participation: Due to the intensive nature of the course, you are required to attend
all classes, actively participate in all in-class activities and discussions, and contribute to
the discussion boards on HuskyCT.
The above activities will be weighted as follows in determining your course grade:
Business Application Presentation
Homework Assignments (3)
Corporate Proposal
Class Participation
15 points
50 points
25 points
10 points
Schedule of Activities:
DATE
TOPICS & CLASS ASSIGNMENTS
5 Feb (evening)
Introduction to the course
Introduction to the software
Model-building demonstration
6 Feb (morning)
Model-building demonstrations
Software tutorials
Methodologies: Decision Tree Models
6 Feb (afternoon)
Model-building demonstrations
Software tutorials
Methodologies: Neural Network Models
19 Feb (evening)
HW Assignment #1 due
Model-building demonstrations
Software tutorials
Methodologies: Clustering Models
Presentation: Consumer Profiling 1
Presentation: Consumer Profiling 2
20 Feb (morning)
Model-building demonstrations
Software tutorials
Presentation: Advertising & Media Analytics
Presentation: Retail Analytics
20 Feb (morning)
Homework Assignment #2 due
Model-building demonstrations
Software tutorials
Methodologies: Association Rules
4 Mar (evening)
Model-building demonstrations
Software tutorials
Presentation: Big Data 1
Presentation: Big Data 2
Presentation: Financial Markets Applications
5 Mar (morning)
Model-building demonstrations
Software tutorials
Presentation: Artificial Intelligence Applications
Presentation: Human-Computer Interaction
Presentation: Human Resources Applications
5 Mar (afternoon
Homework Assignment #3 due
Model-building demonstrations
Software tutorials
Methodologies: Text Mining and Social Network Analysis
Presentation: Education Applications
Presentation: Social Media Applications
Presentation: Social Discovery Applications
COURSE POLICIES:
Professional Integrity: Students in this course are expected to abide by the highest
standards of ethics and personal integrity, and to adhere to all university policies
regarding academic honesty. Academic misconduct can result in a failing grade for the
course. A student who knowingly assists another student in committing an act of
academic misconduct shall be equally accountable for the violation. Issues of academic
misconduct are covered in The Student Code, Part IV: Student Conduct Policies and in
Appendix A. (see http://community.uconn.edu/the-student-code/)
Special Needs: If you are a student who needs academic accommodations due to a
disability or for a religious observance, please contact me at your earliest convenience so
that appropriate accommodations can be made.
Conflict with Course Dates: You may not concurrently enroll in any other course for
which there is a conflict with the scheduled course dates for this course. You may not
enroll in the course if you are participating in a university event (e.g., a case competition)
or work assignment that requires you to miss multiple class sessions.
Course Communications: Important course announcements will be sent your official
UConn (i.e., PeopleSoft) e-mail address only. You are expected to comply with the
instructions in these announcements in a timely manner. Failure to do so could adversely
affect your performance in class.
Use of HuskyCT: HuskyCT will be used to post course materials (e.g., lecture slides)
and for the submission and grading of course assignments. You are expected to be
familiar with the use of HuskyCT for this purpose. If you are not, kindly acquire such
familiarity by reviewing the self-help materials available on HuskyCT.
Use of Laptops and Mobile Devices: Please bring a laptop to all in-class sessions as
you will be needing it to work on the individual assignments and the group project. The
use of mobile devices while class is in session is permitted provide such use is discrete
and not a distraction to students seated adjacent to you. If you have to attend to an
important text immediately, please exit the classroom to do so.
DETAILS ON THE BUSINESS APPLICATION PRESENTATIONS
The purpose of the business application presentations is to relate data analytics
methodologies similar to those covered in the course to the business opportunities and
challenges typically encountered by companies. The readings constituting a particular
application area have been selected from recent business publications and often represent
best practices in that particular application area.
The specific readings that comprise a particular application area have been collated
together and posted on HuskyCT as a single PDF file. Treat the 6-8 readings included in
a particular application area as a set. Consolidate their content while preparing your
presentation--instead of discussing them in isolation (i.e., one at a time). Draw attention
to the key facts and examples mentioned in the readings. Identify best practices which
you believe would be of interest to the class and suitable for class discussion.
Each student is responsible for making a presentation on an application area from among
the 16 application areas listed below. The class session during which a particular business
application is scheduled for presentation is listed in the “Schedule of Activities” section
of the course outline.
Consumer Profiling 1
Consumer Profiling 2
Advertising & Media Analytics
Retail Analytics
Big Data 1
Big Data 2
Financial Markets Applications
Artificial Intelligence Applications
Human-Computer Interaction
Human Resources Applications
Education Applications
Social Media Applications
Social Discovery Applications
Insurance Applications
Health Care Applications
Public Safety Applications
The typical business application presentation should be of 12-15 minutes in duration and
use 15-20 PowerPoint slides. In rare instances it may be necessary to do a limited amount
of online research (60 minutes maximum) to update the content of the readings or provide
additional background information. A printed copy of your business application
presentation is to be handed-in in class at the time of the presentation, followed by an
electronic copy sent later on the same day.
DETAILS ON THE CORPORATE PROPOSAL
The main purpose of the corporate proposal is to enable you to leverage the data analytic
skills you are expected to acquire in this course for career enhancement by moving up the
corporate ladder in your current (or future) company or organization. As described in the
2011 study from the McKinsey Global Institute (MGI), the 2009 study of CIO’s from
IBM, and the 2010 special report in the Economist, there is great demand for these skills
among Fortune 500 companies.
The recommended coverage for your proposal is given below. Feel free to add/delete
headings and create sub-headings as needed. Set up the corporate proposal as a PPT
presentation to be delivered in a corporate or job interview setting at a future date.
Attempt to tailor your presentation to suit a specific current business challenge or
opportunity being faced by your current (or future) company or organization. Be as
precise as you can about the type of data sources that could be used, applicable data
analytic methodologies, and the expected business outcomes.
Recommended Coverage
Description of datasets available for “mining” in terms of what type of information they
contain.
Identification of “data tombs” in the company or organization.
Data merging and consolidation issues.
What specific data analysis methodologies would be used? Why? and How?
What are the “nuggets” of actionable business intelligence that might be discovered?
How might such business intelligence be deployed by the company to gain a strategic
competitive advantage?
Written Report
The corporate presentation should consist of 20-25 slides (with a suggested word density
of 25-40 words per slide). An electronic copy of your corporate proposal in PPT format
(with the notes feature turned on) should be sent to me by March 20, 2016.
Once the course is over, I would encourage you to make such a presentation in a
corporate or job interview setting, and if you do, please let me know how it was received.
Several students who have previously taken an earlier version of this course have
reported career enhancing outcomes from such a presentation.