Download Syllabus

BUS 212f (2) ANALYZING BIG DATA II
Spring 2015—Tuesdays 6:30–9:20 pm
Sachar 116 (International Hall)
Prof. Robert Carver
781-775-5493 (mobile)
[email protected]
Office: Sachar 1B (far end of computer cluster)
Hours: Tuesdays, 4:00 – 5:30 and by appointment
TAs:
Pratibha Harrison and Shourya Veerganti
Overview
This is a two credit module that is a continuation of BUS 211f. This module
provides theoretical and hands-on instruction in three major elements of Big
Data analytics: management-oriented visualizations, data mining, and
predictive modeling. Through the use of widely adopted software tools,
students will build models and execute analyses to address current needs of
selected Brandeis administrative offices as well as solve problems presented in
cases. Assignments and classroom time will be devoted both to analysis of
current developments in business analytics and to gaining experience with
current tools.
Required Readings
Provost, Foster & Fawcett, Tom. Data Science for Business: What You Need to
Know about Data Mining and Data-Analytic Thinking. (2013, Sebastopol, CA:
O’Reilly Media) 978-1449361327. Purchase at Bookstore or on-line.
There is a required on-line course pack available for purchase at the Harvard
Business Publishing website. A direct link is available on LATTE . See last
page of Syllabus for course pack contents.
Other readings as posted on LATTE site.
Recommended
Readings
Berry, M. and Linoff, G. Data Mining Techniques for Marketing, Sales, and
Customer Relationship Management. 3rd ed. (2011, Wiley) available on-line
through LTS. Ebook ISBN9781118087459.
Hastie, T., Tibshirani, R. and Friedman, J.H. The Elements of Statistical Learning:
Data Mining, Inference, and Prediction. (2001, Springer). Available in library
main stacks; pdf of new edition available for download at http://wwwstat.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
Prerequisites
BUS 211f or permission of instructor.
Learning Goals and
Objectives
Upon successful completion of this module, students will:

Understand the challenges of performing a business needs assessment to
determine how analytics and visual displays can provide business value

Be able to use training, validation, and test datasets to carry out data mining
analyses

Use common techniques such as multiple regression, partition trees, k-
BUS 212 f(2) Spring 2015
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means clustering to develop predictive models
Course Approach

Apply best practices of predictive modeling to real and realistic business
problems

Design informational graphics and displays grounded in concepts of
business needs and principles of human cognitive processes
Analysis of massive, real-time data is rapidly gaining prominence in
numerous industries, with applications ranging from fraud detection to
consumer behavior. As in the predecessor course (BUS 211f), BUS212f uses
theory, cases, and hands-on analysis to approach course topics. In six short
weeks, we can only dive so deep; we aim for depth in a carefully selected list of
topics rather than breadth. Students should expect to grapple with complex
software-based analyses that do not lend themselves to quick, easy solutions.
Communications
We’ll make regular use of LATTE. All lecture notes, handouts, assignments,
and supporting materials will be available via LATTE, and any late-breaking
news will reach you via email. Please check your Brandeis email and the LATTE
site regularly to keep apprised of important course-related announcements.
Other Course
Technology
All of the software we will use in this course can be accessed on the public
computer clusters at IBS and/or on your personal laptops. If you do use a
laptop, the class schedule below indicates dates when it will be useful to have it
with you.
As in BUS 211f, we will make use of proprietary and public-use databases
accessible through the World Wide Web. We’ll continue to use some of the tools
we adopted in that course as well as R for most of our analysis.

R: R is a free software environment for statistical computing and graphics,
and is widely used by both academia and industry. The advantage of the R
software is that it can work on both Windows and Mac-OS. It is ranked no. 1
in the KDnuggets 2013 poll on top languages for analytics, data mining, and
data science. RStudio is a user friendly environment for R that has become
popular.
R Software: http://www.r-project.org/index.html.
RStudio: http://www.rstudio.com/products/RStudio/#Desk
Student Classroom
Contributions
Class participation is important in this course both as a means of
developing understanding and as an indicator of student progress. Participation
can take many forms, and each student is expected to contribute actively, freely,
and effectively to the classroom experience by raising questions, demonstrating
preparedness and proficiency in the analysis of problems and cases, and
explaining the implications of particular analyses in context. Homework-based
discussion and presentations are an important part of participation. To this
end, regular class attendance is required, and students should use name
cards. We meet only six times, so absence can become a serious problem. Even
if you must arrive late or leave early, be here.
With assistance from the TA, I will evaluate the quality of your
contributions in class each evening, as well as the quality of your contributions
via email, LATTE discussion, etc. These will all be factored together in
determining your ultimate Contributions grade (see below). In general, absence
BUS 212 f(2) Spring 2015
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from class reduces your contribution grade.
Written
Assignments and
Projects
Students will complete five analytic assignments during the course. Three
of these will be brief analyses, requiring both computer modeling and writing.
These may be completed with one or two partners, and each student should
expect to briefly discuss one of their work products in class.
Two other written assignments will be two phases of a single project
requiring more significant time and analysis. The project assignments will be
prepared in teams of four students, and will include written and computerbased elements. Owing to the size of the class, students will have only limited
opportunities to present parts of their projects orally in the course.
All assignments should be submitted via LATTE upload prior to the start of
class. Papers should be professional in appearance and use clear, grammatically
correct business English. Analytical work (graphs, tables, and other output)
should be incorporated seamlessly into the written document, showing readers
exactly and only what you want them to see.
Evaluation
Your final grade in the course will be computed using these weights:
Contributions to Class Discussions
Brief analyses (3)
Projects (2 parts)
TOTAL
20%
40%
40%
please note!
100%
Academic Integrity
You are expected to follow the University’s policies on academic integrity
(see http://www.brandeis.edu/studentaffairs/srcs/ai/index.html). Instances
of alleged dishonesty will be forwarded to the Office of Campus Life for possible
referral to the Student Judicial System. Potential sanctions include failure in the
course and suspension from the University.
Disabilities
If you are a student with a documented disability on record at Brandeis
and wish to have a reasonable accommodation made for you in this class,
please see me immediately.
Study Groups
Working with one or two partners is an excellent way to gain understanding of
this subject. I encourage small groups to work on assignments, with a few
caveats:



Be sure that you are neither carrying nor being carried by the group; each
member of the group is entitled to learn and expected to contribute.
Except for the group project, each student is responsible for turning in
original memos and problem sets.
Each group member retains the right to “go it alone.” Joining a group is not
a marriage. Similarly, teams are encouraged to dismiss underperforming
members.
BUS 212 f(2) Spring 2015
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Course Outline
Note: for each session, you should complete the assigned reading before coming to class. See list of
deliverables on next page; detailed assignments will be distributed in class each week, and all
assignments and handouts will also be available on our LATTE site. The abbreviation “P&F” refers
to the Provost and Fawcett book.
Session
Date
Topics and Readings
Deliverable Due
by class time
Starting at the End: Visualizations to Support Business
Intelligence
READINGS: Russom, Big Data Analytics (2013, on LATTE)
P&F, Chapter 1 & 2
Watson, “All about Analytics”
Session 1
March 10
a.
b.
c.
d.
Course introduction and objectives
Relationship of Business knowledge and Big Data Analytics
Data Mining Process (overview)
Introduce/ Review R & R Studio
(none)
Laptops helpful
Decision Trees & Logistic Regression
READINGS: P&F, Chap 3
Few, Dashboard Design
Session 2
March 17
CASE READING: A Game of Two Halves: In-Play Betting in Football
a.
b.
c.
Analysis I
(R data analysis)
Supervised Segmentation
Theory: Decision trees and concepts of Logistic Regression
(simple/ multinomial logistic)
Application: Game of Two Halves
Classification Models and Performance
READINGS: P&F, Chaps 4–5
CASE READING: Predicting Customer Churn at QWE
Session 3
March 24
a.
b.
c.
Classification models with regression
Training & Validation
Confusion Matrix to assess model performance
Laptops helpful
Analysis 2
(Game of Two
Halves)
BUS 212 f(2) Spring 2015
Session
Date
5
Topics and Readings
Deliverable Due
by class time
Association Rules
READINGS: P&F, Chaps 6–8
Market Basket Analysis (on LATTE)
Session 4
March 31
a.
b.
Project 1 Debriefing
Unsupervised Data Mining: Association Rules/Market Basket
Analysis
Project 1
(Churn at QWE)
Laptops helpful
Passover
Break
April 7
NO CLASS MEETING THIS WEEK
Text Mining
READINGS: P&F, Chap 10
Tsur et al. A Great Catchy Name
CASE READING: Qantas Airlines Twitter case
Session 5
April 14
a.
b.
c.
Text Mining basics
Word clouds in R
Sentiment analysis with Twitter data
Laptops helpful
Review, Summary & Project
READINGS: P&F, Chaps 11 & 12
Project 2 instructions
Session 6
April 21




Debrief Text Mining assignment
More on the Data Analytic Mindset
Other application areas and challenges
Developing models with Business Value
Analysis 3
(Qantas Twitter
case)
Brief project-2
discussion
No Class Session this week
Tuesday

April 29

Final project due before this date, with revisions & modifications in
response to Session 6 discussions.
Graduating students are encouraged to submit early 
Project 2
Brief Description of Assignments (complete assignment details to be distributed in class):
Analysis 1
Introduction to Modeling with R and R Studio
Analysis 2
Build a model to support In-Game Betting in Football (soccer)
Analysis 3
Qantas Airlines: Twitter Nosedive
Project 1
Customer Churn at QWE
Project 2
As assigned in Class (TBD)
BUS 212 f(2) Spring 2015
Supplementary Readings and Cases (chronologically during course): Those in bold-face are in the
Harvard Business Publishing on-line course.
Russom P., (2011) “Big Data Analytics”, TDWI Best Practices Report
Watson, H. (2013) “All about Analytics” International Journal of Business Intelligence Research,
January-March, Vol. 4, No. 1.
Few, S. (2005). “Dashboard Design: Beyond Meters, Gauges, and Traffic Lights” Business Intelligence
Journal
Kumar, U., Sandeep, V. and Satyabala (2013) “A Game of Two Halves: In-Play Betting in
Football” (IMB-401). Indian Institute of Management–Bangalore.
Ovchinnikov, A. (2013) “Predicting Customer Churn at QWE, Inc.” (UV6694) Darden
Business Publishing
Bigus, P (2012) “Qantas Airlines: Twitter Nosedive.” Ivy Publishing
Tsur, O., Davidov, D., and Rappoport, A. (2010). “A Great Catchy Name: Semi-Supervised
Recognition of Sarcastic Sentences in Online Product Reviews”. Proceedings of the Fourth
International AAAI Conference on Weblogs and Social Media.
Rev. 03/2015
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