BUS 212F ANALYZING BIG DATA II
Fall 2014—Tuesday 6:30–9:20 pm
Location SACHAR 116 (INTERNATIONAL HALL)
Prof. Bharatendra Rai
brai@brandeis.edu
Office: Sachar 1C
Hours: Tuesday, 4:15 – 5:00pm and by appointment
TA: TBD
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) 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.
BUS 212 f(2) Spring 2014
Learning Goals and
Objectives
Course Approach
2
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, kmeans clustering to develop predictive models

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.

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
BUS 212 f(2) Spring 2014
Student Classroom
Contributions
3
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
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 “Projects” requiring more
significant time and analysis. The projects will be prepared in teams of four
students, and will include written and computer-based elements. Owing to the
size of the class this term, 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
10%
45%
45%
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.
BUS 212 f(2) Spring 2014
4
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:
Study Groups



Be sure that you are neither carrying nor being carried by the group; each
member of the group is entitled to learn.
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.
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
October 28
a.
b.
c.
d.
Course introduction and objectives
Relationship of Business knowledge and Big Data Analytics
Data Mining Process (overview)
Introduce R & R Studio
(none)
Laptops helpful
Decision Trees & Logistic Regression
READINGS: P&F, Chap 3
Few, Dashboard Design
Session 2
November
4
CASE READING: A Game of Two Halves: In-Play Betting in Football
a.
b.
c.
Supervised Segmentation
Theory: Decision trees and concepts of Logistic Regression
(simple/ multinomial logistic)
Application: Game of Two Halves
Analysis I
BUS 212 f(2) Spring 2014
Session
Date
5
Topics and Readings
Deliverable Due
by class time
Classification Models and Performance
READINGS: P&F, Chaps 4–5
Session 3
November
11
a.
b.
c.
Classification models with regression
Training & Validation
Confusion Matrix to assess model performance
Analysis 2
(Game of Two
Halves)
Laptops helpful
Association Rules
READINGS: P&F, Chaps 6–8
Market Basket Analysis (on LATTE)
Session 4
November
18
a.
b.
Project-1 Briefing
Unsupervised Data Mining: Association Rules/Market Basket
Analysis
Project 1
Laptops helpful
Text Mining
READINGS: P&F, Chaps 10
CASE READING: Qantas Airlines Twitter case
Session 5
November
25
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
Session 6
December
2
Monday
December
8




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
presentations &
discussions
No Class Session this week


Final project due before this date, with revisions & modifications in
response to Session-6 discussions.
Graduating students are encouraged to submit early 
Project 2
BUS 212 f(2) Spring 2014
6
Brief Description of Assignments (complete assignment details to be distributed in class):
Introduction to Modeling with R and R Studio
Analysis 1
Analysis 2
Build a model to support In-Game Betting in Football (soccer)
Analysis 3
Qantas Airlines: Twitter Nosedive
Project 1
As assigned in class.
Project 2
As assigned in class.
Supplementary Readings and Cases (chronologically during 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.
Bigus, P (2012) “Qantas Airlines: Twitter Nosedive.” Ivy Publishing
Rev. 9/2014