BUS 212f (2) ANALYZING BIG DATA II
Spring 2016—Tuesdays 6:30–9:20 pm
Sachar 116 (International Hall)
Prof. Robert Carver
781-775-5493 (mobile)
rcarver@brandeis.edu
Office: Sachar 1B (far end of computer cluster)
Hours: Tuesdays, 4:00 – 5:30 and by appointment
TAs:
Darsei Canhasi, Shantanu Livania, Tamsa Sabat
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
BUS 212 f(2) Spring 2016
Course Approach
2

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 as well as R for most of our analysis.
You should bring a laptop to each class.


Student Classroom
Contributions
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
Github is also a free environment that facilitates (a) collaborative work and
(b) version control for software projects that are under development. It is
very widely used by data scientists to manage and share their work.
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
BUS 212 f(2) Spring 2016
3
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 TAs, 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 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
15%
35%
50%
please note!
100%
Academic Integrity
You are expected to be honest in all of your academic work. Please consult
Brandeis University Rights and Responsibilities for all policies and procedures
related to academic integrity. Students may be required to submit work to
TurnItIn.com software to verify originality. Allegations of alleged academic
dishonesty will be forwarded to the Director of Academic Integrity. Sanctions
for academic dishonesty can include failing grades and/or suspension from the
university. Citation and research assistance can be found at LTS - Library
guides.
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.
BUS 212 f(2) Spring 2016

4
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
Session 1
March 15
READINGS: Russom, Big Data Analytics (2013, on LATTE)
P&F, Chapter 1 & 2
Watson, “All about Analytics”
Leek & Peng, ”What is the Question?”
a.
b.
c.
d.
(none)
Course introduction and objectives
Relationship of Business knowledge and Big Data Analytics
Data Mining Process (overview)
Introduce/ Review R & R Studio
Decision Trees & Logistic Regression
READINGS: P&F, Chap 3 & 4
Loh, “Classification and Regression Trees” (LATTE)
Session 2
March 22
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 Model Performance
READINGS: P&F, Chaps 5
CASE READING: Heterogeneity of Movement (posted on LATTE)
Session 3
March 29
a.
b.
c.
Classification models with regression
Training & Validation
Confusion Matrix to assess model performance
Analysis 2
(Game of Two
Halves)
BUS 212 f(2) Spring 2016
Session
Date
5
Deliverable Due
by class time
Topics and Readings
Association Rules
Session 4
April 5
READINGS: P&F, Chaps 6–8
“Cluster Analysis for Segmentation”
Recommended: Hastie & Tishbirani (parts of 13 & 14—
LATTE)
a.
b.
c.
Project 1
(Heterogeneity
of Movement)
Project 1 Debriefing
Clustering methods
Unsupervised Data Mining: Association Rules/Market Basket
Analysis
Basics of Text Mining
READINGS: P&F, Chap 10
Session 5
April 12
CASE READING: Job Salary Prediction (LATTE)
a.
b.
c.
Text Mining basics
Word clouds in R
Initial analysis of Job Salary data
Review, Summary & Project
Session 6
April 19
READINGS: P&F, Chaps 11 & 12
Zhao (R & Data Mining) Chapter 9
Nolan & Temple Lang “Exploring Data Science Jobs with
Web Scraping” (LATTE)
Project 2 instructions—Job Salary Prediction




Tuesday
May 3
Brief project-2
discussion
Debrief Analysis 3
Scraping the Web for Data
Other application areas and challenges
Developing models with Business Value
No Class Session this week


Final project due before this date.
Graduating students are encouraged to submit early
Analysis 3
(Job Salary Part
1)

Project 2
(Job Salary
Prediction)
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
Text analysis of Job Salary Prediction Data
Project 1
Heterogeneity of Movement
Project 2
Job Salary Prediction
BUS 212 f(2) Spring 2016
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.
Leek, J. and Peng, R. (2015) “What is the Question?” Sciencexpress. Published online 26 Februrary:
10.1126/science.aa6146.
Loh, Wei-Lin (2011) “Classification and Regression Trees” WIREs Data Mining and Knowledge
Discovery, Wiley.
Kumar, U., Sandeep, V. and Satyabala (2013) “A Game of Two Halves: In-Play Betting in
Football” (IMB-401). Indian Institute of Management–Bangalore.
“Heterogeneity of Movement” case: inspired by entry in the U.C. Irvine Machine Learning
Repository (2015). Online:
https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition.
Venkatesan, Rajkumar (2014). “Cluster Analysis for Segmentation” (UV0745-PDF-ENG).
Darden School of Business.
“Job Salary Prediction” case: Inspired by Kaggle Competition (2013). Online:
https://www.kaggle.com/c/job-salary-prediction.
“Exploring Data Science Jobs with Web Scraping” (2015). Based on Nolan, D. and Temple Lang, D.,
Data Science in R, Chapter 12. Boca Raton, FL: CRC Press.
Rev. 01/2016
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