BUS 212 f(2) Spring 2015 2
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)
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://www-stat.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-means 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
Course Approach / 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 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 computer-based 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 / 20% / Ûplease note!
Brief analyses (3) / 40%
Projects (2 parts) / 40%
TOTAL / 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.
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
/Session 1
March 10
/ Starting at the End: Visualizations to Support Business IntelligenceREADINGS: Russom, Big Data Analytics (2013, on LATTE)
P&F, Chapter 1 & 2
Watson, “All about Analytics”
a. Course introduction and objectives
b. Relationship of Business knowledge and Big Data Analytics
c. Data Mining Process (overview)
d. Introduce/ Review R & R Studio
Laptops helpful /
(none)
Session 2
March 17
/ Decision Trees & Logistic RegressionREADINGS: P&F, Chap 3
Few, Dashboard Design
CASE READING: A Game of Two Halves: In-Play Betting in Football
a. Supervised Segmentation
b. Theory: Decision trees and concepts of Logistic Regression (simple/ multinomial logistic)
c. Application: Game of Two Halves /
Analysis I
(R data analysis)
Session 3
March 24
/ Classification Models and PerformanceREADINGS: P&F, Chaps 4–5
CASE READING: Predicting Customer Churn at QWE
a. Classification models with regression
b. Training & Validation
c. Confusion Matrix to assess model performance
Laptops helpful /
Analysis 2(Game of Two Halves)
Session 4
March 31
/ Association RulesREADINGS: P&F, Chaps 6–8
Market Basket Analysis (on LATTE)
a. Project 1 Debriefing
b. Unsupervised Data Mining: Association Rules/Market Basket Analysis
Laptops helpful /
Project 1
(Churn at QWE)Passover Break
April 7
/ NO CLASS MEETING THIS WEEK /Session 5
April 14
/ Text MiningREADINGS: P&F, Chap 10
Tsur et al. A Great Catchy Name
CASE READING: Qantas Airlines Twitter case
a. Text Mining basics
b. Word clouds in R
c. Sentiment analysis with Twitter data
Laptops helpful /
Session 6
April 21
/ Review, Summary & ProjectREADINGS: P&F, Chaps 11 & 12
Project 2 instructions
· 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 discussionTuesdayApril 29
/ 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 J /
Project 2
Brief Description of Assignments (complete assignment details to be distributed in class):
Analysis 1 / Introduction to Modeling with R and R StudioAnalysis 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)
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