NYU Stern School of Business

Undergraduate College

INFO-UB.0057.001 (C20.0057): DATA MINING FOR BUSNSS INTELLIGENCE

Spring 2013

Instructor Details

Zhang, Xiaohan

xzhang@stern.nyu.edu

TBD

KMC 8-181

 

Course Meetings

MW, 3:30pm to 4:45pm

Tisch T-LC21


Final Exam:

Schedule exceptions
    Class will not meet on:
    Class will meet on:

 

Course Description and Learning Goals

This course will change the way you think about data and its role in business.

Businesses, governments, and individuals create massive collections of data as a by-product of their activity. Increasingly, decision-makers and systems rely on intelligent technology to analyze data systematically to improve decision-making. In many cases automating analytical and decision-making processes is necessary because of the volume of data and the speed with which new data are generated.

We will examine how data analysis technologies can be used to improve decision-making.  We will study the fundamental principles and techniques of data mining, and we will examine real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.  In addition, we will work “hands-on” with data mining software. 

After taking this course you should:

1.      Approach business problems data-analytically. Think carefully & systematically about whether & how data can improve business performance, to make better-informed decisions for management, marketing, investment, etc.

2.      Be able to interact competently on the topic of data mining for business intelligence.  Know the basics of data mining processes, algorithms, & systems well enough to interact with CTOs, expert data miners, consultants, etc.  Envision opportunities.

3. Have had hands-on experience mining data.  Be prepared to follow up on ideas or opportunities that present themselves, e.g., by performing pilot studies.

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The course will explain with real-world examples the uses and some technical details of various data mining techniques. The emphasis primarily is on understanding the business application of data mining techniques, and secondarily on the variety of techniques.  We will discuss the mechanics of how the methods work as is necessary to understand the general concepts and business application.

THE REST OF THIS DOCUMENT IS A PRELIMINARY SYLLABUS FOR REGISTRATION.  A FULL SYLLABUS WILL BE AVAILABLE IN JANUARY 2013.

 

Course Outline

Introduction

What is DM?, why DM now?, DM process, relation to other BI techniques, different data mining tasks

NOTE: This is the outline from Spring 2011; it seemed to work very well, so it likely will be very similar for Spring 2013.
[Sorry for the strange formatting: it was pasted intp the Stern syllabus form from the actual Word syllabus.]
 

Data Mining Fundamentals: Predictive Modeling

How do I produce a useful segmentation? what is a model?  basic terminology,  predictive modeling, classification, regression, tree induction, class-probability estimation

 
 

How do I know my model is any good?  evaluation, in-sample versus out-of-sample, overfitting, cross-validation, domain knowledge validation, error costs, ROC analysis,  expected value framework, geometric interpretation, linear model versus tree induction, logistic regression,

 toolkit demo

 
 
 

Bayesian & memory-based reasoning, nearest neighbors, variable normalization, text classification, "naïve" Bayes, spam filtering

 
 

Data Mining Fundamentals:Descriptive/

Unsupervised Data Mining

descriptive data mining, unsupervised algorithms, associations, clustering

 

 

 

review

 
 
 

MIDTERM QUIZ 

 

 SPRING BREAK

 
 

discuss midterm quiz;

business applications of data mining

 

Data Mining in Action:

 cases, applications, and practical insight

 

Fraud

Customer Retention

Image Classification

 

variable selection, feature engineering,

neural networks

social networks

 
 

On-line Advertising

 
 

ethics of data mining, privacy, what can/do firms know about you?, what should they do? 

 

 
 

Data Mining for a business application TBD

 

Data mining for credit management

data mining process in action, expected value in action, clustering revisited

 

Data mining and electronic commerce

competitive advantage, recommender systems, collaborative filtering, selecting on-line offers

 
 

Wrap-up & review

 

project presentations

 
 

 

Required Course Materials

·         Lecture notes: For most classes I will hand out lecture notes, which will outline the primary material for the class.  You will be expected to flesh these out with your own note taking, and to ask questions about any material in the notes that is unclear after our class discussion. Depending on the direction our class discussion takes, we may not cover all material in the notes.  If the notes themselves are not adequate to explain a topic we skip, you should ask about it on the discussion board.

Other readings are intended to supplement the material we learn in class.  They give alternative perspectives on and additional details about the topics we cover:

·         Supplemental readings posted to blackboard or distributed in class.  

·         Supplemental book (optional):

Data Mining Techniques, Second Edition
by Michael Berry and Gordon Linoff , Wiley, 2004
ISBN: 0-471-47064-3

Many students find this book to be an excellent supplemental resource.  In the class schedule I suggest the most important sections to read to supplement each class module.

 

Assessment Components

Students will be assessed based on homeworks, exams, class participation, and a group project.

 

Group Projects

Guidelines for Group Projects

Business activities involve group effort. Consequently, learning how to work effectively in a group is a critical part of your business education.

Every member is expected to carry an equal share of the group’s workload. As such, it is in your interest to be involved in all aspects of the project. Even if you divide the work rather than work on each piece together, you are still responsible for each part. The group project will be graded as a whole:   its different components will not be graded separately. Your exams may contain questions that are based on aspects of your group projects.

It is recommended that each group establish ground rules early in the process to facilitate your joint work including a problem-solving process for handling conflicts. In the infrequent case where you believe that a group member is not carrying out his or her fair share of work, you are urged not to permit problems to develop to a point where they become serious. If you cannot resolve conflicts internally after your best efforts, they should be brought to my attention and I will work with you to find a resolution.

You will be asked to complete a peer evaluation form to evaluate the contribution of each of your group members (including your own contribution) at the conclusion of each project. If there is consensus that a group member did not contribute a fair share of work to the project, I will consider this feedback during grading.

 

Grading

At NYU Stern we seek to teach challenging courses that allow students to demonstrate their mastery of the subject matter. Assigning grades that reward excellence and reflect differences in performance is important to ensuring the integrity of our curriculum.

In general, students in this elective course can expect a grading distribution where about 50% of students will receive A’s for excellent work and the remainder will receive B’s for good or very good work. In the event that a student performs only adequately or below, he or she can expect to receive a C or lower.

Note that the actual distribution for this course and your own grade will depend upon how well each of you actually performs in this course.

 

Re-Grading

The process of assigning grades is intended to be one of unbiased evaluation. Students are encouraged to respect the integrity and authority of the professor’s grading system and are discouraged from pursuing arbitrary challenges to it.

If you believe an inadvertent error has been made in the grading of an individual assignment or in assessing an overall course grade, a request to have the grade re-evaluated may be submitted. You must submit such requests in writing to me within 7 days of receiving the grade, including a brief written statement of why you believe that an error in grading has been made.

 

Professional Responsibilities For This Course

Attendance

 

Participation

In-class contribution is a significant part of your grade and an important part of our shared learning experience. Your active participation helps me to evaluate your overall performance.
You can excel in this area if you come to class on time and contribute to the course by:

 

Assignments

 

Classroom Norms

 

Stern Policies

General Behavior
The School expects that students will conduct themselves with respect and professionalism toward faculty, students, and others present in class and will follow the rules laid down by the instructor for classroom behavior.  Students who fail to do so may be asked to leave the classroom. 

 

Collaboration on Graded Assignments
Students may not work together on graded assignment unless the instructor gives express permission. 

 

Course Evaluations
Course evaluations are important to us and to students who come after you.  Please complete them thoughtfully.

 

Academic Integrity

Integrity is critical to the learning process and to all that we do here at NYU Stern. As members of our community, all students agree to abide by the NYU Stern Student Code of Conduct, which includes a commitment to:

The entire Stern Student Code of Conduct applies to all students enrolled in Stern courses and can be found here:

Undergraduate College: http://www.stern.nyu.edu/uc/codeofconduct
Graduate Programs: http://w4.stern.nyu.edu/studentactivities/involved.cfm?doc_id=102505

To help ensure the integrity of our learning community, prose assignments you submit to Blackboard will be submitted to Turnitin.  Turnitin will compare your submission to a database of prior submissions to Turnitin, current and archived Web pages, periodicals, journals, and publications.  Additionally, your document will become part of the Turnitin database.

 

Recording of Classes

Your class may be recorded for educational purposes

 

Students with Disabilities

If you have a qualified disability and will require academic accommodation of any kind during this course, you must notify me at the beginning of the course and provide a letter from the Moses Center for Students with Disabilities (CSD, 998-4980, www.nyu.edu/csd) verifying your registration and outlining the accommodations they recommend.  If you will need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be guaranteed accommodation.

 

Course Pre-Requisites

Basic knowledge of statistics and computer programming is recommended but not required.

There will be in-class tutorials on all related subjects.

 

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