Pdf course description new york university

Pdf File 368.20 KByte, 5 Pages

Author: stanislav mamonov
Creator: Microsoft Word 2013
Producer: Microsoft Word 2013
CreationDate: Fri Oct 13 08:12:34 2017
ModDate: Fri Oct 13 08:12:34 2017
Tagged: yes
Form: none
Pages: 5
Encrypted: no
Page size: 612 x 792 pts (letter) (rotated 0 degrees)
File size: 377038 bytes
Optimized: no
PDF version: 1.5

  • New York University Bulletin 2017-2019
  • New York University University of Washington New York ...
  • PDF Course Description - New York University

PDF Course Description - New York University

INFO-GB.3336.00
Data Mining for Business Analytics
Spring 2018
Faculty Stanislav Mamonov, PhD
Adjunct Professor, Stern School of Business, New York University
Assistant Professor, School of Business, Montclair State University
Email: smamonov@stern.nyu.edu
Please put "NYU-DM" in the subject
Office hours Saturdays 12-1 PM or by appointment
Class time Saturdays 9AM -12 PM
Course Description
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 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 mining techniques 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.
The course is a combination of lecture, case studies, hands-on exercises and a final project.
Prerequisites: None
Course Objectives
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.
1
3. Have had hands-on experience mining data. Be prepared to follow up ideas or opportunities
that present themselves, e.g., by performing pilot studies.
Focus and Interaction
The course will explain through lectures and real-world examples the fundamental principles, uses, and
some technical details of 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 fundamental
concepts and business application.
I will expect you to be prepared for class discussions by having satisfied yourself that you understand
what we have done in the prior classes. The assigned readings will cover the fundamental material. The
class meetings will be a combination of lectures/discussions on the fundamental material, discussions of
business applications of the idea and techniques, case discussions, and student exercises.
You are expected to attend every class session, to arrive prior to the starting time, to remain for the
entire class, and to follow basic classroom etiquette, including having all electronic devices turned off
and put away for the during of the class (this is Stern policy, see below) and refraining from chatting or
doing other work or reading during class. In general, we will follow Stern default policies unless I state
otherwise. I will assume that you have read them and agree to abide by them

Classroom equipment
Students will be required to bring their laptops during nearly all classes with wireless Internet access to
complete the class demo and exercises.
Office hours and Email
Instructor's office hours: Saturdays 12-1 PM or by appointment.
If you have questions about class material that you do not want to ask in class, or that would take us
well off topic, please talk to me after class or see me during the office hours.
You may also send emails to ask questions or set up appointments outside of office hours. Please type
"NYU-DM" in the subject line of every email that you send.
I will check my email at least once a day during the week (M-F). Your email will get my priority when you
put "DM3336" in the subject. If no reply is received within 48 hours, your email may have been
overlooked and please feel free to send another email.
Course Homepage
The NYU Classes site is the main site for this Please check the page frequently for updates. You will find
the following materials: syllabus, lecture schedule, lecture notes, group project, frequently asked
2
questions, and course resources. Please print out the necessary materials for yourself. I will assume that
you have read all announcements and class discussion.
Course Materials and Textbook
Lecture slides and handouts distributed by the instructor. All lecture slides will be posted on NYU Classes
website. 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.
Strongly recommended book (provides key information on the topics covered in the course):
: Data Mining for Business Analytics: Concepts, Techniques and Applications. Galit Shmueli is the first
author. There are several versions of this book for different platforms. I recommend the R version if you
are looking to develop deeper analytics knowledge.
Recommended book (provides an introduction to Azure ML platform): Predictive Analytics with
Microsoft Azure Machine Learning, Second Edition. By Roger Barga, Valentine Fontama and Wee-Hyong
Tok. ISBN: 9781484212011.
Grading
Student grades will approximately consist of the following elements as related to learning objectives
Weekly assignments 20-40 points each 60%
Final project presentation 20%
Final project report 20%
At NYU Stern we seek to teach challenging courses that allow students to demonstrate differential
mastery of the subject matter. Assigning grades that reward excellent and reflect differences in
performance is important to ensuring the integrity of our curriculum. Students generally become
engaged with this course and do excellent or very good work, receiving As and Bs, and only one or two
perform only adequately or below and receive Cs or lower. Note that the actual distribution for this
course and your own grade will depend upon how well each of you actually performs this particular
semester.
Weekly assignments
There will be homework assignments nearly every week. Each homework comprises questions to be
answered and/or hands-on tasks. Except as explicitly noted otherwise, you are expected to complete
your assignments on your own - without interacting with others.
Assignments will be due on Friday night. Assignments will be graded and returned promptly. Answers to
homework questions should be well thought out and communicated precisely, avoiding sloppy
language, poor diagrams, and irrelevant discussion.
3
The hands-on tasks will be based on data that we will provide. You will mine the data to get hands-on
experience in formulating problems and using the various techniques discussed in class. You will use
these data to build and evaluate predictive models.
For the hands-on assignments you will be using Azure ML platform. You can create a free Azure ML
account here:
We will also be using Tableau extensively in the course. You can download here -
. I will distribute a license key at the beginning of the
course.
Please plan to bring a laptop to every class session. If you do not have a laptop, please see me
immediately so that we can make alternative arrangements.
Late Assignments
As stated above, assignments are to be submitted on NYU Classes on Friday night. Assignments up to 24
hours late will have their grade reduced by 25%; assignments up to one week late will have their grade
reduced by 50%. After one week, late assignments will receive no credit. Please turn in your assignment
early if there is any uncertainty about your ability to turn it in on time.
Final project
Final project will be done by student teams. Team members will be assigned by the instructor. I will
assign teams and distribute the outline for the final project by the third week of the semester. Teams
are encouraged to interact with the instructor and TA electronically or face-to-face in developing their
projects. You will submit a proposal for your project about half way through the course. Each team will
present its project at the end of the semester. Each team will also submit a written report. We will
discuss the project requirements and presentations in class.
Regrading
If you feel that a calculation, factual, or judgment error has been made in the grading of an assignment
or exam, please write a formal memo to me describing the error, within one week after the class date
on which that assignment was returned. Include documentation (e.g., pages in the book, a copy of class
notes, etc.). I will make a decision and get back to you as soon as I can. Please remember that grading
any assignments requires the grader to make many judgments as to how well you have answered the
question. Inevitably, some of these go "in your favor" and possibly some go against. In fairness to all
students, the entire assignment or exam will be regraded.
Disability Policy
If you have a qualified disability and will require academic accommodation during this course, please
contact the Moses Center for Students with Disabilities (CSD, 998-4980) and provide me with a letter
4
from them 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 Accommodation Form to them at
least one week prior to the scheduled exam time to be guaranteed accommodation.
Honor Code
We assume that you have complete integrity in all your class efforts. Violations of the University's Honor
Code will be taken extremely seriously, and they will be addressed promptly according to the
established procedures. Students are to adhere to the Code of Student Conduct, and other policies and
regulations as adopted and promulgated by appropriate University authorities. Copies of these
documents may be obtained from the Office of the Dean of Students or from the offices of the academic
deans. No infractions will be tolerated. Students violating the Code of Student Conduct will be dismissed
from class and will receive an "F" for the course.

Class Schedule (Tentative)
Topics Deliverables
Week 1 Introduction to Data Mining CV
Data visualization
Week 2 Multiple linear regression Assignment 1
Week 3 Classification / Logistic regression Assignment 2
Week 4 Decision trees Assignment 3
Week 5 Model performance evaluation Assignment 4
Integration
Week 6 Ensemble techniques Assignment 5
Week 7 Neural networks Assignment 6
Final project proposal
Week 8 SVM Assignment 7
Feature engineering and feature
selection
Week 9 Intro to text mining Assignment 8
Week 10 Guest speaker
Week 11 Final project consultations
Week 12 Final group presentations Final project presentation &
report
5

Download Pdf File Online Preview