SYLLABUS
DSCI 5340 – Predictive Analytics and Business Forecasting
Spring 2016
CLASS (DAY/TIME):
INSTRUCTOR:
OFFICE HRS:
CONTACT INFO:
T 6:30 - 9:20 PM, BLB 035
Dr. Nick Evangelopoulos
TW 1:00-2:00pm (BLB 365D)
OFFICE PHONE: 940-565-3056
E-MAIL (preferred): evangeln@unt.edu
Textbooks
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
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Forecasting, Time Series, and Regression, 4th edition, 2005, Bowerman,
O’Connell, and Koehler, Thomson Learning ISBN 0-534-40977-6 (REQUIRED)
Brocklebank & Dickey, SAS for Forecasting Time Series, 2nd Ed., Wiley 2009,
ISBN 978-1-59047-182-1 (OPTIONAL text)
In addition, a number of reference texts will be available in PDF (free of charge)
Software
SAS (main environment). Also Excel, Minitab, IBM SPSS (some exposure).
(NOTE: All packages are available in our COB virtual lab)
Blackboard Learn
Materials for the DSCI 5340 course will be posted on Blackboard Learn system.
Course Description
This course covers major topics used in developing predictive modeling and applied
statistical forecasting models that are of major interest to the business, government, and
academia. These include exploring the calibration of models, the estimation of seasonal
indices, and the selection of variables to generate operational business forecasts. Topics
in this course can assist business professionals in utilizing historical patterns to build a
more constructive view of their future. The course also examines how these topics can be
used in combination with data capture, integration and information deployment
capabilities, to ensure more productive decisions and more accurate planning. Modern
forecasting techniques for the evaluation of sophisticated business models used to make
intelligent decisions in marketing, finance, personnel management, production
scheduling, process control, facilities management and strategic planning, are covered.
Course Objectives
Complex business opportunities and software availability motivate us to equip our
students, who will be future managers and entrepreneurs, with sound business forecasting
skills. This course provides participants with knowledge related to the general topic of
statistical forecast modeling through the presentation of the concepts, methodologies and
tools to extract patterns commonly present in business data to appropriately model data.
Certain special topics related to forecasting intermittent demand will be included.
Understanding the role that forecasting plays in predictive analytics is an important
consideration. The course demonstrates through supplemental materials the practice of
developing predictive models using large volumes of data sets. By presentation of such
course topics as data considerations, model selection, moving averages and exponential
smoothing, regression analysis, time-series decomposition and recent developments in
predictive analytics, students are expected to:
1. Develop a forecasting capability.
2. Understand the significance of data analysis and model selection criteria.
3. Acquire expert level knowledge of providing analysis and interpreting output
from leading forecasting software packages.
Class Attendance
Regular class attendance and informed participation are expected.
Course Prerequisites
Graduate status and some introductory graduate course in Business Statistics such as
DSCI 5010, or DSCI 5180, or consent of the ITDS department, are required. While a
high degree of mathematical skill is not necessary in an “applied” course such as this,
there are certain insights into the course that are gained through the analytics involved.
Statistical software such as SAS, Minitab, and IBM SPSS will be used to demonstrate
specification applications. Information on the use of these software packages will be
provided in the course and students are not required to have prior experience with the
software.
Course Data Sets
Several course datasets required for the Mini-Case assignments, as well as examples
covered in class, will be posted on Blackboard.
Point Allocation
Exam 1
80 pts
Comprehensive Final Exam
100 pts
Quizzes (12@2.5 pts, 4 dropped)
20 pts
Mini-Cases (3@20 pts)
60 pts
Group Project & Presentation
40 pts
_____________________________________________________
TOTAL
300 pts
Letter Grades
270+ pts (=90%)
240+ pts (=80%)
210+ pts (=70%)
180+ pts (=60%)
Below 180 pts
=A
=B
=C
=D
=F
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Quizzes
There will be 12 Quizzes, worth 2.5 points each. These will be typically closed books, at
the instructor’s discretion. The Quizzes will be multiple-choice. They will cover the
material presented in class on the day of the Quiz. Make-up Quizzes will not be allowed,
but the 4 lowest grades among the 12 Quizzes will be dropped.
Mini-Cases
There will be 3 small case studies that will require individual work. Related handouts
will be distributed in class and related datasets will be posted on Blackboard.
Group Project
There will be a project that will require team work. Related handouts will be distributed
in class and related datasets will be posted on Blackboard. You will be asked to form
teams of 2-4 members. You will have to select a business problem that interests you (the
instructor will suggest a problem in case you run out of ideas.) Problem data/facts can be
real, obtained from published sources, or made up by your team in a way that they
correspond to realistic situations. Your team will prepare a written report and a
PowerPoint presentation, to be presented in class.
Exams
There will one mid-term examination during the semester, worth 80 points, and a final
exam, worth 100 points. (See section on Grading). The mid-term exam will be unit
exam. The final will be comprehensive. Exams will typically include multiple choice
questions, covering theory, problem formulation, analysis, and interpretation. The exams
will be open book, open notes. Simple calculators may be useful to some students, but
the exam will not focus at all on manual calculations. Programmable calculators, laptop
computers, tablets, smartphones, smart watches, and similar communication devices will
not be allowed during an examination. Use of cell phones during the exam will be
allowed only in case of emergency.
You are responsible for the exam-supporting materials. There will be no loaning or
sharing of books, calculators, or other items, or sharing of any type of information among
other students while taking the exam. If you fail to bring your required materials, you
must perform the exam to the best of your ability without them. You are encouraged to
collect the exams when they are returned.
Miscellaneous Policies
IMPORTANT DATES: Dates of drop deadlines, exams, final exams, etc., are published
in the university catalog and schedule of classes. It is your responsibility to be informed
with regard to these dates. Unawareness is no excuse. Do not wait until the last day to
drop the course if you are not making satisfactory progress in this class. Your instructor
may not be available at that time.
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Campus Closures
Should UNT close campus, it is your responsibility to keep checking your official UNT
e-mail account (EagleConnect), the UNT Web site, and Blackboard, to learn if your
instructor plans to modify class activities, and how. This may include changing
assignment due dates, rescheduling quizzes and exams, etc.
Student Perceptions of Teaching (SPOT)
Student Perceptions of Teaching (SPOT) utilizes IASystem® and is a requirement for all
organized classes at UNT. This short Web-based survey will be available to you at the
end of the semester, providing you with a chance to comment on how this class is taught.
I am very interested in this feedback from my students, as I work to continually improve
my teaching. I consider SPOT to be an important part of your class participation.
Use of Cell Phones
As a courtesy to your instructor and to your fellow classmates, you are asked to set your
cell phone to vibrate, or switch it off. In case of a personal emergency, if you must use
your cell phone, you are asked to step out of the classroom.
Students with Disabilities
UNT and the College of Business comply with the Americans with Disabilities Act in
making reasonable accommodations. If you have an established disability as defined in
the Americans with Disabilities Act, you need to register with the UNT Office for
Disability Accommodation. Please see your instructor as soon as possible if you have
any questions.
Academic Integrity
This course adheres to the UNT policy on academic integrity. The policy can be found at
http://vpaa.unt.edu/academic-integrity.htm. Practices that violate academic integrity,
such as “cheating” or “plagiarism”, are strongly discouraged. If you engage in academic
dishonesty related to this class, you may receive a failing grade on the test or assignment,
or a failing grade in the course. In addition, the case may be reported to the UNT Dean
of Students/Academic Integrity Office, which maintains a database of related violations.
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Class Schedule (Subject to change; Effective 1/19/2016)
Week
Date
Topics
1
19-Jan
2
26-Jan
3
2-Feb
4
5
6
7
9-Feb
16-Feb
23-Feb
1-Mar
Course Introduction
Review of Simple Regression
Last week to drop for an 80% refund
Multiple Regression
Last week to drop for a 70% refund
Regression with dummy variable terms
Last week to drop for a 50% refund
Decomposition
Exponential Smoothing
Box-Jenkins method
Time Series regression
8
8-Mar
Midterm Exam
9
15-Mar
22-Mar
UNT Spring Break (NO CLASS)
Time Series regression
10
11
29-Mar
5-Apr
12
13
12-Apr
19-Apr
14
15
26-Apr
3-Mar
ARIMA Model assessment
Seasonal Box Jenkins models
Last week to withdraw from a course
Generalized ARIMA
NO CLASS MEETING (Team Time)
Last week to withdraw from all courses
Interventions, Exam review
Project presentations, course
evaluation
Tue
May 10
Readings
Assignment
Due
Case #1 (20 pts)
Exam 1 (80 pts)
FINAL EXAM (Comprehensive):
6:30 pm-8:30 pm, normal
classroom
5
Case #2 (20 pts)
Case #3 (20 pts)
Project (40 pts)
Group
Presentations
Exam 2
(100 pts)