HARVARD UNIVERSITY EXTENSION SCHOOL

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HARVARD UNIVERSITY EXTENSION SCHOOL


COURSE SYLLABUS

Spring

2014


Course Number and Title:


MGMT E


5070


Data Mining
and Forecast Management
( 23274

)


Class Day and Time: Location:


Tues
day
, 7:40


9:40

pm

Emerson Hall, Room 104


Website:
http://isites.harvard.edu/course/ext
-
23274/2014/spring



Professor:


Philip A. Vaccaro, PhD


Telephone: 978
-
542
-
6836

Full
Professor
and Former Chair


Fax:

978
-
542
-
6027

Marketing and Decision Sciences e
-
mail:
pvaccaro@
salemstate.edu

Bertolon School of Business

Salem State

University




Introduction:


Every organization, large and small, private and public, uses forecasting and data mining
either explicitly or implicitly, becau
se it must plan to meet the conditions of the future for

which it has imperfect knowledge. The need for forecasting and data mining cuts across
all functional lines. They are the essential inputs for the practice of operations, strategic,

crisis, change, f
inancial, enrollment, innovation, and quality management, as well as
the

practice of customer relations marketing and marketing research. Consider the following

questions that dictate the need for forecasting and data mining.




If we increase our advertisin
g budget by 10%, how will sales be affected?



How is the market value of a home affected by the presence or absence of a
swimming pool or patio?



What revenue might the state government expect over the next two years?



What are the chances of a castastrophic
power failure or natural disaster on our
operations, and to what degree will they be affected?



What factors can we identify that will help explain the variability in monthly unit
sales?





-
2
-




Will there be a recession? If so, when will it begin, how sev
ere will it be, and
when will it end?



When will we need to readjust our manufacturing and service delivery systems, in
order to maintain our quality standards?


Organizations that cannot react quickly to changing conditions and cannot foresee the
future wi
th any degree of accuracy are doomed to extinction.


Course Description:


This course introduces nonmathematical managers to the major quantitative models
designed for sound demand, competitive, and system forecasting in today’s complex and
increasingly un
certain business environment. The course is useful for multiple business
disciplines, including general management, marketing, and finance. Topics include game
theory, Markov processes, statistical quality control, exponential smoothing, and season
-
ally ad
justed trend analysis. Emphasis is placed on a general understanding of theory,

mechanics, application potential, available software packages, and templates.


Prerequisite:


A rudimentary knowledge of algebra, statistics, and familiarity with spreadsheet
s.


Course Objectives:




To provide an introduction to the field of forecasting and its interface with
business administration and computer science.



To introduce the student to specific forecasting techniques / models in wide use:
their origin, purposes, st
rengths, limitations, and future.



To inculcate an awareness of the usefulness and power of the computer in this
field and the field’s increasing dependency on the computer.



To develop the student’s ability to identify forecasting opportunities; to identify

the critical variables; to build or se
lect an appropriate

model; and to develop an
accurate forecast.



Major Phases of the Course ( see the section below in this syllabus for
assignments associated with each phase. )




qualitative, time
-
series, and causal m
odel overview



exponential smoothing techniques



simple regression models: linear and non
-
linear



multiple

regression models: linear and non
-
linear



correlation analysis, decomposition, seasonal variation, and trend





-
3
-




significance testing



statistical q
uality control systems



markov processes and game theory



monitoring and controlling forecasts


Exit Competencies:


The student shall have demonstrated the ability to identify appropriate forecasting
applications; to identify the critical variables; to build

or select a suitable forecasting
model; and to develop a series of forecasts that are timely, accurate, economical, and easy
to understand, use, and maintain.


Required Materials:


Text:
Barry Render, Ralph Stair, Jr., Michael Hanna,
Quantitative Analysi
s for
Management,
Prentice
-
Hall, 11
th

Edition, 2011
.

Software:
Free downloads from
www.pearsonhighered.com/render
.


Course Activities to Meet Objectives:





Student is required to take four (4) examinat
ions that will test theoretical
knowledge and practical problem
-
solving skills.





Student will be assigned graded homework on a weekly basis for purposes
of developing proficiency and accuracy, both written and computer
-
based,
in the application of cours
e models and techniques.


Course Grading Policies:


Assignments:


o

Homework assignments are due for the class session in which they are
covered. In the event of illness or business necessity, homework assign
-
ments may be mailed, or faxed, or e
-
mailed to the

instructor before the

start of the next class session.

o

Written and computer assignments will be announced in class.

o

Computer malfunctions or other technical problems are
not
acceptable
excuses for missed deadlines.

o

Copies should be made of all assignments

for use in class discussion.






-
4
-


Examinations:


o

Four ( 4 ) examinations will be given during the semester.

o

The examinations will cover the assigned readings

in the text, lecture
material,
and comments made in conjunction with the homework
assignment
s.


Attendance:




Regular attendance is appreciated
.



Students with excessive unexcused absences may either be dropped from
the class roll by the instructor or face an automatic loss of points from the
final point total.



Each student is responsible for keepi
ng abreast of all reading assignments,
homework assignments, and lecture material, whether or not he/she is pre
-
sent in class.


Grading:





Grades will be determined according to the following weights:




Examination

Weight

1
st

20%

2
nd


20%

3
rd


20%

Fi
nal

20%

Homework

20%



Homework Assignments:




Assigned on a weekly basis.



Collected before class.



Returned in the next class as
satisfactory

or
unsatisfactory
.



Answers to the homework assignments are presented and thoroughly
discussed in class.







-
5
-



Schedule of Assignments:


o


The schedule given below is tentative and may vary due to class progress.

o


If a class is missed, check for changes in assignments with the instructor by



e
-
mail or telephone.

o


All students are expected to be present at
the final examination period, or, in the

case of a take
-
home term or final examination, to submit it in accordance with the

date/time specifications of the instructor. There are no excuses.





Date

Session

Topic

Chapter(s)

Exam

1/2
8
/1
4

1

Forecast

Overview

5


2/0
4
/1
4

2

Error Measurement

5


2/
11
/1
4

3

Moving Average Models

5


2/1
8
/1
4

4

Monitoring and Control

5

I
(1
st

5)

2/2
5
/1
4

5

Simple Regression

4

, Readings


3/
04
/1
4

6

Multiple Regression

4

, Readings


3/
11
/1
4

7

Nonlinear Regression

4


3/
1
8
/
1
4

-

Spring Vacation

4

II

(4)

3/2
5
/1
4

8

Correlation Analysis


-


4/01
/1
4

9

Trend, Seasona
l
Analysis

5


4/0
8
/1
4

10

Decomposition

5


4/
15
/1
4

11

Significance Testing

5

III(2
nd

5)

4/
22
/1
4

12

Game Theory

Module 4
-
1


4/
29
/1
4

13

Markov Analysis

1
5


5/
06
/1
4

14

Statistical Quality Ctrl

1
6


5/
13
/1
4

15

Final Examination

-

(4
-
1,
15,16
)





Withdrawal from Course:


Students should refer to the Extension School policies on the website:

http://www.extension.harvard.edu/registration/registration
-
guidelines/course
-
changes
-
withdrawals








-
6
-


Disability Services:


The Extension School is committed to creating an accessible academic community where
stud
ents and instructors with disabilities have equal opportunity to participate in, contri
-
bute to, and benefit from its academic programs. Services for students with disabilities
are approved and coordinated by Academic Services. Students sho
uld contact
the
Disability Services Coordinator at 617
-
495
-
0977 or 617
-
495
-
9419 TTY. Students should
also visit the Extension School website for more information about services for students
with disabilities.


Computer/Software:




Students are required to have computer acc
ounts.



This course is NOT designed to provide in
-
depth training on specific software
packages but allows for a general understanding of the utilization of such pack
-
ages and templates

in quantitative forecasting
.



There are no scheduled computer lab session
s for this course.



The required software for this

course might be packaged with every new copy of
the textbook, or a companion website will permit free software downloads.



If the student CD
-
ROM is misplaced, or if a used text
book is purchased, the
companio
n website is available for a free replacement software download.



Many required software application assignments wil
l be done with “QM for
Windows” and “EXCEL QM”,

easy
-
to
-
use decision support program
s which
assume

no prior programming skills.



Sample sprea
dsheets with step
-
by
-
step

instruction
s are included in the text and
power point presentations.



Mac software versions are available free from the companion website.