CURRICULUM PROPOSAL
BUSINESS ADMINISTRATION: BUSINESS ANALYTICS, B.S.
Item #1 – Deactivate BS: Business Administration: Industrial Management.
Rationale: Significant changes to the program requirements and a change of name warrant
deactivation of Industrial Management concentration.
Item #2 – Create a new concentration: “Business Administration: Business Analytics, B.S.”
Rationale: Business Analytics is the practice of iterative, methodical exploration of an
organization’s data with emphasis on statistical analysis. Advances in information technology
have enabled companies to collect vast amounts of data. Insightful use of this data to drive
decision making and gain competitive advantage is vital for businesses to survive. Tim McGuire
of the global business consulting firm McKinsey & Company says, “Analytics will define the
difference between the losers and winners going forward.”1 Another McKinsey report states
“The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise
and 1.5 million managers and analysts with the skills to understand and make decisions based on
the analysis of big data.“2
There is huge regional and local demand for business analytics trained professionals.
Chattanooga is one of the metropolitan areas with high potential for job growth in the
information technology area. The GIG initiative of the city is expected to further increase the
demand for analytics trained professionals. Area businesses such as BCBST, Unum,
Volkswagen, and TVA have large and growing data analytic departments.
An important part of the mission of the College of Business is providing quality educational
programs that produce academically-prepared and business-world ready men and women for a
competitive global environment. This proposal for Business Analytics concentration is one of
the steps to fulfill this mission.
1
2
http://www.mckinsey.com/insights/business_technology/making_data_analytics_work
http://www.mckinsey.com/features/big_data
CURRICULUM PROPOSAL
BUSINESS ADMINISTRATION: BUSINESS ANALYTICS, B.S.
PROGRAM
General Education
The same as all undergraduate Management majors
Program requirements
The same as all undergraduate Management majors
Required courses: 24 hours including
MGT 3560 - Prescriptive Analytics (Revised course)
MGT 3600 - Management Information Systems
MGT 3660 - Business Forecasting
MGT 4260 - Introduction to Business Analytics (New course)
MGT 4270 - Advanced Business Analytics (New course)
MGT 4280 - Supply Chain Management (New course)
MGT 4250 - Database and Data warehouse (New course)
3 hours from:
MGT 4380 - International Management
MKT 3180 - International Marketing
FIN 4120 - International Finance
Electives: 9 hours chosen from:
BUS 3900r - Internship (Only 3 hours)
ACC 3050 - Managerial Cost Accounting
MGT 3300 - Concepts in Organizational Behavior
MGT 3310 - Organizational Motivation and Leadership
MGT 3320 - Human Resource Management
MGT 4140 - Managerial Decision-Making
MKT 4150 - Business to Business Marketing
ECON 4290 - Managerial Economics
MGT 3760 - Business Simulation (New course)
New courses proposed:
MGT 4260 - Introduction to Business Analytics
MGT 4270 - Advanced Business Analytics
MGT 4280 - Supply Chain Management
MGT 4360 - Database and Data warehouse
MGT 3760 - Business Simulation (Elective)
CATALOG DESCRIPTION OF PROPOSED NEW COURSES
MGT 3760 Business Simulation
Monte Carlo and Discrete-event simulation modeling and analysis of business system.
Applications from a variety of business disciplines including marketing, operations, finance,
scheduling, and staffing will be discussed. Spring semester. Prerequisites: MGT 3110, junior
standing or department head approval. Differential course fee will be assessed.
MGT 4250 Databases and Data Warehouses
This course covers an introduction to organizational databases, database management systems,
data warehouses and queries and business reports. Students will become familiar with
operational and analytical database systems and design and built simple databases, write queries,
and prepare reports for better decision making. Fall semester. Prerequisite: MGT 3600, junior
standing or department head approval. Differential Course Fee will be assessed.
MGT 4260 - Introduction to Business Analytics
This course will provide an introduction to business analytics. Students will learn how to prepare
the data for analytics purposes and understand to use various predictive Analytics approaches.
These techniques will be examined in the context of various business applications such as
healthcare, marketing, finance, and retailing. Fall semester. Prerequisite: MGT 4250 (or
concurrent enrollment), junior standing or department head approval. Differential Course Fee
will be assessed.
MGT 4270 - Advanced Business Analytics
This course builds on the Introduction to Business Analytics course (MGT 4260) and will
describing various methodologies for describing the structured data and applying analytics
approaches to understanding unstructured data from disparate sources. Spring semester.
Prerequisite: MGT 4250, MGT 4260, junior standing or department head approval. Differential
Course Fee will be assessed.
MGT 4280 Supply Chain Management
Concepts and techniques related to designing and managing supply chains. Topics include
Master production scheduling, material requirements planning, capacity requirements planning,
logistics, purchasing/sourcing, warehousing, and inventory management. Emphasis on analysis
of the competitive environment, distribution network alternatives, and customer service aspects
provide a background in each functional area to enable students to pursue their areas of interest.
Fall semester. Prerequisites: MGT 3110, junior standing or department head approval.
Differential course fee will be assessed.
Item #3: Proposed change of name and description
MGT 3560 Prescriptive Analytics (Current title: Management Science)
Concepts and applications of quantitative techniques for business decision making under
deterministic and stochastic conditions. The course will focus on model building, analysis,
interpretation and decision making using linear programming, integer programming, goal
programming, decision analysis, queuing systems, and dynamic programming. Fall semester.
Prerequisites: MGT 1000 or CPSC 1000, MGT 2120, MATH 1130 or MATH 1710 with a
minimum grade of C or MATH 1720 or MATH 1830 or MATH 1910 or Math ACT score of 26
or better, junior standing, or department head approval. Differential course fee will be assessed.
Current description:
Concepts and applications of quantitative (mathematical) techniques, and computer analysis for
business decision making under deterministic and stochastic conditions. Topics include
mathematical model formulation, linear programming, integer programming, goal programming,
transportation problems, assignment problems, network models, project scheduling, decision
analysis, queuing, dynamic programming, and Markov processes. Fall semester. Prerequisites:
MGT 1000 or CPSC 1000, MGT 2120, MATH 1130 or MATH 1710 with a minimum grade of
C or MATH 1720 or MATH 1830 or MATH 1910 or Math ACT score of 26 or better, junior
standing, or department head approval. Differential course fee will be assessed.
Goals of Business Analytics (BA) Concentration
The mission of the Business Analytics concentration is to prepare students for a career in
Business Analytics. Students who successfully complete the program should be able to use a
range of information technologies ranging from spreadsheet software to advanced statistical
packages to analyze big data and develop business strategies. In short, a graduate of the BA
concentration should possess the expertise to analyze data from multiple sources and help
businesses make better decisions.
Students graduating from the BA concentration should demonstrate expertise in the following
functional areas within BA:
1. Data collection, organization, and preparation
2. Static and dynamic data visualization
3. Data mining including Cluster Analysis, RFM modeling and Market Basket analysis
4. Predictive analytic techniques including Forecasting, Decision Tree, Regression, Logit
regression, and Neural Networks
5. Prescriptive analytic techniques including applications such as linear and integer
optimization, nonlinear optimization, optimization models with uncertainty
6. Text mining
Further, students graduating from the BA concentration should demonstrate the ability to
effectively communicate ideas and concepts in oral presentations, interpersonal relations, and
in written formats.
Faculty teaching the courses offered in the BA concentration
Current Faculty:
Dr. Mohammad Ahmadi
Dr. Beni Asllani
Dr. Parthasarati Dileepan
Dr. Ashish Gupta
Prof. John Osterhage
Prof. David Witt
New Faculty:
One BA analytic faculty will be recruited in the approved line available from retirement.
Financial impact
The college already has the necessary hardware and software resources. Therefore, there will be
no financial impact.
CLEAR PATH – Business Administration: Business Analytics, B.S.
Freshman Year




Meet with Academic Advisor two times each semester.
Go to every class. Contact your instructor if you are absent.
Devote enough time to your studies and think about course materials and how they apply to your life.
Become actively involved in at least one co-curricular activity.
Fall Semester:
ENGL 1010
MATH 1130
Fine Arts
Cultures and Civilizations I
Elective Outside of Business
Elective Outside of Business
Hrs
3
3
3
3
3
1
16
Spring Semester
ENGL 1020
MATH 1830
Humanities or 2nd Fine Arts
MGT 1000
Cultures and Civilizations II
Hrs
3
3
3
3
3
15
Sophomore Year



Confirm your academic major choice and know the requirements and career fields related to your major.
Using MyMocsDegree, create course plan for your remaining degree requirements.
Involve yourself in learning opportunities that are challenging and relevant.
Fall Semester:
Natural Science with Lab
ANTH 1520 or PSY 1010 or SOC 1510
MGT 2110
ACC 2010
ECON 1010
Hrs
4
3
3
3
3
16
Spring Semester
Natural Science non-Lab
THSP 1090
MGT 2120
ACC 2020
ECON 1020
Hrs
3
3
3
3
3
15
Junior Year



Participate in study abroad, leadership opportunities, service learning, civic engagement, internships, research
projects, and other learning opportunities.
Develop your job or graduate school strategy.
Update your graduation plan as necessary.
Fall Semester:
MGT 3560
MGT 3150
FIN 3020
MGT 3130
MGT 3100 or ENGL 2880
Hrs
3
3
3
3
3
15
Spring Semester
MGT 3110
MKT 3600
MGT 3660
BUS 3350
Concentration elective
Hrs
3
3
3
3
3
15
Spring Semester
MGT 4400
MGT 4410
MGT 4380
MGT 4270
3000/4000 Elective Outside
Business
Hrs
3
1
3
3
Senior Year

Complete your Graduation Application with the Records Office.
Fall Semester:
MGT 4250
MGT 4260
MGT 4280
Concentration elective
Hrs
3
3
3
3
Concentration elective
3
15
3
13
Item #4: Proposal for the new courses
Syllabi of proposed new courses follow
Department of Management
COURSE:
TITLE:
PREREQUISITES:
CREDIT:
FACULTY:
TEXTS:
MGT 3760
Business Simulation
MGT 3110
3 hours
Dr. Mo Ahmadi
1. Simulation Modeling Using @RISK: Updated for Version 4, 1st
Edition, Wayne L. Winston Indiana University, Kelley School of Business
(Emeritus), ISBN-10: 053438059X, ISBN-13: 9780534380595
2. Simulation Using ProModel, 3rd Edition, Charles R. Harrell, ISBN-13
9780073401300
CATALOG DESCRIPTION
Monte Carlo and Discrete-event simulation modeling and analysis of business system.
Applications from a variety of business disciplines including marketing, operations, finance,
scheduling, and staffing will be discussed. Spring semester. Prerequisites: MGT 3110, junior
standing or department head approval. Differential course fee will be assessed.
COURSE OBJECTIVES
This is a course in Monte Carlo and Discrete-event simulation for undergraduate
students. The course will cover modeling, simulation, analysis of results, and use of
simulation software such as ProModel and @Risk. Complete design, analysis, and
mostly applications of Monte Carlo and Discrete-event simulation experiments will be
emphasized. Applications will be taken mostly from manufacturing, healthcare, and
service systems. The course will include the basic concepts of simulation as well as more
advanced topics, which will make it possible for the students to simulate various systems
and thoroughly understand how simulation can aid in better decision making processes.
Topics include stochastic models for simulation, use of analytics for designing
simulations and output analysis, and random variable and process generation. Other
objectives of the course include:




Develop competency in mathematical model formulation.
Use computer technologies for simulating various systems.
Analyze data and convert data to useful information.
Use the information to enhance critical thinking.
COURSE CONDUCT
The course will be conducted primarily in a lecture-discussion manner. Students will have
access to computers in class. Therefore, in every meeting, students will work on a simulation
project. Homework will be required. Students must have their assignments completed before
each class meeting. Assignments that are turned in late (no later than one class period) will
receive a maximum of 50% of the credit. It is expected that each student will be prepared for
each meeting and will agree with the following policies.
1.
2.
3.
4.
5.
Read the assigned chapters and textbook material before coming to class.
Have the assignments ready to be turned in.
Be prepared to complete an in-class assessment in every meeting.
There will be no make-up for the missed daily assessments.
Missed daily assignments will receive a grade of zero. Assignments that are turned in
late (no later than one class period) will receive a maximum of 50% of the credit.
GRADING SYSTEM
Grade Item
Three exams
HW Assignments
Final project
Total
Points
300
100
50
450
Final grade
90% and above = A
80% - 90% = B
70% - 80% = C
60% - 70% = D
Below 60% = F
COMPUTER USAGE
ProModelTM and @Risk computer software will be used for simulation. Also, Excel will be used
throughout the course. Students MUST HAVE THOROUGH KNOWLEDGE OF EXCEL.
OTHER COMMENTS
a. During the semester, numerous handouts will be distributed or e-mailed to you via
Blackboard. If you miss a class or cannot download a file, you are responsible for
obtaining handouts, assignments and other information from your classmates.
b. This is an elective upper division course. There will not be any tutor available for this
course.
c. Upon registration, all UTC students become subject to the rules and regulations of the Honor
Code (see the Student Handbook).
The Honor Code is fully enforced in my classes.
The Honor Code is based upon the assumption that the student
recognizes the fundamental importance of honesty in all dealings within
the University community and that education is a cooperative enterprise
between student and teacher and between student and student. Any act
of dishonesty violates and weakens this relationship and lessens the
value of the education that the student is pursuing.
d. College of Business faculty will be happy to share their knowledge and experience with you
as you plan for your future. If you have any questions regarding career paths in this field or
interest in applying to graduate school, please feel free to meet with me (or any faculty
member.
ADA STATEMENT: Attention: If you are a student with a disability (e.g. physical, learning,
psychiatric, vision, hearing, etc.) and think that you might need special assistance or a special
accommodation in this class or any other class, call the Office for Students with Disabilities at
425-4006, come by the office - 102 Frist Hall or see http://www.utc.edu/OSD/
If you find that personal problems, career indecision, study and time management difficulties,
etc. are adversely affecting your successful progress at UTC, please contact the Counseling and
Career Planning Center at 425-4438 or
http://www.utc.edu/Administration/CounselingAndCareerPlanning/
SCHEDULE
DATE
ACTIVITY
Week 1
Course Overview – Monte Carlo Simulation using spreadsheet (Chapters 1, 2 and 3)
Week 2
Simulation with @Risk (Chapters 4 and 5)
Week 3
Business applications using @Risk (Selected chapters from 6 to 13)
Week 4
Business applications using @Risk (Selected chapters from 14 to 24)
Week 5
EXAM 1
Week 6
Introduction to discrete event simulation and ProModel (Chapters 1-4 and Labs 1-3)
Week 7
Data collection and Analysis (Chapter 5 and Labs 5)
Week 8
Model building and verification (Chapters 6 and 7, and Labs 4, 6, and 7)
Week 9
Simulation output analysis (Chapter 8 and Lab 8)
Week 10
EXAM 2
Week 11
Comparing systems (Chapter 9 and Lab 9)
Week 12
Simulation optimization (Chapter 10 and Lab 10)
Week 13
Modeling manufacturing systems (Chapters 11 & 12 and Labs 11 & 12)
Week 14
Modeling service systems (Chapter 13 and Lab 13)
Week 15
FINAL EXAM
Department of Management
COURSE:
TITLE:
PREREQUISITES:
CREDIT:
FACULTY:
TEXTBOOK:
MGT 4250
Databases and Data Warehouses
MGT 3600
3 hours
Dr. Arben Asllani
"Database Systems – Introduction to Databases and Data Warehouses",
Jukic, Vrbsky, Nestorov, 1st edition (2014), Publisher: PEARSON, ISBN:
9780132575676
CATALOG DESCRIPTION
This course covers an introduction to organizational databases, database management systems,
data warehouses and queries and business reports. Students will become familiar with
operational and analytical database systems and design and built simple databases, write queries,
and prepare reports for better decision making. Fall semester. Prerequisite: MGT 3600, junior
standing or department head approval. Differential Course Fee will be assessed
GOALS AND OBJECTIVES
This is an undergraduate level course and as such students will be expected to gain knowledge
about databases and data warehouses. Students in this course will learn how to design and use
operational and analytical databases.
Skills and Competencies: Specifically, at the completion of this course, the student will
understand the role of databases and their impact in the organization, represent a business
description into an entity relationship diagram, create tables, queries, forms and reports using a
selected database management system. In addition, students will be introduced to the concepts
of data warehousing, star schemas, data marts, the extract-transform-load process, online
analytical processing, and business intelligence.
GRADING SYSTEM
Grade Item
Group Project
Midterm
HW Assignments
Final Exam
Total
Points
20%
20%
30%
30%
100%
Letter Grade
A
B
C
D
F
From
90%
80%
70%
60%
0%
To
100%
90%
80%
70%
60%
OTHER POLICIES
Policy on Late Submissions:
Timely completion of all assignments is critical to student success. Instructors may grant limited
extensions of time for unexpected business, health or personal emergencies beyond the student's
control. Student must make the request in advance of the due date and support the request by a
compelling. Any such extension will be for a period not to exceed one week. For late
submissions that have not been approved by the instructor (and for assignments submitted after
an extension due date) the penalty will be a 20% reduction in the grade for that assignment for
each day that the assignment is late. Please note that there can be no extensions for the
assignments during the last week.
Policy on Academic Integrity and Plagiarism:
The Honor Code is based upon the assumption that the student recognizes the fundamental
importance of honesty in all dealings within the University community and that education is a
cooperative enterprise between student and teacher and between student and student. Any act of
dishonesty violates and weakens this relationship and lessens the value of the education which
the student is pursuing. The Honor Code and the Honor Court and its procedures are detailed in
the UTC’s Student Handbook.
ADA STATEMENT: Attention: If you are a student with a disability (e.g., physical, learning,
psychiatric, vision, hearing, etc.) and think that you might need special assistance or a special
accommodation in this class or any other class, call the Disability Resource Center (DRC) at
423-425-4006 or come by the office, 102 Frist Hall. (See http://www.utc.edu/disability-resourcecenter/.)
If you find that personal problems, career indecision, study and time management difficulties,
etc. are adversely affecting your successful progress at UTC, please contact the Counseling and
Career Planning Center at 425-4438 or visit:
http://www.utc.edu/Administration/CounselingAndCareerPlanning/
Topics
Class Introduction, Course Syllabus
Introduction to Databases
Conceptual Design
Logical Design
Physical Design
Queries with Access
Forms and Reports with Access
Introduction to Data Warehouses
Star Schema
Online Analytical Processing
Data Mining
NOSQL Databases
Department of Management
COURSE:
MGMT 4260
TITLE:
Introduction to Business Analytics
PREREQUISITES: MGT 4360 (or concurrent enrollment), junior standing or department head
approval
CREDIT:
3 hours
FACULTY:
Ashish Gupta, PhD
TEXTS:
Data Mining Techniques: For Marketing, Sales, and Customer
Relationship Management, 3rd Edition, Gordon S. Linoff, Michael J. A.
Berry, 2011, ISBN: 978-0-470-65093-6
Course Description: This course will provide an introduction to Business Analytics. Students
will learn how to prepare the data for analytics purposes and understand to use various predictive
Analytics approaches. These techniques will be examined in the context of various business
applications such as healthcare, marketing, finance, and retailing. Fall semester. Prerequisite:
MGT 4250 (or concurrent enrollment), junior standing or department head approval. Differential
Course Fee will be assessed.
Course Objectives:
The purpose is to introduce students to
a) Provide the theoretical and practical understanding of the key methods of data preparation
methodologies using SAS Enterprise Guide.
b) Provide theoretical and practical understanding of various Predictive Analytics Techniques
using SAS Miner.
Mandatory Topics
Introduction to Business Analytics
Data preparation (using Enterprise Guide)
Recoding
Conditioning
Formatting
Filtering
Working with data from disparate sources: RDBMS and Data Warehouses
Predictive Analytics: (Using Enterprise Miner)
Accessing and Assaying Prepared Data
Decision Trees
Regression
Neural Networks
Ensemble
Software: SAS Enterprise Guide and SAS Enterprise Miner.
Course Conduct
The class will meet at the scheduled times in our classroom. We will develop the framework
through our class lectures, but also work on exercise, projects throughout the semester to give
you a real life, hands-on experience in Business Intelligence and data mining. There will be
lecture, software demonstrations, project updates, class discussion, etc. We will have a
occasional invited lectures from outside speakers. The class time will typically be distributed
with following proportion: 35% lecture, 45% hands-on training on tools, 20 % Discussion &
Participation. This proportion I sonly estimate and will certainly deviate.
Grading Policy
Grade Item
Class Participation (in class and online forums)
Midterm Exam I
Assignments (Multiple)
Final Exam
Total
Points
10%
20%
50%
20%
100%
Final grade
90% and above = A
80% - 90% = B
70% - 80% = C
60% - 70% = D
Below 60% = F
Exams: There will be two non-cumulative semester exams, including the final. These exams
could be a combination of in-class exams or take home exams. Each exam will be worth 100
points. In all other cases, failure to take an exam on during scheduled time window will
result in a score of zero (0) on the exam.
Case Studies and Assignments: Several assignments and case studies will be given over the
duration of the course during the semester. Unless otherwise specified by the instructor, you will
have one week to work on each assignment. You have to turn in your typed, well-organized
write-up electronically on blackboard by 5:30 PM (before lecture begins) of the stated due date.
The homework assignments are to be solved in groups unless otherwise stated. Assigned cases
will involve a written component and classroom discussion.
Group Term Project: This will be a group project activity of 3-4 students. Online forum use is
encouraged to participate in off-class discussions related to the projects. Projects will require the
use of either SAS guide or SAS miner software to analyze publicly available datasets from
various sources such as http://www.kdnuggets.com/datasets/ , http://archive.ics.uci.edu/ml/ . A
student could also avail datasets through their own resources. Other additional software packages
could be used as well. A proposal will be required to be submitted half way into the semester,
followed by the complete report due near the semester end and one team presentation in the
class. This project will provide the additional opportunity to the students to improve their
business analytics skills. More details about the project, due dates, expectations, will be provided
by the instructor later in the classroom. All projects will typically require teams to select their
own project, define the problem they would like to study within the data sets, and a report of the
findings along with major conclusions. Certain exceptions could be made with respect to
software use, if the project is industry driven or is of high impact value or is innovative while
being relevant to Analytics.
Class Participation- (Classroom interaction and Online Discussions): All students are expected
to actively provide thoughtful and relevant comments to class discussions through online forum
and within the classroom. A forum will be created where students will need to interact among
themselves on varied topics related to the class and the current topic that is underway. Both
types of participations are required: face-to-face and online. This is NOT a group activity and
students will be awarded scores at the end of semester based on their meaningful, value-added
contribution to the class and forum, finding and sharing new information, innovative news
and technologies related to the ongoing topic in the class. This means that students also need to
be reading and finding information from external sources proactively during the course. Students
need to be aware that instructor will be keeping track of individual contributions and will be
reading all the posts carefully. Please remember that quality and quantity are extremely
important. A post just meant for the purpose of quantity will not serve any benefit. It should
contribute towards either an ongoing discussion substantially or must start a new topic. Please do
not post comments such as ‘I agree with the previous post’, etc. Classroom discussions will
happen in multiple ways: case study discussions, reading articles, etc.
Your participation has to be consistent throughout the semester. All the messages relevant to the
topic must be posted while the chapter is still ongoing according to the class schedule.
Readings: Students will be expected to read assigned material before coming to class so that
they can be active participants in the learning experience. Reading throughout the semester will
make the lectures more interesting, help students prepare for tests and quizzes, and will allow
them to have more input in the direction of the course.
Late Work Policy: Late work is discouraged and will not be accepted. Prior arrangement needs
to be made with the instructor.
Honor Code
I understand that any work which I submit for course credit will imply that I have adhered to this
Academic Honor Code: “I will neither give nor receive unauthorized aid and I will not tolerate
an environment which condones the use of unauthorized aid.” All students are subject to the
UTC’s policy on academic dishonesty and integrity in the Student Handbook. Students in
violation of University policies will receive a failing course grade and appropriate administrative
action will be taken for record.
You will need following technology1. Fast speed internet access, UTC email access
2. Blackboard access https://bb4.utc.edu/
3. SAS on Demand http://support.sas.com/ondemand/index.html#account
(Requires one time registration)
4. SAS server Access http://support.sas.com/ctx3/sodareg/index.html
Disclosure: Please note that instructor reserves to make any changes in the course schedule
and syllabus at any time during the semester. Students will be informed beforehand
whenever such changes are made
ADA STATEMENT: Attention: If you are a student with a disability (e.g. physical, learning,
psychiatric, vision, hearing, etc.) and think that you might need special assistance or a special
accommodation in this class or any other class, call the Office for Students with Disabilities at
425-4006, come by the office - 102 Frist Hall or see http://www.utc.edu/OSD/
If you find that personal problems, career indecision, study and time management difficulties,
etc. are adversely affecting your successful progress at UTC, please contact the Counseling and
Career Planning Center at 425-4438 or
http://www.utc.edu/Administration/CounselingAndCareerPlanning/
Department of Management
COURSE:
TITLE:
PREREQUISITES:
CREDIT:
FACULTY:
TEXTS:
MGMT 4270
Advanced Business Analytics
MGT 4260, MGT 4360, junior standing or department head approval.
3 hours
Ashish Gupta, PhD
Business Intelligence: A Managerial Perspective on Analytics, 3/E,
Ramesh Sharda and Dursun Delen. Efraim Turban, ISBN-10:
0133051056, ISBN-13: 9780133051056, 2014, Prentice Hall
Course Description: This course builds on the Introduction to Business Analytics course (MGT
4260) and will describing various methodologies for describing the structured data and applying
analytics approaches to understanding unstructured data from disparate sources. Spring
semester. Prerequisite: MGT 4250, MGT 4260, junior standing or department head approval.
Differential Course Fee will be assessed.
Course Objectives:
The purpose is to introduce students to
a) Provide practical understanding of various descriptive analytics techniques.
b) Provide understanding of using text Analytics for processing unstructured data.
We will primarily use SAS Enterprise Miner, SAS Text Miner and Sentiment Analysis.
Topics covered:
Mandatory Topics
Cluster Analysis
Market Basket Analysis
Segmentation Analysis
Profiling
Visual Analytics
Text Analytics
Sentiment Analysis
Mixed Model Approaches
Introduction to Big Data and Hadoop methodologies
Software: Main program used will be SAS Enterprise Miner. Other SAS programs such as SAS
content Analysis, SAS sentiment Analysis, SAS Text Miner may also be used depending upon
time and availability.
Course Conduct
The class will meet at the scheduled times in our classroom. We will develop the framework
through our class lectures, but also work on exercise, projects throughout the semester to give
you a real life, hands-on experience in Business Intelligence and data mining. There will be
lecture, software demonstrations, project updates, class discussion, etc. We will have a
occasional invited lectures from outside speakers. The class time will typically be distributed
with following proportion: 35% lecture, 45% hands-on training on tools, 20 % Discussion &
Participation. This proportion I sonly estimate and will certainly deviate.
Grading Policy
Grade Item
Class Participation (in class and online forums)
Midterm Exam I
Assignments (Multiple)
Final Exam
Total
Points
10%
20%
50%
20%
100%
Final grade
90% and above = A
80% - 90% = B
70% - 80% = C
60% - 70% = D
Below 60% = F
Exams: There will be one non-cumulative mid-term semester exam. The exam will be a take
home exam. In all other cases, failure to take an exam on during scheduled time window
will result in a score of zero (0) on the exam.
Case Studies and Assignments: Several assignments and case studies will be given over the
duration of the course during the semester. Unless otherwise specified by the instructor, you will
have one week to work on each assignment. You have to turn in your typed, well-organized
write-up electronically on blackboard by the stated due date. The homework assignments are to
be solved in groups unless otherwise stated. Assigned cases will involve a written component
and classroom discussion.
Class Participation- (Classroom interaction and Online Discussions): All students are expected
to actively provide thoughtful and relevant comments to class discussions through online forum
and within the classroom. A forum will be created where students will need to interact among
themselves on varied topics related to the class and the current topic that is underway. Both
types of participations are required: face-to-face and online. This is NOT a group activity and
students will be awarded scores at the end of semester based on their meaningful, value-added
contribution to the class and forum, finding and sharing new information, innovative news
and technologies related to the ongoing topic in the class. This means that students also need to
be reading and finding information from external sources proactively during the course. Students
need to be aware that instructor will be keeping track of individual contributions and will be
reading all the posts carefully. Please remember that quality and quantity are extremely
important. A post just meant for the purpose of quantity will not serve any benefit. It should
contribute towards either an ongoing discussion substantially or must start a new topic. Please do
not post comments such as ‘I agree with the previous post’, etc. Classroom discussions will
happen in multiple ways: case study discussions, reading articles, etc.
Your participation has to be consistent throughout the semester. All the messages relevant to the topic
must be posted while the chapter is still ongoing according to the class schedule.
Readings: Students will be expected to read assigned material before coming to class so that
they can be active participants in the learning experience. Reading throughout the semester will
make the lectures more interesting, help students prepare for tests and quizzes, and will allow
them to have more input in the direction of the course.
Honor Code
I understand that any work which I submit for course credit will imply that I have adhered to this
Academic Honor Code: “I will neither give nor receive unauthorized aid and I will not tolerate
an environment which condones the use of unauthorized aid.” All students are subject to the
UTC’s policy on academic dishonesty and integrity in the Student Handbook. Students in
violation of University policies will receive a failing course grade and appropriate administrative
action will be taken for record.
You will need following technology5. Fast speed internet access, UTC email access
6. Blackboard access https://bb4.utc.edu/
7. SAS on Demand http://support.sas.com/ondemand/index.html#account
(Requires one time registration) optional.
8. SAS server Access http://support.sas.com/ctx3/sodareg/index.html
Disclosure: Please note that instructor reserves to make any changes in the course schedule
and syllabus at any time during the semester. Students will be informed beforehand
whenever such changes are made
ADA STATEMENT: Attention: If you are a student with a disability (e.g. physical, learning,
psychiatric, vision, hearing, etc.) and think that you might need special assistance or a special
accommodation in this class or any other class, call the Office for Students with Disabilities at
425-4006, come by the office - 102 Frist Hall or see http://www.utc.edu/OSD/
If you find that personal problems, career indecision, study and time management difficulties,
etc. are adversely affecting your successful progress at UTC, please contact the Counseling and
Career Planning Center at 425-4438 or
http://www.utc.edu/Administration/CounselingAndCareerPlanning/
Department of Management
COURSE:
TITLE:
PREREQUISITES:
CREDIT:
FACULTY:
TEXT:
MGT 4280
Supply Chain Management
MGT 3110
3 hours
David Witt
Manufacturing Planning and Control Systems for Supply Chain
Management, Sixth Edition, Vollmann, Berry, Whybark, & Jacobs (Irwin)
2010, ISBN 978-007-3377827, www.mhhe.com/jacobs-berry6e
CATALOG DESCRIPTION
Concepts and techniques related to designing and managing supply chains. Topics include
Master production scheduling, material requirements planning, capacity requirements planning,
logistics, purchasing/sourcing, warehousing, and inventory management. Emphasis on analysis
of the competitive environment, distribution network alternatives, and customer service aspects
provide a background in each functional area to enable students to pursue their areas of interest.
Fall semester. Prerequisites: MGT 3110, junior standing or department head approval.
Differential course fee will be assessed.
OBJECTIVES
Achieve competency is designing and managing supply chains through fulfilling the following:
 Master demand forecasting and management techniques.
 Learn Master Production Schedule and capacity management techniques
 Learn to develop Material Requirements Plans, Capacity Requirements plans, and
Distribution Requirements plans
 Learn to manage supply chain logistics
 Achieve expertise in various inventory management and control methods
SKILLS AND COMPTENCIES
We will analyze data and expand proficiency in the use of data, recognize problems and
opportunities and encourage critical thinking, develop expertise in an area related to career
choice, communicate effectively in written form, and foster understanding of the impact of
business decisions in supply chain management.
COURSE CONDUCT
Class meetings will be devoted to lectures and discussion of the material from the texts and
current issues from other sources. We will use the Socratic Method and students are expected to
participate in active dialog regarding the topic at hand. You will need your book in order to
review concepts and work on homework. Please make sure you bring your textbook to each and
every class.
GRADING SYSTEM
Your grade will be determined by and based on the following components and weights. None of
the exams will be deliberately cumulative, although concepts developed in the earlier portion of
the course have applications in the later portions. Eligible material for the exams includes all
text and lecture material covered prior to the exam.
Exams and quizzes will be open book and open notes (including homework problems). The
essay part of the exams will be closed book. Grades will be based on the following weights:
EXAM 1
EXAM 2
FINAL EXAM
HOMEWORK
QUIZZES
PARTICIPATION & ATTENDANCE
Total
100 points
100 points
100 points
60 points
50 points
40 points
450 points
Grading
90% and above = A
80% - 90% = B
70% - 80% = C
60% - 70% = D
Below 60% = F
All grades will be earned based on quality, completeness, and technical proficiency. In order to
complete the course all projects, assignments, exams, etc. must be completed.
Participation & Attendance (40 Points)
Participation and attendance are both expected of all students for each class. Students must
come to class prepared which means having fully read and analyzed the various readings and
assignments. Participation points must be earned. Attendance is also vital so we may have a full
intellectual discussion. If there is an emergency and you will not be able to attend class please
notify me as soon as possible. Participation will be rewarded in part by being prepared to discuss
homework assigned. Advance notice is required if a student is unable to make it to class.
Homework (60 Points)
Homework will be assigned for each class. The cumulative homework will grade up to 60
points. It is expected that students will come prepared to discuss the problems and essays
assigned and share their work with the class.
UTC Honor Code Pledge
I pledge that I will neither give nor receive unauthorized aid on any test or assignment. I
understand that plagiarism constitutes a serious instance of unauthorized aid. I further pledge
that I will exert every effort to insure that the Honor Code is upheld by others and that I will
actively support the establishment and continuance of a campus-wide climate of honor and
integrity.
ADA STATEMENT: Attention: If you are a student with a disability (e.g., physical, learning,
psychiatric, vision, hearing, etc.) and think that you might need special assistance or a special
accommodation in this class or any other class, call the Disability Resource Center (DRC) at
423-425-4006 or come by the office, 102 Frist Hall. (See http://www.utc.edu/disability-resourcecenter/.)
If you find that personal problems, career indecision, study and time management difficulties,
etc. are adversely affecting your successful progress at UTC, please contact the Counseling and
Career Planning Center at 425-4438 or
http://www.utc.edu/Administration/CounselingAndCareerPlanning/
____________________________________________________________________________
COURSE SCHEDULE
DATE
Week 1
ACTIVITY
Course Overview – Chapter 1 (Manufacturing Planning & Control for Supply Chain
Management)
Week 2
Chapter 4 and 4A – Sales and Operations Planning (SOP)
Week 3
Chapter 5 – Master Production Scheduling (MPS)
Week 4
Chapter 5 – Master Production Scheduling (MPS) … continued
Week 5
EXAM 1
Week 6
Chapter 6 – Materials Requirement Planning (MRP)
Week 7
Chapter 6A – Advanced Mat. Req. Planning (MRP)
Week 8
Chapter 7 – Capacity Planning & Management
Week 9
Chapter 9 – Just-in-Time
Week 10
EXAM 2
Week 11
Chapter 10 – Distribution Requirements Planning
Week 12
Chapter 10A – Management of Supply Chain Logistics
Week 13
Chapter 11 – Order Point Inventory Control Methods
Week 14
Chapter 12 – Strategy and Manufacturing Planning & Control (MPC) System Design
Week 15
FINAL EXAM