Download Proposed Analytics Track for B.S. in Business

Department of Supply Chain Management & Analytics
Proposed Bachelor of Science (B.S.)
in Business - Analytics Track
Paolo Catasti, PhD, MBA, CSSBB
Teaching Assistant Professor
Statistics and Analytics
Top Analytics Employers in the Greater
Richmond Area
SCMA Major - Analytics Track
Definition
• Intended for those students who want to develop and sharpen their
analytics skills, and are interested in pursuing a career in the fastgrowing fields of business analytics and decision sciences.
• The program provides students with the knowledge, tools, and skills
needed to help them make sound business decisions that improve
upon the performance of organizations.
SCMA Major - Analytics Track
Objectives
• Upon completion of the SCMA Major - Analytics Track, the student
will have learned how to:
1. Manage an analytic project from concept design to communication of
insights
2. Acquire data from various sources, manipulate data, and transfer data
between different environments
3. Develop strategies and problem-solving skills that help manage business
uncertainty and risk
4. Apply the different methodologies required to make sound analytic
decisions
5. Communicate results through advanced visualization techniques
SCMA Major - Analytics Track
Advanced Business Program
SCMA 350 – Intro to Project Management
SCMA 302
Business
Statistics
SCMA 310
Data
Management
Acquire, manipulate
and transfer data
SCMA 303
Introduction
to Analytics
SCMA 410
Data
Visualization
Communicate
results
Manage business uncertainty and risk
Make sound analytic decisions
Manage an analytic project from concept design to communication of insights
SCMA Major - Analytics Track
Program Requirements
• The Advanced Business Program – Analytics Track would include the
following courses:
• Advanced Business Core (18 credits):
• SCMA 310 Data Management
• SCMA 302 Business Statistics II
• SCMA 303 Introduction to Analytics
• SCMA 350 Introduction to Project Management
• SCMA 386 Global Supply Chain Management
• SCMA 410 Data Visualization
• Three Advanced Business Electives (9 credits), one of which from SCMA
Notes: In Red are shown the courses currently under development.
In Blue are shown the courses that have recently been introduced/updated.
SCMA Major - Analytics Track
Proposed List of Advanced Business Electives
One course (3 credit) from:
• SCMA 339 Quantitative Solutions for Management
• SCMA 440 Data Mining and Forecasting
• SCMA 441 Prescriptive Analytics
Two courses (6 credit) from:
• ACCT 408 Accounting Decision Analysis
• BUSN 400 & 401 Principles of Consulting & International Consulting Practicum
• ECON 403 Introduction to Mathematical Economics
• FIRE 312 Financial Modeling
• INFO 320 Data Mining and Business Intelligence
• INFO 361 Systems Analysis and Design
• INFO 491 Big Data Analytics
• MKTG 310 Information for Marketing Decisions
New Courses Emphasis – Critical Thinking Definition
• The Analytics Track will help the student refine Critical Thinking skills
through the development of the following core competencies:
1. Active thinking: the ability to recognize the most efficient path to the
correct solution to a project or task.
2. Pattern recognition: the ability to identify the correct approach to a
problem through a framework built from experience.
3. Paraphrasing: the ability to synthesize a complex word problem into a
model or algorithm.
4. Attention to detail: the ability to thoroughly complete tasks and provide
accurate reports, and to efficiently troubleshoot errors in quantitative
analyses.
New Courses Emphasis – Critical Thinking framework
• Critical Thinking framework:
o Definition of issue/problem.
o Recognition of issue/problem’s thesis/hypothesis.
o Validation of assumptions and conditions.
o Data analysis.
o Conclusions and related outcomes.
New Courses Emphasis – Team Projects
• Courses focus on the communication & presentation of business insight to an
audience of peers.
• In the later part of each half of the course, students will assemble in teams of
about four, and work on a case that is designed to evaluate knowledge and
understanding of the course concepts.
• Team project deliverables and related weights:
o Results and conclusions
o Written report
o Visual presentation
o Oral delivery
25%
25%
25%
25%
• Individual case weight:
• Teammates will assign a participation weight to each team member, meant to characterize
each student’s level of involvement and contribution to the project.
Creation of New Courses
SCMA 310 Data Management
• Course Objective:
This course is designed for those undergraduate business students who seek to
develop intermediate to advanced spreadsheet modeling skills, and learn the
basic elements of database management for data querying, manipulation, and
extraction.
• Learning Outcomes:
The course is divided in two parts:
1. Data wrangling with OpenRefine, and database querying with Microsoft
Access & SQL.
2. Intermediate spreadsheet management and modeling with Excel.
Creation of New Courses
SCMA 310 Data Management
• Course Textbook
Custom book to be created from bundling together parts of the following materials:
o Monk et al., Problem-Solving Cases in Microsoft Access & Excel, 14th edition (2016),
Cengage Learning. ISBN-13: 978-1305-86862-5
o Coronel & Morris, Database Systems: Design, Implementation, & Management,
12th Edition (2016), Cengage Learning. ISBN-13: 978-1-305-62748-2
o Albright & Winston, Business Analytics: Data Analysis & Decision Making, 6th
Edition (2017), Cengage Learning. ISBN-13: 978-1-133-62960-3
• Prerequisites (with a grade of “C” or above):
• Digital Literacy: Spreadsheets Skills I (INFO 162)
• Differential Calculus and Optimization (SCMA 212)
• Business Statistics I (SCMA 301)
Creation of New Courses
SCMA 410 Data Visualization
• Course Objective:
This course is designed for those undergraduate business students who want to
develop basic to intermediate data visualization skills, and learn the basic
elements of GIS mapping and reporting through dashboards, and the authoring
of presentations through the R environment.
• Learning Outcomes:
The course is divided in two parts:
1. Basic to intermediate data visualization techniques using Excel and Tableau.
2. Use of open source environments such as RStudio and R Presentations to
author professional and customized presentations.
Creation of New Courses
SCMA 410 Data Visualization
• Reference Materials:
o Albright & Winston, Business Analytics: Data Analysis & Decision Making, 6th
Edition (2017), Cengage Learning. ISBN-13: 978-1-133-62960-3
o Peck, Tableau 9: The Official Guide, 2nd Edition (2016), Wiley. ISBN-13: 978-0071-84329-4
o Leemis, Learning Base R, 1st Edition (2015), Lawrence Leemis. ISBN-13: 978-0982-91748-0
• Prerequisites (with a grade of “C” or above):
• Business Statistics II (SCMA 302) or equivalent
• Introduction to Analytics (SCMA 303)
Creation of New Courses
SCMA 441 Prescriptive Analytics
• Expands on the topics of Optimization, Simulation, and Decision
Analysis introduced in SCMA 303 Introduction to Analytics.
• A list of desirable topics could be:
Optimization:
• Linear
• Integer/Binary
• Non-linear
Simulation:
• Probability distributions
(discrete and continuous)
• Risk Analysis
• Simulation Optimization
Decision Analysis:
• Decision Trees
• Bayes Theorem
• Utility Functions
Modification of Existing Courses
SCMA 303 Introduction to Analytics
• Course layout changes:
Proposed
Existing
Q1
1st Half
Q2
Q3
2nd Half
Q4
1.
2.
3.
4.
5.
6.
7.
8.
Introduction to Data Visualization
Spreadsheet Modeling
Regression Analysis
Time Series & Forecasting
Linear Optimization
Integer Optimization
Simulation
Decision Analysis
1.
2.
3.
4.
5.
6.
7.
8.
Introduction to Data Visualization
Time Series & Forecasting
Introduction to Optimization
Linear Optimization
Integer Optimization
Introduction to Simulation
Simulation Models
Decision Analysis
• Pre-requisite changes:
Existing
Proposed
SCMA 212 (or equivalent)
SCMA 301 (or equivalent)
SCMA 212 (or equivalent)
SCMA 301 (or equivalent)
SCMA 302 (or equivalent)
SCMA 310 Data Management
Modification of Existing Courses
SCMA 440 Data Mining and Forecasting
List of desirable topics:
1st Half - Data Mining:
Q1 - Descriptive:
Data partitioning
Hierarchical clustering
K-means clustering
Q2 - Predictive:
Logistic regression
Classification trees
2nd Half - Forecasting:
Q3 - Time Series:
Moving average
Exponential smoothing
Holt-Winters
Q4 - Forecasting:
Autoregressive models
Regression-based forecasting
Appendix
New Course Syllabi
SCMA 310 Data Management –
Class Schedule (First Half)
Class #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Date
1/17
1/19
1/24
1/26
1/31
2/2
2/7
2/9
2/14
2/16
2/21
Att.
1
1
1
1
1
1
2/23
2/28
3/2
3/7
3/9
3/14
1
1
2
1
1
1
Lesson type
Introduction/Learn
Practice
Learn
Practice
Learn
Practice
Quiz
Discussion
Learn
Practice
Learn
Practice/Learn
Practice
Quiz
No class
No class
Presentation
Subjects
Data wrangling
Openrefine examples
Database design and Microsoft Access
Create tables, forms, queries, and reports
Design a relational database
Relational database design applications
Quiz 1
Quiz debrief and midterm case introduction
Introduction to Structured Query Language (SQL)
Examples of SQL query applications
SQL data definition and manipulation commands
SQL manipulation commands and SELECT
queries
Examples of SELECT query applications
Quiz 2
Spring Break
Spring Break
Midterm case study
SCMA 310 Data Management –
Class Schedule (Second Half)
Class #
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Date
3/16
3/21
3/23
3/28
3/30
4/4
4/6
4/11
4/13
4/18
4/20
4/25
4/27
5/2
Att.
1
1
1
1
1
1
1
1
1
1
1
Lesson type
Learn
Practice
Learn
Practice
Learn
Practice
Quiz
Discussion
Learn
Practice
Learn
Practice
Quiz
Presentation
Subjects
Import data in Excel and use of functions
Spreadsheet management applications
Finding relationships among variables
Pivot Tables, filtering, and what-if analyses
Decision Support System using Scenario Manager
Examples of decision support applications
Quiz 3
Quiz debrief and final case introduction
Visual Basic for Applications (VBA)
Examples of macros for spreadsheet management
Events, functions, and Application object in VBA
Examples of events, functions, and Application object
Quiz 4
Final case study
SCMA 410 Data Visualization–
Class Schedule (First Half)
Class #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Date
1/17
1/19
1/24
1/26
1/31
2/2
2/7
2/9
2/14
2/16
2/21
2/23
2/28
3/2
3/7
3/9
3/14
Att.
1
1
1
1
1
1
2
1
1
1
1
1
Lesson type
Introduction/Learn
Practice
Learn
Practice
Learn
Practice
Quiz
Discussion
Learn
Practice
Learn
Practice/Learn
Practice
Quiz
No class
No class
Presentation
Subjects
Introduction to basic chart types in Excel
Visualizations of variables and their relationships
The Tableau interface
Examples of basic chart applications with Tableau
Join types, visual analytics, sorting and grouping
Visual analytics applications
Quiz 1
Quiz debrief and midterm case introduction
Table calculations and parameters
Calculated field applications
GIS mapping
Geocoding and layers applications. Dashboards
Dashboard and storyboard applications
Quiz 2
Spring Break
Spring Break
Midterm case study
SCMA 410 Data Visualization–
Class Schedule (Second Half)
Class #
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Date
3/16
3/21
3/23
3/28
3/30
4/4
4/6
4/11
4/13
4/18
4/20
4/25
4/27
5/2
Att.
1
1
1
1
1
1
1
1
1
1
1
Lesson type
Learn
Practice
Learn
Practice
Learn
Practice
Quiz
Discussion
Learn
Practice
Learn
Practice
Quiz
Presentation
Subjects
Introduction to R and RStudio
Examples of data manipulations with R
Bar charts and histograms with Rstudio
Applications on bar charts and histograms
Box plots and scatter plots with Rstudio
Applications on plots with partition
Quiz 3
Quiz debrief and final case introduction
Spatial data in R
GIS applications in Rstudio
Introduction to Markdown and R Presentations
Applications of HTML5 authoring with R
Quiz 4
Final case study