1. 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

2. Top Analytics Employers in the Greater Richmond Area

3. 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 fast- growing 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.

4. SCMA Major - Analytics Track Objectives
Upon completion of the SCMA Major - Analytics Track, the student will have learned how to:
Manage an analytic project from concept design to communication of insights
Acquire data from various sources, manipulate data, and transfer data between different environments
Develop strategies and problem-solving skills that help manage business uncertainty and risk
Apply the different methodologies required to make sound analytic decisions
Communicate results through advanced visualization techniques

5. SCMA Major - Analytics Track Advanced Business Program
SCMA 350 – Intro to Project Management
Manage business uncertainty and risk
Make sound analytic decisions
Manage an analytic project from concept design to communication of insights
SCMA 310 Data Management
Acquire, manipulate and transfer data
SCMA 410 Data Visualization
Communicate results
SCMA 302 Business Statistics
SCMA 303 Introduction to Analytics

6. 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.

7. 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

8. 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:
Active thinking: the ability to recognize the most efficient path to the correct solution to a project or task.
Pattern recognition: the ability to identify the correct approach to a problem through a framework built from experience.
Paraphrasing: the ability to synthesize a complex word problem into a model or algorithm.
Attention to detail: the ability to thoroughly complete tasks and provide accurate reports, and to efficiently troubleshoot errors in quantitative analyses.

9. New Courses Emphasis – Critical Thinking framework
Definition of issue/problem.
Recognition of issue/problem’s thesis/hypothesis.
Validation of assumptions and conditions.
Data analysis.
Conclusions and related outcomes.

10. 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:
Results and conclusions 25%
Written report 25%
Visual presentation 25%
Oral delivery 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.

11. 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:
Data wrangling with OpenRefine, and database querying with Microsoft Access & SQL.
Intermediate spreadsheet management and modeling with Excel.

12. Creation of New Courses SCMA 310 Data Management
Course Textbook
Custom book to be created from bundling together parts of the following materials:
Monk et al., Problem-Solving Cases in Microsoft Access & Excel, 14th edition (2016), Cengage Learning. ISBN-13:
Coronel & Morris, Database Systems: Design, Implementation, & Management, 12th Edition (2016), Cengage Learning. ISBN-13:
Albright & Winston, Business Analytics: Data Analysis & Decision Making, 6th Edition (2017), Cengage Learning. ISBN-13:
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)

13. 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:
Basic to intermediate data visualization techniques using Excel and Tableau.
Use of open source environments such as RStudio and R Presentations to author professional and customized presentations.

14. Creation of New Courses SCMA 410 Data Visualization
Reference Materials:
Albright & Winston, Business Analytics: Data Analysis & Decision Making, 6th Edition (2017), Cengage Learning. ISBN-13:
Peck, Tableau 9: The Official Guide, 2nd Edition (2016), Wiley. ISBN-13:
Leemis, Learning Base R, 1st Edition (2015), Lawrence Leemis. ISBN-13:
Prerequisites (with a grade of “C” or above):
Business Statistics II (SCMA 302) or equivalent
Introduction to Analytics (SCMA 303)

15. 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:
Simulation:
Decision Analysis:
Linear
Probability distributions (discrete and continuous)
Decision Trees
Integer/Binary
Bayes Theorem
Non-linear
Risk Analysis
Utility Functions
Simulation Optimization

16. Modification of Existing Courses SCMA 303 Introduction to Analytics
Course layout changes:
Pre-requisite changes:

17. Modification of Existing Courses SCMA 440 Data Mining and Forecasting
List of desirable topics:
1st Half - Data Mining:
2nd Half - Forecasting:
Q1 - Descriptive:
Q3 - Time Series:
Data partitioning
Moving average
Hierarchical clustering
Exponential smoothing
K-means clustering
Holt-Winters
Q2 - Predictive:
Q4 - Forecasting:
Logistic regression
Autoregressive models
Classification trees
Regression-based forecasting

18. Appendix New Course Syllabi

19. SCMA 310 Data Management – Class Schedule (First Half)
Date
Att.
Lesson type
Subjects 1
1/17
Introduction/Learn
Data wrangling 2
1/19
Practice
Openrefine examples 3
1/24
Learn
Database design and Microsoft Access 4
1/26
Create tables, forms, queries, and reports 5
1/31
Design a relational database 6
2/2
Relational database design applications 7
2/7
Quiz
Quiz 1 8
2/9
Discussion
Quiz debrief and midterm case introduction 9
2/14
Introduction to Structured Query Language (SQL)
2/16
Examples of SQL query applications 10
2/21
SQL data definition and manipulation commands 11
2/23
Practice/Learn
SQL manipulation commands and SELECT queries 12
2/28
Examples of SELECT query applications 13
3/2
Quiz 2
3/7
No class
Spring Break
3/9 14
3/14
Presentation
Midterm case study

20. SCMA 310 Data Management – Class Schedule (Second Half)
Date
Att.
Lesson type
Subjects 15
3/16 1
Learn
Import data in Excel and use of functions 16
3/21
Practice
Spreadsheet management applications 17
3/23
Finding relationships among variables 18
3/28
Pivot Tables, filtering, and what-if analyses 19
3/30
Decision Support System using Scenario Manager 20
4/4
Examples of decision support applications 21
4/6
Quiz
Quiz 3 22
4/11
Discussion
Quiz debrief and final case introduction 23
4/13
Visual Basic for Applications (VBA) 24
4/18
Examples of macros for spreadsheet management 25
4/20
Events, functions, and Application object in VBA 26
4/25
Examples of events, functions, and Application object 27
4/27
Quiz 4 28
5/2
Presentation
Final case study

21. SCMA 410 Data Visualization– Class Schedule (First Half)
Date
Att.
Lesson type
Subjects 1
1/17
Introduction/Learn
Introduction to basic chart types in Excel 2
1/19
Practice
Visualizations of variables and their relationships 3
1/24
Learn
The Tableau interface 4
1/26
Examples of basic chart applications with Tableau 5
1/31
Join types, visual analytics, sorting and grouping 6
2/2
Visual analytics applications 7
2/7
Quiz
Quiz 1 8
2/9
Discussion
Quiz debrief and midterm case introduction 9
2/14
Table calculations and parameters
2/16
Calculated field applications 10
2/21
GIS mapping 11
2/23
Practice/Learn
Geocoding and layers applications. Dashboards 12
2/28
Dashboard and storyboard applications 13
3/2
Quiz 2
3/7
No class
Spring Break
3/9 14
3/14
Presentation
Midterm case study

22. SCMA 410 Data Visualization– Class Schedule (Second Half)
Date
Att.
Lesson type
Subjects 15
3/16 1
Learn
Introduction to R and RStudio 16
3/21
Practice
Examples of data manipulations with R 17
3/23
Bar charts and histograms with Rstudio 18
3/28
Applications on bar charts and histograms 19
3/30
Box plots and scatter plots with Rstudio 20
4/4
Applications on plots with partition 21
4/6
Quiz
Quiz 3 22
4/11
Discussion
Quiz debrief and final case introduction 23
4/13
Spatial data in R 24
4/18
GIS applications in Rstudio 25
4/20
Introduction to Markdown and R Presentations 26
4/25
Applications of HTML5 authoring with R 27
4/27
Quiz 4 28
5/2
Presentation
Final case study