MSBA Fall Semester Core Classes
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Welcome MSBA Fall Semester Core Classes
Beginning, Monday, September 20, 2021
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Joe Wilck
BUAD 5022 Optimization
Welcome to Optimization! We have an exciting semester ahead and I will have the honor of guiding you through your introduction to prescriptive analytics during this course. Along with your Stochastic Modeling course, together these courses provide the analytic foundation for problem solving and decision-making; collectively, Management Science and Operations Research.
Have you ever had to make a big decision? How did you decide? Did you win or lose? How do you know? Was it rational … or driven by intuition? Data drives business decision-making every day, especially for those that want to operate efficiently, grow, and continue to compete. How do we use data to our competitive advantage? How do we recommend decisions and know that our analyses are correct? The emphasis on the word know is deliberate, and powerful.
Statistics is the language by which we communicate about data, and in that course, you will learn to model uncertainty. Numbers like “average labor cost”, “customers per hour”, and “expected manufacturing capacity” will be analyzed from data and the variation modeled. You will also learn to model how these figures change over time and possibly forecast to the future. Why is this important? Well, the analysis using Probability & Statistics and Database Systems then become inputs to your Optimization models, a subject I can’t wait to introduce you to! We will discuss algorithms that efficiently solve business problems as well as introduce some terminology and techniques that are in much demand in industry.
Inundated with data, decision-making can seem untenable. My goal is for you to understand what options are available for solving massive decision problems, among a plethora of options. We will move beyond the descriptive and predictive analytics of statistics. We will practice harnessing huge amounts of data to recommend options that betray intuition alone. Further, we will know when our solution is the best, and when further effort might yield an even better decision. This is the domain of prescriptive analytics.
Finally, we will integrate our newfound knowledge of descriptive, predictive, and prescriptive analytics to solve business cases. This course will assemble what you learn in Stochastic Models, Optimization, and Database Systems into a larger picture. The idea that fortunes thrive and rest upon your analyses should energize you! The models you build can be the engines behind very powerful software! Come December, you will be amazed with your progress.
The text I have chosen gives a unified treatment of our main topics. We will extend its contents in class with additional resources, especially for the tools we will be using: Python, Gurobi, R, Excel, MySQL, and Solver. (You do not need to order the MindTap extras.) While electronic rentals are acceptable, this text contains so many interesting problems you may want to keep it as a reference as you begin your analytics careers.
Required: Winston and Albright. Practical Management Science. 6th ed. 2018. ISBN-13: 978-1337406659
Sincerely,
Joseph Wilck, Ph.D., P.E.
Miller Hall Room 3072
757-221-2894 (office); 434-390-4576 (mobile)
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Monica C. Tremblay
BUAD 5272 Database Management
Welcome to the MSBA Program and the Database Management course. Databases are vital for the functioning of modern companies and organizations. We all interact with databases every day, such as when we make purchases, view content on the Internet, or engage in banking transactions. Databases store data. In this program, you will learn how to turn that data into actionable and useful information. You must understand how these data are collected, cleaned, manipulated, and stored. This course will provide coverage of the most fundamental issues and topics related to the development and use of databases and database systems.
Organizations store data in two types of databases: operational and analytical. We will spend the first half of the class covering operational database topics, including database requirements, entity relationship modeling, relational modeling database constraints, update anomalies, normalization, Structures Query Language (SQL), the database front end, and data quality. In the second half of the class, we will focus on analytical database topics including coverage of data warehousing concepts, dimensional modeling (star schemas), data warehouse/data mart modeling approaches, the extraction/transformation/load (ETL) process, online analytical processing (OLAP)/business intelligence (BI) functionalities, and the data warehouse/data mart front end.
In this course we will use MySQL Workbench. We will also build cubes using a data integration tool called Alteryx. I will be providing tutorials and lots of hands opportunities in class. We will also be using a free Web-based data modelling suite ERDPlus to create ER diagrams, relational schemas, and dimensional models (star schemas).
We will be using the following text:
I wish you well and look forward to meeting you in the Fall. Please don’t hesitate to contact me if you have any questions.
Regards,
Dr. Monica Chiarini Tremblay
Associate Professor
757-221-2621
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Pamela Schlosser
BUAD 5072 Machine Learning I
Welcome to Machine Learning. My class is the first part of a two-part series on Machine Learning. Throughout both semesters, you will work on initial principles of Machine Learning, all the way through advanced concepts in the discipline.
Machine Leaning is an exciting subfield of Artificial Intelligence, which broadly refers to the ability of a computer system or program to perceive and learn by interpreting data. I will teach Machine Learning from the perspective of Statistical Learning, a well-established perspective on Machine Learning. Statistical “Machine” Learning is an analytical approach where data is modeled with the goal of understanding varying levels of uncertainty. One example is Predictive Modeling, which attempts to predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time.
Regarding required software and materials, we will be using R throughout these two courses. R is an increasingly popular, open source language and environment for statistical computing and graphics. We will use R to analyze and visualize our data. I have chosen the following text for Machine Learning I, which is written in R:
Title: An Introduction to Statistical Learning with Applications in R; Authors: James, Witten, Hastie, and Tibshirani; ISBN: 978-1-4614-7137-0
Some examples of the topics that will be covered in Machine Learning I are:
- Where Machine Learning fits in the Business Analytics skills hierarchy
- The trade-off between prediction accuracy and model interpretability
- Simple and Multiple Linear Regression
- Logistic Regression
- Discriminant Analysis
- Cross Validation and the Bootstrap
Sincerely,
Dr. Pamela Galluch Schlosser
Associate Professor
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Alejandro Gelves
BUAD 5032 Stochastic Modeling
Welcome to the MSBA program! We have an exciting semester ahead and I will have the honor of guiding you through the stochastic modeling course. This course provides the analytic foundation for problem solving and decision-making under uncertainty.
Statistics is the language by which we communicate about data. Thus, if you want to talk about data, you do so using statistics. If we want to acknowledge uncertainty, we must use probability. Together, probability and statistics allow us to capture the essence of our business, model the uncertainty in the world, and communicate our findings with a shared language.
On Mondays and Wednesdays (and occasional Fridays), we will discuss stochastic modeling. My goal is to arm you with the techniques necessary to navigate decisions under the uncertainty that you will most definitely face in your career. Further, it is my hope that you will be able to contribute analytic support with analyses that allow decision makers to make better decisions in your organizations.
Inundated with data, decision-making can seem untenable. My goal is for you to understand what options are available for solving massive decision problems, among a plethora of options. We will move beyond the descriptive and predictive analytics of statistics. We will practice harnessing huge amounts of data to recommend options that betray intuition alone. Further, we will know when our solution is the best, and when further effort might yield an even better decision. This is the domain of prescriptive analytics.
The text I have chosen for reference give a unified treatment of our main topics. We will extend their contents in class with additional resources (R and Excel). Over time, the tools will come and go, but the fundamental knowledge will remain.
Winston and Albright. Practical Management Science. 6th ed. 2018. ISBN-13: 978-1337406659
I sincerely look forward to working with you this fall.
Regards,
Dr. J. Alejandro Gelves
Associate Clinical Professor
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One Year Masters' Program Office
Please feel free to contact us with any questions!
Beth Snavely
Asst. Director One Year Masters' Programs
email - Beth.Snavely@mason.wm.edu
757-221-2879 (office)
Julie Hummel, M.Ed.
Director One Year Masters' Programs
email - Julie.Hummel@mason.wm.edu
757-221-6213 (office)
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