Master of Organizational Analytics (MOA)
Master of Organizational Analytics
The new Master of Organizational Analytics is designed to provide students with an overview of applied statistical analysis, one of the most prominent, in-demand and growing fields. You will gain the ability to manage, process, analyze, and interpret data through taking a variety of courses, across several fields. Students can further customize their program by focusing on one of four key tracks in Business, Health Care, Leadership, or Logistics.
MOA Degree Requirements
Sample Full-Time, One-Year Course Schedule:
|Fall 2020||Spring 2021|
|DAT 200/MOA 500||3||Reddig||DAT 315/MOA 515||3||McDevitt|
|DAT 300/MOA510||3||Krichevskiy||DAT 400/MOA 600||4||Krichevskiy|
|MOA 570 (new)||3||Lehman/McDevitt/Li||SCPS or BA course||3/4||Paul of SCPS|
|MA 252/MOA 552||4||Sigdel||SCPS or BA course||3||Paul or SCPS|
|SCPS||3||SCPS||SCPS or BA course||3/4||Staff or SCPS|
DAT 320/ MOA 520 (3) Reddig – Included in the tuition price, but not required. Will walk at graduation without course. This course prepares students to take the Oracle Exam. This course is only offered online in the summer because that is when the adjunct has been willing to teach it. If she is willing to teach during the spring, this could replace one of the SCPS electives and students would finish by graduation, for a total of 30+ credits.
Big Data and Statistical Analysis provides an introduction to the fundamentals of data preparation and management, data mining, and forecasting using SAS® Enterprise Miner. This is an application-driven course allowing students to gain an understanding of Enterprise Miner software environment, statistical basics of data mining and forecasting, and the practical issues involved in applied forecasting in a business setting. Upon completion of this course, students shall be able to independently carry out data analysis and forecasting projects. *Prerequisite(s): MA 251.
An overview of machine learning techniques using R. Topics include an introduction to programming in R, the use of nearest neighbor and naive Bayes algorithms, support vector machines, hidden Markov models, and market basket analysis as well as model evaluation and improvement. *Prerequisite(s): MA 251, and CS 113 or CS 121 or MDA 570.
Get ready to acquire some seriously marketable computer skills! A company’s biggest asset is their data and most companies’ databases use the Structured Query Language (SQL) to manage data. DAT 200 teaches students to extract data from a database, and DAT 320 adds to this by teaching students to design and build databases with plenty of progressively challenging assignments with the goal of preparing successful students to pass the Oracle® Certification Exam. Oracle is the most popular relational database in the world, and the national average salary for a database administrator is $89,626 (glassdoor.com, November 8, 2017). *Prerequisite(s): DAT 200.
Under the supervision of a faculty mentor, students use their knowledge of data analytics to complete a project contributing to research in an academic area or to solve a problem for a local business. Projects will involve data collection, data cleaning, data analysis, and reporting results both orally and in writing to a domain expert or business leader. *Prerequisite(s): Students must have completed 14 credits in the minor or permission of the instructor. Signature Learning Experience: Capstone. Register by Instructor.
This course will cover the required programming concepts for data analytics, taught in R or python as the DAT/DS faculty choose. This course will replace prerequisites of CS 113 or 121, in MDA courses. This course will be taught in the fall by adjunct George Lehman, Tim McDevitt, or Peilong Li.
If students also take the DAT 320/ MOA 520 course offered online over the summer they will additionally be prepared to pass the Oracle® Certification Exam.
At the completion of the program, graduates may also have the ability to earn the SAS Joint Certificate. Additional information and steps will be presented at the applicable time.
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