Busi Analysis and Computing (BUAL)

Busi Analysis and Computing (BUAL)

BUAL 2305  Business Statistics  3 Credits  
Department: College of Business  
This course covers descriptive and inferential techniques for business and economic decision-making. Topics which are included in this course are the collection, description and analysis and summarization of data; probability; discrete and continuous random variables; the binomial and normal distributions; tests of hypotheses; estimation and confidence intervals; linear regression; and correlational analysis. Microsoft Excel will be used to analyze data throughout the course.
Prerequisite(s): MATH 1324  
Restriction(s):

Students with a class of Freshman may not enroll.

  
Grade Mode(s): Standard Letter, Registrar do not use FN, Registrar do not use FS  
BUAL 3330  Data Analytics in Business  3 Credits  
Department: College of Business  
This course will provide the student the opportunity to learn about data analytics as it applies to business. Data Analytics is a topic of increasing importance for many organizations as the need for data-driven insights and recommendations grows throughout the business industry. This course gives students an overview of data literacy and quantitative skills. This provides students practical experience with different types of data and the tools that are used to analyze it. These skills are essential for success in data-driven fields of study and in the workplace. Various software tools will be introduced to enhance students' hands-on capabilities.
Prerequisite(s): ACCT 2301 and MISY 1373  
Grade Mode(s): Standard Letter, Registrar do not use FN, Registrar do not use FS  
BUAL 5380  Managerial Decision Making  3 Credits  
Department: College of Business  
This course promotes tools for effective decision-making using a variety of techniques. Students learn to apply analytical methods to the processes of data collection, presentation, assessment and interpretation. The course emphasizes quantitative and statistical methods and includes topics such as correlation, regression analysis, data mining and model optimization.
Restriction(s):

Undergraduate level students may not enroll.

  
Grade Mode(s): Standard Letter, Registrar do not use FN, Registrar do not use FS