BUS 605: Fundamentals of Business Analytics (Financial Data Analytics)

Business analytics refers to techniques used by enterprises to gain insights and make better decisions using data. It has applications in all the functional areas of an enterprise including accounting, finance, marketing, operations and strategic planning. It is essential for students to develop the skills to ask the right questions and solve them using the right statistical tools. This class provides a framework for developing a business mindset called SOAR analytics. Following the SOAR analytics, students will be guided to:  

1. Specify the question relevant to any business entity based on observations

2. Obtain the right data to ensure a methodical analysis of the problem identified 

3. Analysis of the data using the right statistical methods and metric 

4. Report of results in a practical manner for business managers and stakeholders

Specifically, this course provides a complete set of statistical tools for financial analysts. Students will understand a fundamental balance between financial risk theory and applications using R. The course supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. Finally, the class will focus on how to report analyzed data.

Upon completing the course, students will be expected to 

a. Identify specific challenges of a business when given access to data regarding that business

b. Select the type of data and variables capable of solving the identified business problem 

c. Implement analytical models in the software tools (excel, tableau, and R) to gain insights.

d. Communicate the output from analytical models in a practical manner to managers and consumers

e. Make data-driven decisions to optimize the business process and solve business problems.

f. Enhance their understanding of financial data and today's financial markets.

g. Understand linear time series analysis and different approaches to calculating asset volatility/various volatility models

h. Be acquainted with quantitative methods for risk management, including value at risk and conditional value at risk

i. Be familiar with econometric and statistical methods for risk assessment based on extreme value theory and quantile regression

The syllabus is found here.