Financial Analytics

The Master of Science in Financial Analytics prepares students for success in the financial professions by focusing on analytical and algorithmic techniques in financial analysis. The program is open to students with strong quantitative and analytical skills, regardless of their undergraduate major.

Students may enroll on a full- or part-time basis, but course availability is greatest during the fall and winter semesters. The program usually can be completed within three semesters of full-time study  Most students begin the program in Fall.  Admission in January and May may also be possible.  

Goal 1:  Students will demonstrate analytical skills in solving problems.

Objectives:  MS Financial Analytics students will have the ability to:

  1. Apply Quantitative and Analytical knowledge in financial analysis.
  2. Evaluate Banking, Insurance, and Fintech's role in the modern financial system.
  3. Apply Python programming to financial data processing and modeling of financial data. 
  4. Manage corporate and portfolio risk exposures.
  5. Value assets and financial securities using quantitative tools.
  6. Evaluate managerial decisions concerning financial policy.​
  7. Apply Quantitative portfolio techniques to construct and manage the client’s portfolio.

Goal 2:  Students will be persuasive and/or informative communicators.

Objective 1:  MS Financial Analytics students will be able to convey finance knowledge through data visualization and effective communication.

MS in Finance Admission Prerequisites

  • Mathematics admission prerequisite. Calculus is not required for admission to the MS in Finance.  However, applicants who wish to pursue careers in investments or risk management, as well as those who wish to earn Chartered Financial Analysts (CFA) credentials, are strongly recommended to satisfy the Mathematics admission requirement with a college level Calculus course. 

MS in Financial Analytics Curriculum

Foundation Courses 1
Required:0-12
ACC 505
Devel & Interp Financial Info
BE 530
Econ Analysis: Firm & Consumer
DS 520
Applied Statistical Modeling 3
or BA 510
 Introduction to Business Analytics
FIN 531
Fin Fundament & Value Creation
Core Courses
Required:21
BA 530
Programming and Data Structures with Python
FIN 651
Investment Procedures, Analysis & Management
FIN 652
Derivatives & Risk Management
FIN 653
Asset Pricing and Portfolio Management
FIN 654
 Banking, Insurance, and Fintech
FIN 656
Fixed Income Securities
FIN 658
Algorithmic Finance Using Python
Electives
Select one to three courses (3-9 credits). Must include at least one FIN course:3-9
ACC 608
Financial Statement Analysis
DS 630
Applied Forecasting with Python
DS 633
Machine Learning for Business Intelligence
FIN 581
Advanced Corporate Finance
FIN 650
Corporate Valuation & Strategy
FIN 655
International Financial Mgt
FIN 657
Investment Fund Management
BA 682
Experiential Project 2
BA 690
Graduate Research 2
BA 691
Graduate Seminar 2
BI 500
Business Internship 2
Total Credit Hours30-36
1

Previous equivalent undergraduate or graduate coursework may qualify students to waive any of the foundation courses. Students may complete the MS Financial Analytics in as little as 30 credit hours if they have completed at least two equivalent foundation courses, with a converted grade of "B" or better, before admission.  Otherwise, students complete remaining required foundation courses in the program for a total of 36 credit hours.

2

A maximum of 3 credit hours on any combination of BA 682, BA 690, BA 691, and BI 500.  Requires Department of Accounting & Finance Chair approval.

3

If an admitted student has not already fulfilled this requirement, he/she/they is/are recommended to take DS 520.

 

Learning Goals

Goal 1:  Students will demonstrate analytical skills in solving problems.

Objectives:  MS Financial Analytics students will have the ability to:

  1. Apply Quantitative and Analytical knowledge in financial analysis.
  2. Evaluate Banking, Insurance, and Fintech's role in the modern financial system.
  3. Apply Python programming to financial data processing and modeling of financial data. 
  4. Manage corporate and portfolio risk exposures.
  5. Value assets and financial securities using quantitative tools.
  6. Evaluate managerial decisions concerning financial policy.​
  7. Apply Quantitative portfolio techniques to construct and manage the client’s portfolio.

Goal 2:  Students will be persuasive and/or informative communicators.

Objective 1:  MS Financial Analytics students will be able to convey finance knowledge through data visualization and effective communication.