MS-BIA/MBA Prerequisites

The MS-BIA/MBA dual-degree program requires the following prerequisite courses in business, mathematics and programming.


8-week Courses


One Day (Saturday) Courses

*Prerequisites to Applied Data Mining, BIA 6301

Rockhurst offers all prerequisite courses in an evening or weekend format. Students are permitted to complete equivalent coursework at other academic institutions and transfer it toward the program as well. All prerequisites must be completed with a final grade of C or better.

Most of these prerequisites are met with an undergraduate degree in business. Students who need to complete prerequisite courses may do so in tandem with their graduate work.

Questions? Contact the director of graduate business admission at 816-501-4632.

Course Descriptions

BIA 6201. Statistics and Machine Learning (2 credit hours)

This intermediate level class covers multiple and logistic regression methods including correlation, residual analysis, analysis of variance, and robustness. These topics are studied from a data analytic perspective using business examples. The class also explores multivariate models as they relate to problems encountered in data and text mining. 

Prerequisite: Introductory statistics and knowledge of R programming language.

BIA 6314. Databases for Analytics (2 credit hours)

This course details database design, normalization and query methods pertinent for analytics. Topics include relational databases, SQL, data warehouse architecture, data marts and data lakes. Further investigation includes cloud computing options, APIs and emerging database forms. Emphasis is placed on the use of these infrastructures and architectures for analytics.

Prerequisite: Introductory course in programming or computer science.

BIA 6311. Introduction to R (.5 credit hours)

This course is a one-day workshop on the fundamentals of the R programming language. Only students who do not have experience and proficiency in R need take this course.

Prerequisite: Introductory course in programming or computer science.

BIA 6312. Introduction to Python (.5 credit hours)

This course is a one-day workshop on the fundamentals of the Python programming language with emphasis on working with data frames (Pandas), arrays (Numpy) and visualization (Matplotlib).

Prerequisite: Introductory course in programming or computer science.

AC 4500. Principles of Accounting (3 credit hours)

This course provides a foundation for students with no prior accounting experience. Financial and management basics are included, but emphasis is placed on developing an overall comprehension of the accounting field. General topics include the preparation and interpretation of basic financial statements and the use of accounting information for managerial decision-making. Specific topics include the balance sheet, income statement, statement of cash flows, cost behavior, cost-volume-profit analysis, and relevant cost analysis.

FN 6010. Basics of Finance (0.5 credit hours)

An introduction to the area of corporate managerial finance, this course emphasizes comprehension of tools and methodologies available to the financial manager for decision making in such areas as capital budgeting, working capital management, capital structure and profit planning and control.

MK 6000. Essentials of Marketing (0.5 credit hours)

This one-day course introduces the student to the role of marketing in business and how to make informed decisions based on market analysis and research. Topics include the 4 P’s (product, placement, promotion, and pricing) of marketing, the marketing function, how to develop successful marketing strategies, and how to view and extract insights from market research.

EC 6002. Essentials of Economics (0.5 credit hours)

This one-day course covers the principles in macro and microeconomics, including how consumers and producers interact through supply and demand within the economy.  Students examine the structure of market behavior, performance in the marketplace and optimizing behavior regarding consumer demand.  Various analysis techniques are covered, including PEST (political, economic, social, and technological) and SWOT (strengths, weaknesses, opportunities, threats) analysis.