Before matriculating, applicants must have completed a bachelor’s degree from an accredited college or university and have a proven track record of strong academic performance. It is not uncommon for applicants to already hold advanced degrees (MS, MBA, and PhD). We accept applicants from a wide variety of academic majors. However, you will need to have successfully completed prerequisite courses prior to or concurrent with your application for admission to the MSA program.
An additional requirement of MSA admission is the ability to code in one or more languages, particularly as it relates to data science and analytics. Your ability could have been gained in formal coursework or through substantive experience coding. There are numerous online resources for enhancing coding skill, many of them free or at low cost.
Prerequisite courses for applicants include at least one, but ideally two semesters of college-level statistical methods, including substantive coursework covering regression analysis.
We recognize prerequisite courses completed for credit and a grade from accredited institutions. To gauge whether courses you’ve taken previously would serve as sufficient preparation, please compare their content against the sample course descriptions below. You may also use these descriptions to guide your selection of future courses.
If you are an undergraduate student currently enrolled at NC State, we strongly recommend that you complete one of the following course pairs:
- ST 305 – Statistical Methods and ST 430 – Regression Analysis
- ST 371 – Probability and Distribution Theory and ST 372 – Statistical Inference and Regression
If you already hold an undergraduate degree but do not feel that your past coursework provided sufficient preparation (or if wish to have a refresher), you might consider completing ST 513 and ST 514 through NC State’s Non-Degree Studies (NDS) program. For further information and instructions on how to apply, visit the NDS website.
Sample Course Descriptions
Statistics for Management I:
- Data Collection / Sampling
- Normal & Binomial Distributions
- Sampling Distributions / Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- Analysis of Variance (ANOVA)
- Simple Linear Regression
- Matrix Manipulation
- Solving Systems of Linear Equations
- Gauss-Jordan Elimination
Statistics for Management II:
- Multiple Linear Regression
- Variable Selection
- Residual Diagnostics
- Matrix Manipulation
- Least Squares Estimation / Normal Equation
- Variable Reduction through Eigenvalues
- Eigenvalues / Eigenvectors
Please send questions about prerequisites to MSA Admissions.