Kalie, MSA Class of 2019Students come to the Institute with a wide variety of academic backgrounds, but it’s essential to have a solid grasp of the underlying math and statistics before entry.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, PhD, etc.)

We accept applicants from a wide variety of academic majors. However, to be a competitive applicant, you will need to have successfully completed prerequisite courses prior to or concurrent with your application for admission to the MSA program.

Prerequisite Courses

Prerequisite courses for applicants include at least one, but ideally two semesters of college-level statistical methods, including substantive coursework covering regression analysis. To gauge whether courses you’ve taken previously (or are considering taking in the future) would serve as sufficient preparation, please compare their content against the topics/methods listed below.

It is advantageous for MSA applicants to be familiar with the following topics/methods:

  • Analysis of Variance (ANOVA)
  • Confidence Intervals
  • Correlation
  • Data Collection / Sampling
  • Eigenvalues / Eigenvectors
  • Gauss-Jordan Elimination
  • Hypothesis Testing
  • Least Squares Estimation / Normal Equation
  • Matrix Manipulation
  • Multicollinearity
  • Multiple Linear Regression
  • Normal & Binomial Distributions
  • Probability
  • Residual Diagnostics
  • Sampling Distributions / Central Limit Theorem
  • Simple Linear Regression
  • Solving Systems of Linear Equations
  • Variable Reduction through Eigenvalues
  • Variable Selection

If you are an undergraduate student currently enrolled at NC State, courses you might consider completing are:

  • ST 311 and ST 312 – Introduction to Statistics I and II
  • ST 371 and ST 372 – Introduction to Probability and Distribution Theory, and Introduction to Statistical Inference and Regression
  • If you have the prerequisites for it, then ST 430 – Introduction to Regression Analysis

If you already hold an undergraduate degree but do not feel that your past coursework provided sufficient preparation (or if you wish to have a refresher), you have a few options:

  • NC State’s Non-Degree Studies (NDS) program offers viable online options (e.g., ST 513-514).
  • We offer a self-paced online, non-credit course called Introduction to Analytics 2, taught by the Institute’s Dr. Aric LaBarr, through NC State’s Wolfware Outreach platform. This course is appropriate for those who have previously completed introductory statistics coursework and are looking to advance their knowledge.
  • We recognize comparable courses completed for credit and a grade from other accredited institutions as well.

Coding Experience

An additional requirement of MSA admission is the ability to code in one or more computer programming languages, especially those most relevant to the MSA program (Python, R, and SQL). Your ability could have been gained in formal coursework, or through work experience or independent study.

There are numerous online resources for enhancing coding skill, many of them free or at low cost; however, they are just the beginning. Think about what it was like to learn a new spoken language: it’s one thing to learn vocabulary, grammar, and syntax in the classroom, but engaging in conversation with a native speaker of that language is an entirely different (and much more nuanced) experience.

The same is true of learning to code. The most effective way to learn is by practicing coding — actually getting your hands dirty and applying what you learn in online modules to real datasets such as those available via Kaggle and Google. When you encounter difficulties or errors (and you will!), use resources such as Google and YouTube to find possible solutions.

Should you have questions about prerequisite courses or coding experience, contact MSA Admissions.

Dustin and Rocky, MSA Class of 2017