To be a competitive applicant, you should:

• Aspire to a career working with data and/or AI, in any industry
• Hold a bachelor’s degree, or be in your final year of undergraduate study
• Have a record of strong academic performance
• Have completed at least one, but ideally two or more college-level statistics courses, including substantive coursework covering regression analysis
• Have the ability to write code from scratch — what we consider to be an intermediate level of proficiency — in one or more languages, especially those most relevant to data science (e.g., Python, R, SQL)
• Thrive in a collaborative, team-based environment
• Demonstrate maturity, self-initiative and leadership potential
• Satisfy NC State’s English proficiency requirements

A Record of Strong Academic Performance

To be considered for admission, NC State’s Graduate School requires applicants to have an undergraduate GPA of at least 3.0 (on a 4.0 scale). If your undergraduate GPA is below a 3.0, you’ll need to provide evidence of your ability to succeed in an accelerated master’s program — such as strong performance in recent coursework that’s directly related to the MSA curriculum. Professional experience and relevant personal projects can also help us justify an admission offer in these cases.

Statistics Coursework

The MSA curriculum encompasses many statistical concepts and methods, and as a 10-month program, instruction occurs at a rapid pace. To ensure you’re ready to hit the ground running, we require admitted students to have completed foundational statistics coursework. 

We encourage MSA applicants to be familiar with a majority of the following topics/methods:

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

If you are an undergraduate student currently enrolled at one of the following institutions, you might consider completing some of these courses (depending on your major and prereqs):

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 options:

• We offer a self-paced online, non-credit course called Introduction to Analytics 2, through NC State’s Wolfware Outreach platform. This course is appropriate for those who have previously completed introductory statistics coursework and are looking to refresh and advance their knowledge.
• NC State’s Non-Degree Studies (NDS) program offers viable online options (e.g., ST 513-514).
• Past MSA students completed a variety of statistics-based courses through community colleges and universities local to them, as well as via online learning platforms such as Coursera and Udemy. Thousands of choices exist!

Computer Programming Proficiency

Do MSA alumni code from scratch on the job? In most cases, no — they’re leveraging AI tools. But in order to know whether the AI output is accurate, they need to understand what’s going on “under the hood.” That’s why we require admitted students to possess the ability to write code from scratch — what we consider to be an intermediate level of proficiency — in one or more languages, especially those most relevant to data science (e.g., Python, R, SQL).

One can acquire coding skills through formal coursework, work experience and/or independent study. There are numerous online resources for enhancing coding skill, many of them free or at low cost; for example, you might consider the Python Institute’s PCEP™ – Certified Entry-Level Python Programmer certification.

The most effective way to build coding proficiency is through hands-on practice. Consider completing a personal coding project using a real dataset, such as those available via Kaggle and Google. When you encounter difficulties or errors (and you will!), use AI tools and other resources (e.g., YouTube tutorials) to troubleshoot.