To be eligible for admission, a student must have a B.S. degree with a minimum GPA of 3.0 on a 4.0 scale and have the following background (typically obtained through a BS in a STEM field):
- Calculus: Derivatives, integrals, applications
- Linear Algebra: Vector spaces, dot products, Euclidean norm, matrices
- Probability and Statistics: Random variables, probability distributions, basic statistics
- Programming: Basic programming constructs, writing and debugging programs, iteration, recursion, arrays, lists
- Data Structures and Algorithms: Basic data structures, search and sort, algorithm analysis
Applicants lacking this background may take the Refresher boot camp to acquire it.
- Be able to acquire, clean, and manage massive data sets.
- Play an analytical role in your company where you design, implement, and evaluate advanced statistical models and approaches for application to your company’s most complex problems.
- Be able to provide econometric and statistical models for a variety of problems including projections, classification, clustering, pattern analysis, sampling and simulations.
- Research new ways for predicting and modeling end-user behavior as well as investigating data summarization and visualization techniques for conveying key applied analytics findings.
- Apply modern artificial intelligence and deep learning methods to complex prediction and recognition tasks.
