Complex systems, massive datasets and computation-driven decisions define many of today’s most critical industries. The Master of Science in Applied Mathematics and Statistics (Non-Thesis) at Colorado School of Mines prepares you to meet those challenges as a rigorous mathematical and statistical problem solver that organizations can rely on when the stakes are high.
At Mines, applied mathematics and statistics are embedded in real-world domains where quantitative insight drives progress: data systems, subsurface modeling, critical minerals, advanced materials, space resources, infrastructure and construction engineering and environmental monitoring. The skills gained at Mines become a career multiplier. Graduating with a Master of Science in Applied Mathematics and Statistics (Non-Thesis) means more than mastering theory—you’ll be prepared to deliver analysis that informs strategy, strengthens resilience and stands up to real-world scrutiny.
Program Detail
The Master of Science in Applied Mathematics and Statistics (Non-Thesis) offers two paths to specialization and two approaches to complex problems, both grounded in mathematics but addressing different types of questions.
Both the computational and applied mathematics and statistics specialties of this degree option require 30 credits of coursework. The curriculum structure consists of a set of required courses, a pair of math electives and general elective courses that serve to supplement your technical interests.
Computational and Applied Mathematics Specialization
The Computational and Applied Mathematics option prepares you for careers in engineering simulation, scientific computing, physics-based AI, aerospace, energy systems and computational modeling. The insights you gain enable you to approximate, simulate and compute solutions using mathematics and high-performance computing. Your tools include advanced linear algebra, partial differential equations and numerical methods.
These enable mathematical problem solvers to describe how quantities change over space and time, such as heat flow, wave propagation, fluid movement or stress in materials. The insights drive the computational tools that turn equations into working simulations and machine-learning models with thousands or millions of variables. Imagine one day solving challenges such as: How will this reservoir, structure or physical system behave over time? How do we design or optimize a system under constraints? How do we simulate reality accurately and efficiently on a computer?
Statistics Specialization
The Statistics option prepares you for careers in data science, machine learning, analytics, risk analysis, environmental statistics and AI-enabled decision making. The insights you gain enable you to learn from data, quantify uncertainty and make reliable predictions, especially when the underlying system is complex, noisy or only partially understood.
Your tools include linear models, mathematical statistics and statistical learning. They explain why patterns occur and connect statistics to modern machine learning, with a focus on prediction, model selection and overfitting avoidance. Imagine one day solving questions such as: What patterns or signals are hidden in this data? How certain are we about this prediction or decision? How do we build models that generalize well to new data?