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Math Degree

Program Offerings

• Bachelor of science degree in Applied and Computational Mathematics
• Minors: Mathematics and Computational Statistics

What is Math?

Mathematics is a language and methodology for classifying, studying, and predicting patterns: patterns of quantity, of structure, of change, of logic. Applied mathematics is the use of mathematical language and methods to understand real-word phenomena and to solve real-world problems, while computational mathematics involves numerical and statistical methods better suited to computers to carry out.

Thus, an applied and computational mathematics degree gives students a firm understanding of mathematics and its applications to science and engineering in the 21st century. The degree provides students with a strong foundation of knowledge and skill in the core areas of analysis, differential equations, numerical methods and modeling. Students will also attain a basic understanding of probability, statistics, algebra and programming. The degree will prepare students to work in such areas as defense technology, business, robotics, public policy and biomedicine.

What does a mathematician do?

A mathematician is a problem solver. An applied mathematician uses mathematical theory and technique to critically analyze and solve problems in business, government, engineering and the social sciences.

Some duties that a mathematician might do in their career include:
• Analyze ways to schedule airline routes between cities
• Analyze the effects and safety of a new medication
• Analyze the aerodynamic characteristics of an experimental automobile
• Work in computer programming or software engineering
• Test weapons and defense systems
• Market research and big data
• Manage hedge funds or analyze financial markets

While an engineering or science degree focuses of developing theories and methods to solve a specific class of real-world issues and phenomena, mathematics provides a powerful method for posing and solving problems in general. As a result, a mathematics degree naturally complements an engineering degree: an engineering degree will get you a job; a mathematics degree will get you a promotion. As a result, mathematics is the most popular double major at SD Mines.

First-Year Courses

Computer science I & II
Calculus I & II
Composition

Course Curriculum

Link to Catalog

Internships and Co-ops

Through cooperative education (co-ops) and internships, School of Mines students have many opportunities to apply their education to "real world" work experiences. 77% of Mines graduates have at least one paid internship before they graduate, earning over $18 an hour. One example of a math student's internship was in Silicon Valley at imo.im, a startup company that was founded and funded by one of the first ten employees at Google.

Career Outlook

Starting salary for a 2017-18 SD Mines applied and computational math graduate was $60,333. The job placement rate for 2017-18 Mines applied and computational mathematics graduates was 92 percent.

Careers

A degree in applied and computational math prepares students for careers in:
• data analysis and statistics
• actuary and banking
• industrial processes, manufacturing
• market research, IT
• telecommunications
• aerospace, space exploration
• pharmaceuticals, drug trials
• agriculture
• military, weapons systems testing
• hedge funds, financial analysis

Employers

Mines applied and computational mathematics graduates go on to work for such companies as:
Bluestem Brands
Burns McDonnell
CHR Solutions
POET
Sammons Financial Group
Security First Bank
Microsoft
NISC
John Deere
Google
DocuTap
L-3 Communication
Computers Unlimited
Microsoft
Whitehat Security
FAST Enterprises
Mayo Clinic
U.S. Department of Agriculture

Math Research

Students have many opportunities to be involved in mathematical research, in collaborations with department faculty and in interdisciplinary teams with faculty and students from other departments. The primary research focus areas of our faculty are:
• numerical linear algebra and multilinear algebra
• parameter estimation, decision theory, and non-parametric statistics
• computationally intensive statistical methods
• time series forecasting, massive data streams
• combinatorial matrix theory and graph theory
• mathematical modeling, mathematical biology and ecology
• finite geometry and non-associative algebras
• complex analysis and formal power series theory
• celestial mechanics and astronomy
• mathematical pedagogy and history

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