Industrial mathematics and statistics is designed for students with a strong interest in applying a wide range of skills in mathematics, statistics and computer science to problems encountered in "real world" settings. In addition to coursework in these areas, students will obtain expertise in an area of application in which they are interested. They can seek employment in a wide range of fields including statistics, computer science and applied mathematics. According to a 1993 survey of recent graduates performed by the National Science Foundation, while only 12 percent of the graduates in the mathematical sciences obtained degrees with a concentration in applied math or statistics (82 percent had degrees in general math), 63 percent of those employed in nonacademic jobs reported that the two jobs they spent the majority of their time on were computer applications and applied research. This degree is designed to enhance a student’s marketability by giving them expertise in mathematics, statistics and computer science.
The curriculum provides the critical skills and knowledge needed to apply sophisticated tools from both statistics and mathematics to industrial and scientific problems. IMS is concerned with the mathematical, statistical and computer modeling of various physical, biological and social processes.
Graduates are trained to work in business, industry and the government or to pursue a graduate degree in any of the mathematical sciences. IMS is vital to our economic competitiveness and is critical to the development of our increasingly scientific/technological society. IMS is built on a foundation of differential and integral calculus, differential equations, applied probability and statistics.
The mathematical tools encompass linear algebra, numerical analysis, continuous models rooted in differential equations, discrete models linked to finite mathematical structures and Markov processes. Scientific computing extends the rudiments of programming into data visualization, development of algorithms and selected topics using high-level languages.
Statistical topics especially relevant to industrial and scientific applications include design and analysis of experiments, statistical models, sequential analysis, reliability models and time series analysis. These statistical methodologies are grounded in fundamental concepts of statistics and probability such as discrete and continuous probability distributions, estimation and hypothesis testing and exponential family models.
Everything from coal efficiency to biometrics identification systems to predicting lung disease are primarily being researched by mathematicians and statisticians at WVU, and you could be a part of that research as well!