Fast Track Bachelor's Degree/Master's Degree in Computational and Quantitative Methods
Degree: Bachelor of Science, Master of Science
Major: Mathematics, Computational Quantitative Methods
Hours: 120 (undergraduate), 30 (graduate)
Mathematics-Computational and Quantitative Methods Fast Track Overview
Lamar University's Bachelor of Science in Mathematics/Master of Science in Computational and Quantitative Methods Fast Track program is designed to allow motivated undergraduate students to earn both degrees within five years. This is achieved by allowing you to take dual-credit courses during your senior year. Students who successfully complete these dual-credit courses will receive both the graduate credit and undergraduate credit.
This program is for anyone interested in using mathematical tools in data analysis, with applications in mathematics, statistics, finance, computer science or accounting courses. Mathematics and statistics professionals who are products of of CMQM are trained to work in several areas that involve the use of modern tools and technologies for analyzing large and high dimensional data, generating possible trends and using outcomes to make complex and informed decisions.
LU has a vibrant campus community. Our mathematics faculty are engaged in current research, and strive to engage our students in project-based learning. Students entering this program can expect direct contact with talented faculty who are interested in extending their students' knowledge. Students who graduate from the program will be well placed to work in a variety of careers in emerging fields. Applicants need to have a cumulative GPA of 2.5 (on a four-point scale). GRE scores are optional. International applicants must have an English proficiency score.
Mathematics-Computational and Quantitative Methods Courses You May Take
Linear Algebra: This course introduces and provides models for application of the concepts of vector algebra. Topics include finite dimensional vector spaces and their geometric significance; representing and solving systems of linear equations using multiple methods, including Gaussian elimination and matrix inversion; matrices; determinants; linear transformations; quadradic forms, eigenvalues and eigenvectors; and applications in science and engineering.
Introduction to Advanced Mathematics: This course provides introduction to logic and the basic methods of proof required to be successful in a proof oriented mathematics course. Students will study applications in basic set operations, relations, functions, cardinality, and the real number system to learn the basics of mathematics proofs.
Regression Analysis: Regression Analysis is considered the bedrock of statistical techniques for modeling and analyzing data. This course provides a rigorous discussion of simple linear regression analysis, theory of least squares, multiple regression models in matrix terms, multivariate analysis, theory of the general linear model, and nonlinear regression.
Advanced Statistical Methods: Statistical methods and reasoning, principles and applications of probability and statistics with emphasis on real-world data pertaining to data collection, organization, and analysis. Specifically, descriptive, and inferential statistical methods, probability distribution, permutation-based methods of inference, bootstrap confidence intervals, and the binomial exact test for proportions, confounding, randomization, and sampling variability, linear regression, and correlation. Statistical computing language and environment.
Advanced Machine Learning: Machine learning and statistical pattern recognition concepts that include cost functions, gradient descent, backpropagation, neural networks, natural language processing, sentiment analysis, chatbots, recommender systems, reinforcement learning, supervised learning and unsupervised, computer vision, text processing, and bioinformatics.
Career Paths for Mathematics-Computational and Quantitative Methods Fast Track
The study of the market reveals an increasing national and global demand for mathematics and statistics related professionals. According to the Occupational Outlook Handbook, general employment in mathematics occupations is projected to increase 29% from 2021 to 2031, significantly faster than the average for all occupations; this increase is expected to produce approximately 82,000 new jobs over the decade. The study also projects the following growth rates: 36% for data scientists, 31% for mathematicians and statisticians, 23% for operation research analyst, 21% for actuaries, and 9% for financial analyst (Bureau of Labor& Statistics, 2022).
Primary Careers
Data analyst, data scientist, risk analyst, financial analyst
- Technology
- Public health
- Business
- Insurance
- Education
- Manufacturing companies
- Corporate and small businesses
- Government agencies
- Colleges and universities
- Research institutions and facilities