Data Analytics Minor

Courses

FOR REQUIRED COURSES AND DEGREE PLANS, VISIT THE OFFICIAL UNIVERSITY CATALOG. This is a list of courses offered by Western State Colorado University. To ensure the courses you need are offered during the current semester, please visit the current university catalog at http://www.western.edu/catalog. To determined the courses required for your major, check the "Majors and Minors" tab for your area of study.

 CS 190 - COMPUTER SCIENCE I (3 credits)

An introduction to software development. Students develop applications using modern programming languages and techniques. Emphasis is placed on good software engineering practices for problem analysis, program design, documentation, testing and debugging. The course uses an industry standard programming language.

 CS 303 - MACHINE LEARNING (3 credits)

A study of computer systems that learn from experience. Classroom exercises include the building of systems that learn and adapt using real-world applications. Topics covered include decision trees, concept learning, neural networks, reinforcement learning, linear and non-linear models, clustering, validation, and feature selection.

Prerequisites: CS 190 and MATH 213.

 ECON 202 - MICROECONOMICS (3 credits)

The theory of microeconomics makes use of the tools of marginal cost-benefit analysis to provide a framework for the economic analysis of decision-making. The focus is on the choices of individual firms and consumers, and the resultant outcomes in individual markets. The social implications of the functioning of competitive markets are examined, as well as the causes of market failure and the potential roles of government in correcting them. Prerequisite: ACT math score of 19 or above; SAT math score of 460 or above; pass MATH 099; or Accuplacer Elementary Algebra test score of 85 or higher, or college-level math requirement with a minimum grade of "C-."

 ECON 216 - STATISTICS FOR BUSIN ECONOMICS (3 credits)

An introduction to descriptive statistics and statistical inference, with application in business, including hypothesis testing, confidence intervals, and simple regression analysis. Prerequisite: MATH 140, MATH 141, or MATH 151 with a minimum grade of "C-".

 ECON 316 - ECONOMETRICS (3 credits)

The application of advanced statistical methods and modeling to an empirical understanding of economic issues. Combines elements of statistical reasoning with economic theory and provides an excellent opportunity to combine concepts learned in previous economics courses. Topics covered include multiple regression analysis, model specification, dummy variables, multicollinearity, heteroscedasticity, autocorrelation, limited dependent variables, simultaneity, time series, forecasting, and methodological issues. Prerequisites: ECON 201or ECON 202; and ECON 216 or MATH 213. 

 MATH 151 - CALCULUS I GMA1 (4 credits)

A study of differential calculus, including limits, continuous functions, Intermediate Value Theorem, tangents, linear approximation, inverse functions, implicit differentiation, extreme values and the Mean Value Theorem. This course also introduces Integral calculus including anti-derivatives, definite integrals, and the Fundamental Theorem of Calculus. Prerequisite: ACT math score of 27 or above; SAT math score of 610 or above; MATH 141 with a minimum grade of "C-"; or Accuplacer university-level mathematics test with a score of 95 or above. GT-MA1

 MATH 213 - PROBABILITY STATISTICS GMA1 (3 credits)

An introduction to descriptive statistics, probability concepts, and inferential statistics. The topics for the course include presentation of data, counting principles, probability rules, and discrete and continuous probability distributions. Prerequisite: MATH 141 with a minimum grade of "C-,"' or Accuplacer College-Level Mathematics test score of 85 or above; or instructor permission.

 MATH 220 - INTRO TO ADVANCED MATHEMATICS (3 credits)

Students develop and use elementary logic and set theory to construct deductive proofs with relations, functions, and some algebraic structures. Topics include indexing, equivalence relation theory, and cardinality. Prerequisite: MATH 151 with a minimum grade of "C-."

 MATH 251 - CALCULUS II (4 credits)

Topics include techniques of integration, area computations, improper integrals, infinite series and various convergence tests, power series, Taylor's Formula, polar coordinates, and parametric curves. Prerequisite: MATH 151 with a minimum grade of "C-."

 MATH 260 - APPLIED LINEAR ALGEBRA (3 credits)

A course in the techniques and applications of linear algebra. The core topics include solving systems of linear equations, eigenvalues and eigenvectors, matrix decomposition, the pseudoinverse and least squares approximations, and the singular value decomposition. The theory is supplemented with extensive applications and computer programming. Prerequisite: MATH 141.

 MATH 313 - STATISTICAL MODELING AND SIMULATION (3 credits)

A study of statistical techniques used to model and simulate stochastic processes. The core topics include linear and nonlinear multivariate models, generalized additive models, time series models with auto-correlated error, and mixed effects models. Emphasis is placed on computational techniques appropriate to large data sets and data visualization. Prerequisites: ECON 316, MATH 260, CS190. 

 MATH 451 - ANALYSIS I (3 credits)

An introduction to the theory of calculus. Topics include the usual topology of the real's, sequences, limits, continuity, differentiation, and Riemann integration. Prerequisites: MATH 220 and MATH 252 with minimum grades of "C-."

 MATH 471 - ABSTRACT ALGEBRA I (3 credits)

An introduction to the theory of groups and rings. The fundamental group properties and concepts including cyclic groups, subgroups, direct products, symmetric groups, cosets, normal subgroups, and the group homomorphism theorems are discussed. Prerequisite: MATH 220 with a minimum grade of 'C-.'

 MATH 495 - SENIOR SEMINAR (2 credits)

A Capstone Course for the Mathematics Standard Major and for the Secondary Licensure Emphasis. Each student selects an area of interest, researches the selected area, generates a reference list and research paper, and presents the paper to a seminar of faculty and students. Prerequisites: MATH 360 and either MATH 451 or MATH 471.

The future belongs to those who can handle the data.  Nearly every human endeavor is now measured in great detail and these data are used to guide businesses, research, government, industries, and medicine.  While it is easy to collect these data there are few who can extract useful information.  The data analytics minor provides the skills to find the true story and make useful predictions.

What You Will Learn

Data analytics involves learning both the techniques of data analysis and the context in which the data are generated.  We use statistics, statistical modeling, data visualization, computer science to massage data and reveal knowledge.  Moreover we use economics, biology, sports science, and environment science to provide a context for interpreting the results.

You will learn to:

  • Build models to discover relationships between data and questions.
  • Analyze models to distinguish between causal agents and spurious correlations.
  • Visualize data to discover hidden patterns and communicate these patterns to others.
  • Massage and manipulate data into a form useful for analysis.
  • Harness computers to find structures hidden in vast databases.

 

Beyond the classroom:

Data are best understood within a context.  We use data collected by regional researchers, governmental agencies, and land management agencies to both illustrate the techniques and to give valuable insights to our collaborators.  There are numerous opportunities for internships and collaborations with regional data consumers.

After graduation:

Data analytics is one of the fastest growing professions in the country.  The bureau of labor statistics lists quantitative professions as growing "Much faster than average" and Forbes says "The growth rate in marketing-related analytics hires is what’s eye-popping – up 67% over the past year, and 136% over the past three years."  There will be a role for data analytics in nearly every career on may choose so one can work in any field he or she chooses and expect to carve out a valuable career drawing insights from data.

Next Steps

If you're interested in Western's Data Analytics Program, we invite you to take the next steps towards becoming a part of the Mountaineer family. 

Share your interest with friends and family: 
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  2. Get more information about the program.
  3. Schedule a campus visit so you can meet professors, see the beautiful Gunnison Valley, and find out if Western is the perfect school for you.
  4. Start the online application process - apply online now.
  5. Find scholarships, grants, or financial aid that match your interests and situation.

Faculty & Staff

Faculty

Andrew G. Keck
Professor of Mathematics
B.A., DePauw University; M.Phil., University of Utah; Ph.D., University of Montana.
Phone: (970) 943-2802
Office Location: Hurst Hall 212