Data Analytics

An introduction to software development taught in Python. Topics include control structures, I/O, functions, strings, lists, files, other data structures and basic algorithms that use them. Emphasis is placed on good problem-solving practices, testing and debugging.

A study of computer systems that learn. Topics include decision trees, concept learning, neural networks, reinforcement learning, linear and non-linear models, clustering, validation, feature selection, support vector machines and hidden Markov models with applications to the arts and sciences. Prerequisite: CS 190 with minimum grade of “C-”.

An intensive study of big data and informatics applications for digital data. Topics include text analysis using classic works and social media, numeric analysis using economic and scientific data and symbolic analysis using genomic data. Emphasis is on programming solutions to complex problems. Prerequisite: CS 220 and MATH 151 with minimum grade of “C-”.

A programming intensive introduction to distributed computing with attention to applications in machine learning and data analysis. Topic includes distributed sequential analysis methods, distributed Markov model-based methods, and distributed support vector machine-based methods. Prerequisite: CS 303 or CS 365 with a minimum grade of “C-”.

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 500 or above; pass MATH 099; or Accuplacer Advanced Algebra and Functions test score of 235 or higher, or university-level math requirement with a minimum grade of “C-.”

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-.”

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.

A course in the use of statistical techniques to draw knowledge from data. Topics include exploratory data analysis, descriptive statistics, t-procedures, ANOVA, chi squared procedures, regression, and non-parametric tests. Statistical software is used extensively to analyze real data sets. Prerequisite: ACT math score of 27 or above; or SAT math score of 630 or above; or MATH 141 with a minimum grade of C-; or instructor permission.

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.

A study of statistical techniques used to model, simulate and understand complex data. The core topics include linear and nonlinear multivariate models, generalized additive models, tree-based models, and mixed effects models, as well as techniques for selection and regularization of models. Emphasis is placed on computational techniques appropriate to large data sets and data visualization. Prerequisites: MATH 213 or ECON 216; and MATH 260; and CS190.