This course will cover how to access, structure, format, manipulate and archive data using R and SAS. It will include topics in data inputting, merging files, cleaning data, data summary, descriptive statistics, running procedure statements, graphical presentation of data, loops, if/then statements, and creating your own scripts and functions that extend the language. Prereq.: MATH 020 or equivalent college algebra course, knowledge of basic software tools including word processing, email, Internet browsers, and presentation software.
An introduction to the concepts of probability that form the foundation for analytics practice. Descriptive statistics, data visualization, univariate discrete and continuous probability distributions, confidence intervals and one-sample hypotheses testing. Applies R and/or SAS skills.
Data Mining and Generalized Linear Modeling - The emphasis will be on data analysis, statistical assumptions, and diagnostics. Topics include: Linear Regression, Logistic and Probit Regression, CART, Neural Networks, Association Rules, Clustering, Generalized Linear Models, Models for Continuous Data, Models for Binary Data, Models for Polytomous data, Log-Linear Models, Conditional Likelihoods, and Gamma Regression.
In this course, students will apply description, predictive, and prescriptive data analysis methods learned in previous cases to new cases. Students will learn to effectively manage long-term data analysis projects within diverse teams through a complete data analytics project lifecycle and compellingly communicate outcomes through writing and oral presentations which include appropriate use of data visualizations.