Courses
GDAT-501 Intro to Stat for Data Sci (3)
The course covers statistical principles and methods for data analysis. In addition to a survey of traditional parametric procedures, the course covers modem and robust statistical methods that address deficiencies in traditional methods and allow the analyst a more accurate understanding of data. Topics include numerical summaries, probability and sampling distributions, estimation and hypothesis testing, correlation and regression analysis of variance, and the analysis of frequency data. The course emphasizes data analytic skills, statistical computing in R, and the ability to interpret and communicate data analytic results.
Attributes: TGDA
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-502 Found Working with Data (3)
The statistical analysis of data, from the acquisition of data through the development of models for those data, relies on a number of database and mathematical concepts. This course will consider a number of topics that underlie statistical analysis, including scraping of data from various sources, cleaning and imputing data, distributions and probability arguments that support various statistical methods, concepts of minimization and summation, and various matrix methods for computation. The use of technological tools for computation will be emphasized.
Attributes: TGDA
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-511 Exploratory Data Analysis (3)
This course covers data mining (statistic and graphical methods to explore and communicate the underlying structure in data) and statistical/machine learning (statistical and graphical methods for learning from data about underlying relationships). The course covers plotting systems in Rand R packages and functions for exploratory analysis, data mining, and statistical learning. The course emphasizes data analytic skills, statistical computing in R, and the ability to interpret and communicate data analytic results.
Attributes: TGDA
Pre-requisites: GDAT-501 C OR GMGT-576 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-512 Predictive Modeling I (3)
Basic concepts and method in predictive modeling and machine learning are covered for classification (dichotomous outcomes) and prediction (continuous outcomes). Modeling techniques including k-nearest neighbors, classification and regression trees (CART), and linear and logistic regression will be covered, with applications. Additionally, this course will introduce path analysis. The course will also cover the partitioning of data into model-building, validation, and test data, as well as the evaluation of predictive models. The course includes hands-on work with R.
Attributes: TGDA
Pre-requisites: (GDAT-501 C OR GMGT-576 C) AND GDAT-511 Y C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-514 Introduction to Databases (3)
This course presents an overview of database organization and management. Topics include database organization, querying techniques, data integrity, data extraction and manipulation, big data and data warehouses. Students work with databases in multiple environments, including PCs, networks, and the WWW, and design and develop small database applications using Microsoft Access, MySQL with PHP and Oracle Apex.
Attributes: TGDA
Pre-requisites: GDAT-501 C OR GMGT-576 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-515 Data Visualization (3)
Methods for visualizing data for analytics. Topics include data preparation (merging data, dealing with missing data), statistical and graphical distribution analysis, graphing time series data, multivariate plots, treemaps for hierarchical data, and specialized visualizations for data. The development of visual dashboards for applications will be emphasized.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-531 Education Analytics (3)
Leaders in education work in systems that are increasingly concerned about the collection, interpretation, analysis, and presentation of data as it relates to student learning. The course will provide a broad overview of the field, examining the historical context, logic and methods of analytics as applied to teaching and learning. Topics include the Elementary and Secondary Education Act, data and assessment literacy, multiple measures of data, data equity, and the use of various types and sources of educational data by learning analytics teams.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-532 Healthcare Analytics (3)
The convergence of Data Science with Healthcare has resulted in a proliferation of new data structures, standards, and challenges unique to the Heath Care community. This course will examine the data structures and practices unique to the Health Care field. Topics will include a historic overview of health data in the United States, the sweeping changes resulting from the Affordable Care Act, the evolution of metrics defining ?meaningful use? and outcome based accountability, electronic health records (EHR), privacy, security, and ethical challenges stemming from new technologies, the spread and impact of national health information exchange (HIE) networks, the growth of patient centered care, and fundamental barriers to implementing new IT strategies throughout the health sector.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-613 Predictive Modeling II (3)
An introduction to the use of multilevel models for analyzing clustered or hierarchically structured data that are common in fields such as healthcare and education. Topics include an introduction to multilevel analyses, random intercept and slope models, 2 and 3 level models, hypothesis testing, model assessment, longitudinal data, and generalized hierarchical models for dichotomous response/dependent variables. Students will apply the methods to real data from studies in education, healthcare and the social sciences.
Attributes: TGDA
Pre-requisites: GDAT-502 C AND GDAT-512 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-621 Nonparametric Statistics (3)
This course is fundamentally a survey of nonparametric procedures for hypothesis testing in 1 and 2?sample designs, including rank?based tests of location, rank?based correlations, nonparametric regression, and analysis of contingency table data through goodness of fit and likelihood ratio tests. The course also covers other procedures whose main concern are the limitations associated with parametric statistics, such as robust and resampling procedures. The course stresses statistical computing and data analysis in the learning of nonparametric statistics, as well as their proper use and interpretation. All statistical computing is done in R, but prior experience with R is not assumed.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-622 Statistics of Networks (3)
This course is an introduction to the statistical analysis of networks; the structure of network connections introduces a number of unique statistics to networks. Beginning with an introduction to graph theory, it will look at the representation of networks, appropriate descriptive statistics for networks, issues related to sampling networks, and how networks can be compared and modeled. The R statistical environment will be used throughout the course, although no previous experience with R is assumed. Specific topics to be examined include social network analysis, analysis of control points in flow networks, and the use of networked data in educational assessment.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-623 Textual Analysis (3)
This course will cover the basics of textual analysis, including the collection of textual data from various sources (focus groups, interviews, web-sources such as Twitter or blogs, etc.), and the analyses of that data, including methods such as analytic induction, content analysis, network-based tools, sentiment analysis, and the like.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-624 Web Analytics (3)
This course introduces students to a cross-section of qualitative, quantitative, and industry related techniques used to measure and evaluate audiences using interactive media. Topics covered include: fundamentals in research design, measurement, data collection, and analysis; the design and execution of surveys, focus groups, content analyses, among other primary research methods; and industry applications for media research including analyzing web metrics to evaluate the success of online public relations and advertising campaigns, and how to apply these analytics to make strategic decisions for business success.
Attributes: TGDA
Pre-requisites: GDAT-511 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-640 Practicum I (3)
GDAT 640 is designed to provide candidates the opportunity to apply their data science skills to (relatively) large real-world problems. This course consists of choosing a project, choosing and arranging for access to data (including any Institutional Review Board requirements), establishing a relationship with a mentor for the project, and acquiring appropriate data.
Permission of the instructor is required to register.
Attributes: TGDA
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: GraduateGDAT-641 Practicum II (3)
GDAT 641 is designed to provide candidates the opportunity to apply their data science skills to (relatively) large real-world problems. This course consists of appropriately analyzing the data and effectively communicating the results of the analysis.
Attributes: TGDA
Pre-requisites: GDAT-640 C
Restrictions: Including: -Major: Applied Data Science, Management Graduate -Level: Graduate