IAA 621 Statistical Computing 3

Statistical methods requiring significant computing or specialized software. Simulation, randomization, bootstrap, Monte Carlo techniques; numerical optimization. Extensive computer programming involved. This course does not cover the use of statistical software packages.

IAA 622 Complex Data Analysis 3

Methods for modeling and understanding complex data. Topics include linear regression models for sparse and high dimensional data sets, nonlinear models, tree-based methods, and clustering methods.

Prerequisites: Permission of instructor.

Notes: Same as STA 622.

IAA 623 Categorical Data Analysis 3

Methods for analyzing dichotomous, multinomial and ordinal responses. Measures of association; inference for proportions and contingency tables; generalized linear models including logistic regression and loglinear models.

Prerequisites: STA 602 or permission of instructor.

IAA 689 Capstone Project in Advanced Data Analytics 3

Capstone course. Students work with local industries and nonprofit organizations to solve important data science problems under the supervision of a mentor.

IAB 620 Introduction to Bioinformatics 3

The class will introduce concepts and methods to analyze biological data including DNA sequence data, genome assembly and annotation, DNA sequence comparison, phylogeny construction and protein structure analyses.

IAB 621 Bioinformatics 3

A variety of concepts required to analyze biological data will be discussed. Sample topics include biological databases, sequence alignment, ontologies, reproducible science, etc.

Prerequisites: IAB 620 or permission of instructor.

IAB 622 Advanced Bioinformatics 3

Understanding key concepts and advanced tools in bioinformatics. Managing, manipulating, and analyzing biological data for genomics, haplotypes, data mining, transcriptomics, biological databases and ontologies, and hypothesis testing.

Prerequisites: IAB 620 or permission of instructor.

IAB 689 Capstone Project in Bioinformatics 3

Capstone course. Students work with local industries and nonprofit organizations to solve important data science problems under the supervision of a mentor.

IAC 620 Algorithm Analysis and Design 3

An examination of topics in algorithm design and analysis including sequential algorithm design and complexity analysis, dynamic programming, greedy algorithms, and graph algorithms. Also covers selected advanced topics from NP-completeness; approximation, randomized, parallel, number-theoretic algorithms; Fast Fourier Transform; computational geometry; and string matching.

IAC 621 Data Science 3

Problem-based learning introduction to data science, including programming with data; data mining, munging, and wrangling; statistics, analytics, and visualization towards scientific, social, and environmental challenges.

IAC 622 Big Data and Machine Learning 3

Big data definitions and characteristics, computing environment for big data management and processing, machine learning models and algorithms, and scaling up machine learning (high dimensionality reduction).

IAC 689 Capstone Project in Computational Analytics 3

Capstone course. Students work with local industries and nonprofit organizations to solve important data science problems under the supervision of a mentor.

IAH 630 Fundamentals of Health Informatics 3

Introduction to healthcare data, data systems, and different kinds of applications of analytics for health care, including clinical and research applications.

Notes: Students cannot receive credit for both IAH 630 and CSC 630.

IAH 631 Artificial Intelligence in Health Care 3

Problem-based learning with Artificial Intelligence approaches of data science, data mining, statistics, and machine/deep learning, directed towards solving quantitative problems in the domain of Health Care Informatics.

Notes: Students cannot receive credit for both IAH 631 and CSC 631.

IAH 689 Capstone Project in Health Informatics 3

Students work with local industries and nonprofit organizations to solve important data science problems under the supervision of a mentor. Students develop a portfolio and give a presentation to program faculty and students reflecting on their experiences using informatics and analytics in a specific work situation.

IAL 620 Text Mining and Natural Language Processing 3

Students collect and analyze unstructured text data using web/API scraping methods, and then analyze their corpus using text mining and natural language processing. Additionally, students conduct a survey of relevant issues pertaining to privacy rights and intellectual property rights for web scraping and text mining methods.

Prerequisites: Admission to major or permission of instructor.

IAL 621 Content Analysis for Social Network Data 3

Students collect social network data to analyze trends (both #hashtag trends and organic/non-tagged trends), focusing specifically on audience engagement and comments. Additionally, students conduct a survey of relevant issues pertaining to privacy rights and intellectual property rights for social network trends and audience analytics.

Prerequisites: Admission to major or permission of instructor.

IAL 622 The Internet of Things and Wearable Analytics 3

Students collect remote/mobile data using a microcomputer (Arduino, Raspberry Pi) or mobile phone, and then analyze that data by creating a dashboard visualization of their data. Additionally, students conduct a survey of relevant issues pertaining to surveillance and privacy rights for remote/mobile data collection projects.

Prerequisites: Admission to major or permission of instructor.

Notes: Same as ENG 613.

IAL 632 Ethics and Intellectual Property for Informatics and Analytics 3

Students engage relevant ethical and legal issues pertaining to datafication and intellectual property for informatics and analytics. Students conduct ethics and intellectual property research for a particular domain and then produce reports and presentations using reproducible methods.

Notes: Students cannot receive credit for both IAL 632 and CSC 632.

IAL 689 Capstone Project in Cultural Analytics 3

Capstone course. Students work with local industries and nonprofit organizations to solve important data science problems under the supervision of a mentor.

Notes: Same as ENG 624.

IAF 601 Introduction to Data Analytics-Methods and Approaches 3

Managing, manipulating, and analyzing structured/unstructured data to understand relationships and generate useful insights. Principles such as programming for analytics, data visualization, statistical modeling, database design, high performance computing are discussed.

Prerequisites: Programming and statistics experience. M.S. in Informatics and Analytics student or permission of instructor required.

IAF 602 Statistical Methods for Data Analytics 3

Introduction to fundamental statistical techniques for data analytics such as hypothesis testing, data transformation, estimation, confidence intervals, regressions models, ANOVA, multivariate analysis, non-parametric methods, and design of experiments.

Notes: Same as STA 602. Prerequisite: Student in the M.S. in Informatics and Analytics or the M.S. in Applied Statistics program or permission of instructor.

IAF 603 Preparing Data for Analytics 3

Students are exposed to current approaches, techniques and best practices for collecting, cleaning and normalizing data, processing, storing, managing, securing and preparing structured and unstructured big data sets for analytics.

Prerequisites: Programming and statistics experience. M.S. in Informatics and Analytics student or permission of instructor required.

IAF 604 Machine Learning and Predictive Analytics 3

Introduction to machine learning and predictive analytics for Big Data. Some key components include deep learning, supervised, unsupervised models, regression, inductive learning, and time series analysis.

Prerequisites: M.S. in Informatics and Analytics student with a grade of C or better in IAF 601 and IAF 603 or permission of instructor.

IAF 605 Data Visualization 3

Data are analyzed to answer questions. Students are exposed to concepts and techniques to understand analytics results and appropriately infer relationships to answer questions and visualize results using contemporary techniques.

Prerequisites: M.S. in Informatics and Analytics student or permission of instructor required.

IAF 606 Solving Problems with Data Analytics 3

How data analytics is used to solve applied problems in varied contexts. Students will learn how to choose appropriate methodologies, manage data, conduct analyses and report results.

Prerequisites: Student in the M.S. in Informatics and Analytics or the M.S. in Applied Statistics program with a grade of C or better in IAF 601 and IAF 602 or permission of instructor.

Notes: Same as STA 606.

IAF 690 Directed Study in Informatics and Analytics 1-3

Directed study in a topic related to Informatics and Analytics.

IAF 695 Practicum 3

Directed practical experience in a professional setting in the student's area of interest within Informatics and Analytics.

Prerequisites: At least 15 credit hours of IAF courses.