Integrating Research and Careers on the Phenotype

A National Science Foundation Research Traineeship (NRT)

Spring 2023 Courses

Spring 2023 Courses

Professional Skills Workshops

Internship Bootcamp in Partnership with PhD Plus, (Workshop Series, no credit hours) - TBD

This workshop series will use a student-centered approach to analyze the benefits of experiential learning for PhD students. Students will gain professional experience, obtain knowledge of a career field, and develop career-specific skills to increase their career preparedness. Ideally, this workshop should be taken by students who plan to complete an internship within the next year. Contact instructors if interested in participating.

 

Instructors: Nicole Hensch and Mallory Neil 


Quantitative Courses

EVSC 5030, 

Applied Statistics for Environmental Scientists (4 Credit Hours) Lecture - TuTh 11am-12:15pm, Lab - We 3:30-4:45pm

Provides a firm knowledge of experimental design, hypothesis testing, and the use of statistical methods of data analysis. Prerequisite: MATH 1110, STAT 1120, or equivalent; corequisite: EVSC 5031.

Instructor: David Carr

PSYC 7720, 

Quantitative Methods II: Experimental Design, (4 Credit Hours) Lecture TuTh 11am-12:15pm, Lab - Th 6-8pm or Fr 9-11am

Includes Chi-square tests for contingency tables, correlation, multiple regression, analysis of variance of one-way and factorial designs including repeated measures experiments, and analysis of covariance. Extension work with SPSS and MANOVA computer routines. Prerequisite: PSYC 7710 or equivalent.

Instructor: Xin Tong

BIMS 8380, Basic Study Design and Practical Statistics, (2 Credit Hours) TuTh 9-10:30am

The course format will include: lecture, web-based learning, group discussions, and practical laboratory exercises with stats software. Students will learn the basics of typical study designs and practical use of common statistical methods. Students will apply learning to reinforce skills and achieve practical competence in: identification of design and statistical resources, experimental design, evaluation of results and data interpretation.

Instructors: Marieke Jones and David Martin

BIMS 8382, Introduction to Biomedical Data Science, (2 Credit Hours) TuTh 9-10:30am

This course introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, the course introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analysis of data from a real RNA-seq experiment using R and Bioconductor packages. Prerequisite: Must be currently enrolled in BIMS 8380.

Instructors: Marieke Jones and David Martin 

GCOM 7240, Advanced Quantitative Analysis, (3 Credit Hours) TuTh - 12:30-3:15pm

Multivariate statistics training to analyze Big Data sets. The course covers discrete choice modeling (logistic and probit models), classification techniques (discriminant and cluster analyses), data reduction techniques (factor analysis), and advanced predictive techniques (regression models with interactions and curvilinear effects, structural equation modeling, and factorial ANOVA). Trains students on IBM-SPSS, SAS, and R.

Instructor: Polina Landgraf

PHS 7001, Introduction to Biostatistics II, (3 Credit Hours) TuTh 11am-12:15pm

An illustration of the indications, limitations, assumptions, and appropriate applications of analytical methods in a variety of biomedical settings. Students will learn how to determine which analytic technique would be best suited for a variety of translational and clinical research, evaluation, and policy study designs. Prerequisite: Instructor permission; PHS 7000.

Instructor: Pavel Chernyavskiy

PSYC 5323, RM: R in Psychology, (3 Credit Hours) MoWe 3:20-4:45pm

This course is designed to introduce the statistical language R, with the purpose of preparing students to use and apply quantitative methods in their future psychology research. Topics may include handling data structures, cleaning data, visualizing and presenting data, and reviewing introductory statistics using R.

Instructor: M. Joseph Meyer

PSYC 7760, Introduction to Applied Multivariate Methods, (3 Credit Hours) Mo 9-11:30am

Introduces major statistical methods used for the data analysis of multiple measures. Includes elementary matrix algebra, multivariate regression (canonical correlation; multivariate analysis of variance and covariance; and discriminant analysis and classification), correlational methods (principal components and exploratory and confirmatory factor analysis), and the analysis of multivariate contingency tables using log-linear models. Emphasizes concepts, issues, and examples over mathematical derivations. Prerequisite: PSYC 7710-7720 or equivalent.

Instructor: Steven Boker

PSYC 5710, Machine Learning and Data Mining, (3 Credit Hours) Mo 9-11:30am

Machine learning and data mining are among the topics that are very demanded nowadays. They can be used to extract knowledge from multivariate datasets, to transform unstructured data into analyzable datasets, and to make extremely accurate and stable predictions. The present course will be an introductory, hands-on course, covering a number of basic techniques and methods used in the fields of machine learning and data mining, using R. 

Instructor: Hudson Golino