Career Design for Life

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

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.

 

Suggested Quantitative Courses

EVSC 5030 

Applied Statistics for Environmental Scientists (4 Credit Hours) Lecture 

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.

PSYC 7720 

Quantitative Methods II: Experimental Design, (4 Credit Hours) Lecture

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.

BIMS 8380 Practical Biomedical Statistics I, (2 Credit Hours)

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.

BIMS 8382 Practical Biomedical Statistics II, (2 Credit Hours) 

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.

GCOM 7240 Advanced Quantitative Analysis, (3 Credit Hours) 

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.

PHS 7001 Introduction to Biostatistics II, (3 Credit Hours)

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.

PSYC 5323 RM: R in Psychology, (3 Credit Hours) 

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.

PSYC 7760 Introduction to Applied Multivariate Methods, (3 Credit Hours) 

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.

 PSYC 5710 Machine Learning and Data Mining, (3 Credit Hours) 

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.