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Courses - Summer 2024
EPIB
Epidemiology and Biostatistics Department Site
Open Seats as of
06/15/2024 at 10:30 AM
EPIB300
Biostatistics for Public Health Practice
Credits: 3
Grad Meth: Reg, P-F, Aud
GenEd: FSAR
Prerequisite: Minimum grade of C- in CHEM131 and CHEM132.
Restriction: Must be in Public Health Science program; and must have earned a minimum of 60 credits.
Credit only granted for: EPIB300, EPIB315 or HLTH300.
An examination of biostatistical concepts and procedures as they relate to contemporary issues in public health. Focus on applications, hands-on-experience, and interpretations of statistical findings in public health research.
EPIB301
Epidemiology for Public Health Practice
Credits: 3
Grad Meth: Reg, P-F, Aud
Restriction: Must be in Public Health Science program; or must be in Community Health program. And must have earned a minimum of 45 credits.
An examination of the discipline of epidemiology and its application to public health issues and practices, covering current epidemiological concepts and methods.
EPIB315
Biostatistics for Public Health Practice
Credits: 3
Grad Meth: Reg, P-F, Aud
GenEd: FSAR
Prerequisite: Minimum grade of C- in EPIB301; or must have completed or be concurrently enrolled in HLTH200.
Restriction: Must be in one of the following programs (Public Health Science; Community Health).
Credit only granted for: EPIB300 or EPIB315.
Formerly: EPIB300.
Additional information: Course is cross-listed; students should check program advising information to determine which counts for their major. Note that EPIB300 (old number) is still offered for students under previous curriculum.
An examination of biostatistical concepts and procedures as they relate to contemporary issues in public health. Focus on applications, hands-on-experience, and interpretations of statistical findings in public health research.
EPIB399
Epidemiology and Biostatistics Independent Study
Credits: 1 - 3
Grad Meth: Reg
EPIB463
Introduction to Biostatistical Programming
Credits: 3
Grad Meth: Reg, P-F, Aud
An introduction to basic programming principles; data analysis tasks such as the calculation of summary statistics and the creation of graphs; and the implementation of statistical analysis concepts such as T-tests, ANOVA and correlation. Querying and managing data sets using SQL in SAS will also be covered.
EPIB467
Introduction to Machine Learning with Python
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: EPIB300, EPIB315, EPIB650, or SPHL602.
Jointly offered with: EPIB667.
Credit only granted for: EPIB467 or EPIB667.
Machine Learning is the cutting-edge technology revolutionizing predictive analytics. This introductory course covers multiple models/algorithms widely applied to structured data with numerical and categorical variables, serving both as outcomes and predictors. It explores over a dozen foundational and advanced models for supervised learning, as well as several models for unsupervised learning. By covering essential math and statistics for understanding ML algorithms, the focus is on understanding the distinctive features of the models and hands-on coding exercises in the Cloud, emphasizing practical application from the start.
Machine Learning is the cutting-edge technology revolutionizing predictive analytics. This introductory course covers multiple models/ algorithms widely applied to structured data with numerical and categorical variables, serving both as outcomes and predictors. It explores over a dozen foundational and advanced models for supervised learning, as well as several models for unsupervised learning. By covering essential math and statistics for understanding ML algorithms, the focus is on understanding the distinctive features of the models and hands-on coding exercises in the Cloud, emphasizing practical application from the start.
EPIB611
Intermediate Epidemiology
Credits: 3
Grad Meth: Reg, Aud, S-F
Prerequisite: 1 course with a minimum grade of B- from (SPHL602, EPIB610); or a minimum score of 70% on the SPHL602 or EPIB610 waiver exam.
Analysis of epidemiologic methods as applied to epidemiologic research, analysis of bias, confounding, effect modification issues, overview of design, implementation, and analysis of epidemiologic studies.
EPIB620
(Perm Req)
Chronic Disease Epidemiology
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Must have completed or be concurrently enrolled in SPHL602; or EPIB610.
Overview of prevalence and risk factors for major chronic diseases. Discussion of methodological issues unique to specific chronic disease.
EPIB651
Applied Regression Analysis
Credits: 3
Grad Meth: Reg, Aud, S-F
Prerequisite: 1 course with a minimum grade of B- from (SPHL602, EPIB650); or a minimum score of 70% on the SPHL602 or EPIB650 waiver exam.
Recommended: EPIB697 or previous experience working with SAS is highly recommended.
An introduction to important statistical methods used in public health research, including nonparametric hypothesis testing, ANOVA, simple and multiple linear regression, logistic regression, and categorical data analysis.
EPIB660
Analysis of National Health Survey Data
Credits: 3
Grad Meth: Reg, Aud, S-F
Prerequisite: EPIB650; or permission from Instructor.
Recommended: EPIB697.
Provides background on how features such as stratification, clustering, and unequal sample selection probabilities can invalidate the assumptions underlying traditional statistical techniques, those implicitly assuming a simple random sampling with replacement design. Application using the SURVEY family of SAS/STAT procedures (Version 9.4 or later).
EPIB667
Applied Machine Learning with Python
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: EPIB315, EPIB300, EPIB650, or SPHL602.
Jointly offered with: EPIB467.
Credit only granted for: EPIB467 or EPIB667.
Machine Learning is the cutting-edge technology revolutionizing predictive analytics. This introductory course covers multiple models/algorithms widely applied to structured data with numerical and categorical variables, serving both as outcomes and predictors. It explores over a dozen foundational and advanced models for supervised learning, as well as several models for unsupervised learning. By covering essential math and statistics for understanding ML algorithms, the focus is on understanding the distinctive features of the models and hands-on coding exercises in the Cloud, emphasizing practical application from the start.
Machine Learning is the cutting-edge technology revolutionizing predictive analytics. This introductory course covers multiple models/ algorithms widely applied to structured data with numerical and categorical variables, serving both as outcomes and predictors. It explores over a dozen foundational and advanced models for supervised learning, as well as several models for unsupervised learning. By covering essential math and statistics for understanding ML algorithms, the focus is on understanding the distinctive features of the models and hands-on coding exercises in the Cloud, emphasizing practical application from the start.
EPIB695
Introduction to R for Health Data Analysis
Credits: 3
Grad Meth: Reg, Aud
Jointly offered with: EPIB463.
Credit only granted for: EPIB463 or EPIB695.
A hands-on introduction to the statistical package R for health data management and analysis. The first part of the course focuses on basic and essential data manipulation and visualization using R. The second part emphasizes the use of R in statistical analyses, including summarization, correlation, chi-squared test, t-tests, ANOVA, simple and multiple regression. Students will also learn fundamental R language programming to perform user-defined calculations. No previous knowledge of R or of statistical analysis are assumed.
EPIB697
Public Health Data Management
Credits: 3
Grad Meth: Reg
Prerequisite: Permission of instructor.
This course is designed to provide students with the expertise needed to effectively manage research data using SAS as the statistical programming language.
EPIB778
(Perm Req)
Practical Experience in Public Health
Credits: 1 - 4
Grad Meth: Reg
Contact department for information to register for this course.
EPIB786
(Perm Req)
Capstone Project in Public Health
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Permission of SPHL-Epidemiology & Biostatistics department.
Capstone experience providing opportunity to apply knowledge and skills to a specific public health problem or issue. Completion of project relevant to public health under the direction of an advisor.
Contact department for information to register for this course.
EPIB798
(Perm Req)
Credits: 1 - 6
Grad Meth: Reg, Aud
Contact department for information to register for this course.
EPIB799
(Perm Req)
Master's Thesis Research
Credits: 1 - 6
Grad Meth: S-F
Contact department for information to register for this course.
EPIB898
(Perm Req)
Pre-Candidacy Research
Credits: 1 - 3
Grad Meth: Reg
Contact department for information to register for this course.
EPIB899
Doctoral Dissertation Research
Credits: 1 - 8
Grad Meth: S-F
Contact department for information to register for this course.