# Public Health Data Analytics Concentration Specific Courses

### MPH5000 Introduction to Public Health Data Modeling (Formerly MPH0602)

**Credits: 3                                                                                                                                  Offered: Spring 2**\
This course introduces students to core statistical modeling approaches applied in public health data analysis. Students will learn how to select, apply, and interpret statistical models, including both linear and non-linear models. The course includes strategies for building models, adequate choice of models to answer specific public health questions, and data reporting and interpretation. Emphasis is placed on developing practical analytical skills using statistical software, with a focus on real public health datasets and effective communication of results to public health audiences.

**Pre-requisites:**\
MPH1004 (formerly MPH0400) Introduction to Epidemiology\
MPH1002 (formerly MPH0300) Introduction to Biostatistics

### MPH5001 Introduction to Epidemiology Data Analysis with R and Python (Formerly MPH0413)

**Credits: 3                                                                                                                                  Offered: Spring 1**\
R and Python are both open-source languages widely used by epidemiologists to manage and clean data, carry out statistical analyses of epidemiologic data, and produce high-quality figures for research communications. This course will give students a solid foundation in the most important tools for performing epidemiology data analyses using R and Python. Students will learn how to import data, merge datasets, clean and transform variables, visualize, and model population data. Emphasis will be given to modeling approaches for association estimates calculation such as beta coefficients, relative risks, and odds ratios using R as well as data wrangling and exploratory data analysis with Python. Students will also learn about the similarities and differences between R and Python, and how to strategically leverage the strengths of each language depending on the task at hand. Students will be given hands-on training during class and work on an epidemiologic project using R and Python. A key learning goal of this course is to help students familiarize with R and Python and build basic coding skills primarily in R, and extending to Python, while recognizing each unique strengths and complementary utility. Prior programming experience is helpful but not necessary.

**Pre-requisite:**\
MPH 1002 (formerly MPH 0300) Introduction to Biostatistics<br>

### MPH5002 Introduction to Geoinformatics in Public Health (Formerly MPH0601)

**Credits: 3                                                                                                                                        Offered: Spring 1**\
This course introduces students to the foundational tools and concepts of geoinformatics as applied to public health. Students will learn how to analyze, visualize, and interpret spatial health data using open- source GIS platforms such as QGIS and R. Through weekly labs and assignments, students will gain hands-on experience in mapping environmental exposures, identifying geographic patterns in health disparities,and conducting spatial epidemiologic analysis. Geoinformatics has become a crucial methodology in understanding and addressing public health challenges that vary by place, such as access to care, environmental risks, and disease outbreaks. By equipping students with these skills, this course supports the growing need for spatial thinking and data science in public health research, planning, and policy.

**Recommended Pre-requisite:**\
MPH 1004 (formerly MPH 0400) Introduction to Epidemiology

### MPH5003 Machine Learning in Public Health (Formerly MPH0603)

**Credits: 3                                                                                                                                        Offered: Spring 2**\
This course provides a comprehensive overview of unsupervised and supervised machine learning\
algorithms for analysis of continuous and categorical (binary) data, with a focus on applications for public health and epidemiology research. Topics discussed include hierarchical clustering, principal component analysis, factor analysis, LASSO, ridge and elastic net regressions, random forest algorithm, combined with hands-on training using public health datasets. The emphasis is on machine-learning concepts and applications in public health, rather than underlying theory. As mathematical results are presented without proof, students are not required to be proficient in calculus or matrix algebra to take this introductory course.

**Pre-requisites:**\
MPH 1002 (formerly MPH 0300) Introduction to Biostatistics\
MPH 5000 (formerly MPH 0602) Introduction to Public Health Data Modeling or MPH 2002 (Formerly MPH 0812) Applied Linear Models I


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