Practical Epidemiology

Unit 1.1. Introduction to Epidemiological Concepts and Measures of Disease Frequency (Incidence, Prevalence, Rates).

1 week

Unit 1.2. Measures of Association and Effect (Relative Risk, Odds Ratio, Attributable Risk).

1 week

Unit 1.3: Fundamental Biostatistics: Descriptive Statistics, Probability, and Confidence Intervals.

1 week

Unit 1.4: Introduction to Hypothesis Testing and P-values in Medical Research.

1 week

Unit 2.1: Data Entry Best Practices, Data Cleaning, and Structuring Raw Health Datasets.

1 week

Unit 2.2: Advanced Formulas and Functions for Public Health Data (VLOOKUP, INDEX/MATCH, IF statements).

1 week

Unit 2.3: Pivot Tables and Data Aggregation for Epidemiological Summaries.

1 week

Unit 2.4: Creating Professional Epidemiological Charts and Visualizations in Excel.

1 week

Unit 3.1: R Basics: Installing R/RStudio, Working Directories, Data Types, and Basic Syntax.

1 week

Unit 3.2: Data Manipulation and Management using the Tidyverse

1 week

Unit 3.3: Descriptive Analysis and Epidemiological Data Summaries using R.

1 week

Unit 3.4: Introduction to Data Visualization in R with ggplot2 (Epidemic Curves, Scatter Plots, Boxplots).

1 week

Unit 4.1: Formulating Research Questions and Developing Protocols.

1 week

Unit 4.2: Outbreak Investigation: Steps, Case Definitions, and Line Listing.

1 week

Unit 4.3: Designing Effective Health Surveys and Questionnaires.

1 week

Unit 4.4: Scientific Writing, Interpreting Results, and Communicating Public Health Data to Stakeholders.

1 week

Unit 5.1: Overview of Observational Studies: Cross-Sectional and Ecological Studies.

1 week

Unit 5.2: Case-Control Studies: Design, Selection of Controls, and Bias Mitigation.

1 week

Unit 5.3: Cohort Studies: Prospective, Retrospective, and Measuring Long-Term Outcomes.

1 week

Unit 5.4: Experimental Studies: Randomized Controlled Trials (RCTs) and Quasi-Experimental Designs.

1 week

Unit 6.1: Fundamentals of Causal Inference in Epidemiology.

1 week

Unit 6.2: Drawing and Interpreting Directed Acyclic Graphs (DAGs): Nodes, Edges, and Paths.

1 week

Unit 6.3: Identifying Bias: Confounding, Selection Bias, and Mediation using DAGs.

1 week

Unit 6.4: The Rules of d-Separation and Determining Minimal Sufficient Adjustment Sets.

1 week

Unit 7.1: Comprehensive Review and Synthesis of Epidemiological Methods and Tools.

1 week

Unit 7.2: Practical Case Study: Troubleshooting a Public Health Dataset from Cleaning to Analysis.

1 week

Unit 7.3: Final Course Capstone Project Presentation.

1 week

Unit 7.4: End of Course Comprehensive Written and Applied Assessment.

1 week