Data Science Applications in Public Health (SVUEK_145)
About Study Course
Objective
Acquire in-depth knowledge, understanding, and skills in specific methods of mathematical statistics and recently developed data science techniques for academic use and work in Public Health; also to promote the learning of data science terminology and its practical applications
Prerequisites
Research methods, basic statistics, preferably a mathematical understanding of logarithms and differentiation, computer literacy
Learning outcomes
Upon successfully completing the course students will:
Understand time series analysis terminology and its implementation;
Be familiar with time series analysis functionality in the OxMetrics package;
Learn learn how to formulate, develop, and implement predictive classification models using the KNIME platform
Upon successfully completing the course students will
* Know how to open, create, and manipulate time series data in OxMetrics
* Using OxMetrics, know how to prepare a proper descriptive univariate time series model
* Using OxMetrics, know how to prepare a proper descriptive multivariate time series model
* Using KNIME, know how to open data sets and prepare data for the development of predictive classification models
* Know how to set up and execute a predictive model in KNIME
* Know how to evaluate a predictive model in KNIME
* Using KNIME, know how to identify the major drivers of a predictive model and their effect on the target variable
* Know how to explain the implementation and monitoring of a classification model
* Know how to summarize the methods taught in the course and their outcomes
Upon successfully completing the course students will
Correctly interpret and evaluate applications of time series models in the Public Health field;
Using Public Health data, plan, develop, and evaluate a predictive classification model
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Public Health, SVFM | 3 | Master’s | Limited choice |
