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About Study Course

ECTS:5
Course supervisor:Uģis Kārlis Sprūdžs
Study type:Full time
Course level:Master's
Target audience:Health Management; Public Health
Language:Latvian
Study course description Full description, Full time
Branch of science:Mathematics; Theory of probability and mathematical statistics

Objective

To acquire in-depth knowledge, abilities and skills in specific methods of mathematical statistics and latest data science for study purposes; work in a public health specialty; and to promote the learning and practical application of data science terminology.

Prerequisites

Research methodology, basic principles of statistics, mathematics (preferable) – logarithms, differentials, computer skills, health data types and elements.

Learning outcomes

Knowledge

1.As a result of successfully completing the study course, students:
Will recognise the terminology of time series analysis and its use;
Will be familiar with the functionality offered by Oxmetrics in time series analysis;
Will learn how to formulate, develop and deploy regression and classification models using the KNIME platform.

Skills

1.As a result of successfully completing the study course, students will be able:
- To open, create, and edit time series data in Oxmetrics;
- To correctly prepare a descriptive model of a univariate series using the Oxmetrics platform;
- To correctly prepare a descriptive model of a multivariate series using the Oxmetrics platform;
- To open and prepare data for the development of regression and classification models using the KNIME platform;
- To set up and execute regression and classification model procedures on the KNIME platform;
- To assess the validity of models using the KNIME platform;
- To identify the main factors of a model and the form of their impact on the KNIME platform;
- To explain the model deployment and monitoring practices;
- To create a description of the methods and results used.

Competence

1.As a result of successfully completing the study course, students will be able:
To correctly interpret and evaluate the use of time series models in the public health sector;
To plan, set up and evaluate regression and classification models using healthcare data.

Study course planning

Planning period:Year 2026, Spring semester
Study programmeStudy semesterProgram levelStudy course categoryLecturersSchedule
Digital transformation in the health care sector2Master'sRequired
Digital transformation in the health care sector 2Master'sRequired