Veidlapa Nr. M-3 (8)
Study Course Description
Data Science Applications in Public Health
Course Code
SVUEK_145
Branch of Science
Clinical medicine; Health care sciences and services
ECTS
3.00
Target Audience
Health Management; Public Health
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Uģis Kārlis Sprūdžs
Structure Unit Manager
Anda Ķīvīte-Urtāne
Structural Unit
Institute of Public Health
Contacts
Riga, 9 Kronvalda boulevard, svek@rsu.lv, +371 67338307
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
Preliminary Knowledge
Research methods, basic statistics, preferably a mathematical understanding of logarithms and differentiation, computer literacy
Learning Outcomes
Knowledge
1.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
Skills
1.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
Competences
1.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
Assessment
Individual work
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Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Independent work outside the classroom involves preparing for lectures, utilizing lecture notes to study for quizzes, doing homework assignments – data preparation, model development, and model assessment. Completing the study course evaluation questionnaire.
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Examination
|
Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Active participation in class discussions and activities;
Quizzes on the practical use of terminology and methods learned;
Evaluation of homework assignments.
Final exam covering terminology, methods, and practical applications – 40%
Homework assignments involving data preparation and model development – 30%
Quizzes – 30%
Missed lectures require a minimum one-page summary of the covered material based on the information provided during the lecture.
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||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series description and examples. Univariate time series analysis: trend, stationarity, seasonality, outliers.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series description and examples. Univariate time series analysis: trend, stationarity, seasonality, outliers.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series description and examples. Univariate time series analysis: trend, stationarity, seasonality, outliers.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series forecasting models: univariate forecasting. Trend and seasonality adjustments. Differencing. Outliers and one-time effects. Autoregression. Model evaluation. Notation. Forecasting interval.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series forecasting models: univariate forecasting. Trend and seasonality adjustments. Differencing. Outliers and one-time effects. Autoregression. Model evaluation. Notation. Forecasting interval.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series forecasting models: univariate forecasting. Trend and seasonality adjustments. Differencing. Outliers and one-time effects. Autoregression. Model evaluation. Notation. Forecasting interval.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Multivariate time series models. Structural vs. forecasting models. Concurrent effects. Time-lagged effects. Missing variables. Spurious correlation. Logarithmic models.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Multivariate time series models. Structural vs. forecasting models. Concurrent effects. Time-lagged effects. Missing variables. Spurious correlation. Logarithmic models.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Multivariate time series models. Structural vs. forecasting models. Concurrent effects. Time-lagged effects. Missing variables. Spurious correlation. Logarithmic models.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Multivariate time series models as a simulation platform.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Multivariate time series models as a simulation platform.
|
-
Unaided Work
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
0
|
Topics
|
Multivariate time series models as a simulation platform.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series models compared with traditional SIR epidemiological models
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Time series models compared with traditional SIR epidemiological models
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Predictive models vs. descriptive models, definitions. Model architecture. Decision tree form as example of classification model. Model requirements.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Predictive models vs. descriptive models, definitions. Model architecture. Decision tree form as example of classification model. Model requirements.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Predictive models vs. descriptive models, definitions. Model architecture. Decision tree form as example of classification model. Model requirements.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Classification model development: data preparation and partitioning, model algorithm selection, validation. XGBoost method.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Classification model development: data preparation and partitioning, model algorithm selection, validation. XGBoost method.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Classification model development: data preparation and partitioning, model algorithm selection, validation. XGBoost method.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Classification model implementation. Identification of primary predictors and describing their contribution to the model. Model monitoring.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Classification model implementation. Identification of primary predictors and describing their contribution to the model. Model monitoring.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Classification model implementation. Identification of primary predictors and describing their contribution to the model. Model monitoring.
|
-
Unaided Work
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
0
|
Topics
|
Classification model implementation. Identification of primary predictors and describing their contribution to the model. Model monitoring.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Brief overview of alternative modeling approaches. AutoML procedures.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Brief overview of alternative modeling approaches. AutoML procedures.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Sprūdžs U. Lekciju materiāli 2022/2023
2.
Russell S & Norvig P. Artificial Intelligence: A Modern Approach. Pearson, 2021.
3.
Ārvalstu studentiem/For international students:
4.
Russell S & Norvig P. Artificial Intelligence: A Modern Approach. Pearson, 2021.