Open University

Computational Diagnostics of Data, Lectures

Tampere University

Extent

5 cr

Course dates

13.3.2023- 29.4.2023

Application period

14.11.2022-13.2.2023

Fees

75,00 € (15 €/cr)

Campus

Hervanta Campus

City

Tampere

Faculty or school

Faculty of Information Technology and Communication Sciences

Faculty of Medicine and Health Technology

Language of instruction

English

Code

DATA.ML.390

Study fields

Technology and Natural Sciences

Mode of study

Contact teaching

Study level

Advanced studies

Computational Diagnostics of Data, 5 cr

Code: DATA.ML.390

Language of instruction: English

Description

After completing the course, the student gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for biomedical research and business intelligence. Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to large-scale and high-dimensional data sets from genomic experiments and business processes, e.g., the stock market or consumer behavior. Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. In general, the course teaches statistical thinking in the context of biomedical and business problems, i.e., the adaptation of machine learning methods in a problem specific manner.

Core content

Classification of disease groups
Business intelligence
Survival analysis
Genomics data
Programming in R
Quantitative assessment of results
Predictive models

Complementary knowledge

Computational implementation and interpretation; classification methods
Data analysis methods for business processes
Regression models for time-to event processes
Preprocessing and normalization of gene expression data from microarray experiments
Usage and programming in the statistical programming language R
Statistical error measures; resampling techniques
Linear regression, hypothesis testing; general models in data science

Course dates

Common

Exercise:

14.03.2023 12:00 - 14:00
21.03.2023 12:00 - 14:00
28.03.2023 12:00 - 14:00
04.04.2023 12:00 - 14:00
18.04.2023 12:00 - 14:00
25.04.2023 12:00 - 14:00

Lecture:

13.03.2023 12:00 - 14:00
17.03.2023 12:00 - 14:00
20.03.2023 12:00 - 14:00
24.03.2023 12:00 - 14:00
27.03.2023 12:00 - 14:00
31.03.2023 12:00 - 14:00
03.04.2023 12:00 - 14:00
14.04.2023 12:00 - 14:00
17.04.2023 12:00 - 14:00
21.04.2023 12:00 - 14:00
24.04.2023 12:00 - 14:00
28.04.2023 12:00 - 14:00

Prerequisites

Teaching material

Material

Type: Book
Name: An Introduction to Statistical Learning
Author: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Exam material: No
URL: http://www-bcf.usc.edu/~gareth/ISL/
Language: English
Info:

Material

Type: Book
Name: Statistics and Data Analysis for Microarrays Using R and Bioconductor
Author: Sorin Dr
Exam material: No
URL: https://www.crcpress.com/Statistics-and-Data-Analysis-for-Microarrays-Using-R-and-Bioconductor/Drghici/9781439809754
Language: English
Info: Introduction to the analysis of microarray data.

Assessment methods

Contact information

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