Core content <ul><li>General error measures </li><li>Statistical hypothesis testing </li><li>Classification models </li><li>Resampling methods </li><li>Regression models </li><li>Survival analysis </li><li>Examples for complex data </li><li>Programming in R </li></ul>Complementary knowledge <ul><li>Statistical thinking </li><li>Introduction to programming in R </li><li>Data analysis is unlike following a cookbook recipe </li><li>Learn about different data types </li><li>Evaluating results requires different statistical error measures </li><li>Resampling techniques allow the quantification of uncertainty </li></ul>

Open University

Lectures in spring 2026. Scheduled online teaching. The course includes an electronic Exam. The student takes the exam at a time of their choosing in the EXAM facilities. Please familiarize yourself with the <a href="https://www.tuni.fi/en/students-guide/handbook/uni/studying-0/examinations/electronic-exams" target="_blank" rel="noopener noreferrer">instructions for taking the Exam</a>.

Faculty of Information Technology and Communication Sciences

Faculty of Medicine and Health Technology

English

Data science is a subject consisting of many important topics. This course covers some of such topics, including statistical hypothesis testing, classification, resampling methods, regression and survival analysis, indispensable to analyze data. Examples will be discussed illustrating the interplay between computational and statistical methods that are applied to complex data sets. 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 for analyzing data.

Basic programming skills. Experience with the language R is desirable, but not necessary. Basic knowledge in Mathematics, Statistics and Machine Learning. Please ensure that you have completed the required prerequisites before registering! By registering, you agree to comply with the requirements and, upon request, you can present a transcript of records showing the completion of the required prerequisites.

<h3>Lectures</h3><h4>Lecture: </h4>04.03.2026 10.00 - 04.03.2026 12.00 06.03.2026 12.00 - 06.03.2026 14.00 11.03.2026 10.00 - 11.03.2026 12.00 13.03.2026 12.00 - 13.03.2026 14.00 18.03.2026 10.00 - 18.03.2026 12.00 20.03.2026 12.00 - 20.03.2026 14.00 25.03.2026 10.00 - 25.03.2026 12.00 27.03.2026 12.00 - 27.03.2026 14.00 08.04.2026 10.00 - 08.04.2026 12.00 10.04.2026 12.00 - 10.04.2026 14.00 15.04.2026 10.00 - 15.04.2026 12.00 17.04.2026 12.00 - 17.04.2026 14.00 24.04.2026 12.00 - 24.04.2026 14.00 <h4>Exercise: </h4>05.03.2026 10.00 - 05.03.2026 12.00 12.03.2026 10.00 - 12.03.2026 12.00 19.03.2026 10.00 - 19.03.2026 12.00 26.03.2026 10.00 - 26.03.2026 12.00 02.04.2026 10.00 - 02.04.2026 12.00 16.04.2026 10.00 - 16.04.2026 12.00 23.04.2026 10.00 - 23.04.2026 12.00

TUNI Moodle: DATA.ML.390-2025-2026-1 Computational Diagnostics of Data (Lectures)

Lectures, spring 2026

Lectures in spring 2026. Scheduled online teaching. The course includes an electronic Exam. The student takes the exam at a time of their choosing in the EXAM facilities. Please familiarize yourself with the <a href="https://www.tuni.fi/en/students-guide/handbook/uni/studying-0/examinations/electronic-exams" target="_blank" rel="noopener noreferrer">instructions for taking the Exam</a>. Number of seats for Open university students: 5<a href="https://www.tuni.fi/en/study-with-us/computational-diagnostics-data" target="_blank" rel="noopener noreferrer">More information about the course.</a><a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university" target="_blank" rel="noopener noreferrer">More information about studying at the Open University</a>.

Independent studies

Online studies

Technology and Natural Sciences

Advanced studies

<h3>Material</h3><ul><li>Type: Book</li><li>Name: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R </li><li>Author: Frank Emmert-Streib, Salissou Moutari, and Matthias Dehmer </li><li>Exam material: No</li><li>URL: <a href="https://link.springer.com/book/10.1007/978-3-031-13339-8" target="_blank" rel="noopener noreferrer">https://link.springer.com/book/10.1007/978-3-031-13339-8</a></li><li>Language: English</li><li>Info: The book included chapters for the lectures covered in this course.</li></ul> For an introduction to programming in R:<ul><li>Type: Book</li><li>Name: Mathematical Foundations of Data Science Using R </li><li>Author: Frank Emmert-Streib, Salissou Moutari, and Matthias Dehmer </li><li>Exam material: No</li><li>URL: <a href="https://www.degruyter.com/document/doi/10.1515/9783110796063/html" target="_blank" rel="noopener noreferrer">https://www.degruyter.com/document/doi/10.1515/9783110796063/html</a></li><li>Language: English</li><li>Info: The first part of the book provides an introduction to programming in R.</li></ul>

DATA.ML.390 Computational Diagnostics of Data, 5 cr

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