*Conducted in term:*2022/23L

*ISCED code:*0542

*ECTS credits:*2

*Language:*Polish

*Organized by:*Faculty of Philosophy and Social Sciences

# (in Polish) Advanced statistics 2401-CS-11-AS-s2uz.

Advanced statistics is a course that delves deeper into the field of statistics. It is designed to provide students with a comprehensive understanding of some of more advanced analysis methods commonly used in neuroscience.

First, students will be presented with theoretical and practical knowledge on how to use supervised and unsupervised methods used in data preprocessing, visualization, classification, and prediction (e.g., k-means, hierarchical cluster analysis, decision trees).

Then, the course, building on basics in probability theory, statistical inference, and modeling, will introduce advanced approaches for data modeling, such as multivariate partial least squares regression and correlation techniques (PLSR/PLSC).

Students will be prepared to use appropriate tools offered by statistical software packages such as R, SPSS, and other accessible tools for data manipulation in order to design a desired analysis pipeline.

Throughout the course, students are expected to develop a deep understanding of how to interpret statistical results, critically evaluate research findings, and effectively communicate statistical information to others.

List of topics:

1. Unsupervised data reduction, segmentation, and visualization:

– k-means,

- hierarchical cluster analysis

2. Supervised advanced data predictive models

- decision trees

3. Multivariate correlation / regression models

– partial least square correlation

- partial least square regression

4. Writing scripts for advanced data statistical analysis using R / SPSS software

5. Statistical inference using learned methods and tools.

## Total student workload

## Learning outcomes - knowledge

## Learning outcomes - skills

## Learning outcomes - social competencies

## Teaching methods

## Expository teaching methods

## Type of course

## Prerequisites

## Course coordinators

## Assessment criteria

Assessment methods:

- lecture: oral examination

Assessment criteria:

lecture: oral exam, with three open-ended questions regarding data visualization, modeling, and inference, respectively. Grading:

satisfactory – 1 correct answers

good – 2 correct answers

very good – 3 correct answers

## Additional information

Additional information (*registration* calendar, class conductors,
*localization and schedules* of classes), might be available in the USOSweb system: