Study Guide

Objectives

The student
- is able to handle data sets
- has basic knowledge of mathematics related to data-analysis
- is able to use and apply classical data analysis for solving technical problems
- is familiar with basics and methods of regression, classification and clustering

Content

• Classical Data Analysis
• Classification, Decision Trees, Random Forests
• Clustering, K-means
• Regression, Linear Regression, Logistic Regression
• Basics of Neural Network

Assessment criteria, satisfactory (1-2)

The student is able to handle data and knows the basics of data analysis and the related key methods. The student is able to calculate simple tasks related to the topics of the course, which are similar to the examples presented.

Assessment criteria, good (3-4)

In addition to the above, the student is able to apply the course knowledge in new situations and justify his/her solutions. The student is able to use the concepts and methods related to the subjects of the course correctly. The student performs the given tasks independently.

Assessment criteria, excellent (5)

In addition to the above, the student has a comprehensive understanding of the course topics and their use in problem solving, as well as the ability to present and justify his/her solutions logically.