Master 2024/2025

Discrete Mathematics

Type: Compulsory course (Master of Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 1 year, 1 module

Mode of studies: offline

Open to: students of one campus

Master’s programme: Master of Data Science

Language: English

ECTS credits: 3

Contact hours: 24

Course Syllabus

Abstract

This course encompasses various topics in discrete mathematics that are relevant for data analysis. We will begin with a brief introduction to combinatorics, a branch of mathematics concerned with counting. Familiarity with this topic is critical for anyone who wants to work in data analysis or computer science. We will learn how to put our new knowledge into practice, for example, we will count the number of features in a dataset and estimate the time required for a Python program to run. Next, we will use our knowledge of combinatorics to study the Basic Probability Theory. Probability is the cornerstone of data analysis, and we will consider it in much more detail later. However, this section will allow you to get a taste of the probability theory and learn important information which will be essential for the Algorithms and Data Structures course. Finally, we will study a combinatorial structure that remains most relevant for data analysis, namely graphs. Graphs can be found everywhere around us, and we will provide you with numerous examples proving this statement. In this course, we will focus on social network graphs. You will learn the most important notions of the graph theory, have a look at how social network graphs work and study their basic properties. At the end of the course, you will be expected to complete a project related to social graphs. Topics: ● Basic combinatorics ● Advanced combinatorics ● Discrete probability ● Introduction to graphs ● Basic graph parameters ● Social graphs