## Description

We will use moodle for this course All the students have been imported in the Moodle.

here you can find basic information regarding the course

The objective of this course is to introduce concepts of network science using hands-on exercises. To be specific, the students will learn how relationships among various entities (for example people) can be analysed and interpreted through methods of social network analysis. Students will be introduced to how social network analysis borrows ideas from Mathematics (Graph Theory), Social Sciences (Sociology, Economics), Natural Sciences and Engineering (Physics and Computer Science) to solve problems in different domains. In addition, advanced neural networks based techniques such as Graph Neural Networks will also be introduced. For hands on session, Python and Gephi will be used, for manipulating, analyzing, and visualizing the network data.

## Schedule

- Thursdays 10:15 - 14, online.

## Consultation hours

- Please book an appointment with Email.

## Organization of the course

Each lecture is 4 academic hours, where in the first 2 hours we learn and discuss the problem, relevant techniques, and solutions, while the next 2 hours are practical, where we solve the case studies. The homework will be mostly the continuation of the provided cases.

## Assessment of the course

There are three modes of assessment. Homeworks (60 marks), Project (30 Marks) Paper Presentation (10 marks). To get a pass grade, you must score at least 51 points in total AND at least 50% in each type of assessment.

## Organizers of the course

Rajesh Sharma is responsible for this course. For any questions please send an email: `rajesh.sharma@ut.ee`