University courses

The aim of the course is to develop the knowledge on master thesis writing requirements and criteria at the Institute of Computer Science; to get an overview of possible research methodologies; help resolving possible questions arising during the thesis writing; to learn the terms and the language connected to the speciality; to learn using efficiently the language maintenance related resources; to develop critical attitude towards text creation; to practice presentation skills for the defense of the master thesis; to learn on how to get and how to give feedback to work by fellow students.

The goal of the course is to introduce the possible applications of data science for business and to demonstrate the application of data science in the decision-making processes. The following topics like data collection, cleaning and processing, visualization, business analytics, machine learning, business process analysis, data science application in decision-making processes, roles in data science projects and ethics and regulations are tackled in this course. Course includes the planning and execution of a practical Data Science project. Practice sessions are conducted in Python programming language, however the course does not have programming prerequisites.The course is conducted in the Estonian language. It consists of video lectures, textual study materials, tests, practical sessions, and a course project.

This introductory course aims to introduce what is Artificial Intelligence in theory and in practice for those who have little or no prior contact with Artificial intelligence and/or programming. The course is conducted in the Estonian language. It consists of video lectures, textual study materials, tests and a final exam. By the end of the course, the student knows the main concepts and applications of artificial intelligence and can make simple changes to code snippets according to the tasks given.

Goal of the course is to give theoretical and practical skills in data visualization and communication of analysis results. During the course we provide overview of most important visualization methods, principles of visual design and application of those in desinging slidedecks, posters and dashboards.

The aim of this course is to provide basic knowledge and skills to the student to conduct health data analysis tasks. In this course, the following topics are covered: what are electronic health data and how they look like, for what purposes and how they can be used, what are the main health data standards.

This is very interactive and practical course – lots of practical exercises, discussions, we will see interviews with the health data analysts and doctors, etc. The course takes place during spring sessions.

Comments from students:

  • “This is one of the best courses at our university! The most important thing is to take part from the live lectures as the discussions are very interesting and wonderfully support learning new content.”
  • “This is the course which is not only interesting but you will learn a lot of useful technical stuff as well. I honestly recommend to take this course.”
  • “I warmly suggest to take this course even when you are studying at curriculum where it is not mandatory.”

 

This course aims at introducing students to the main concepts and principles of algorithm and data structure design and analysis. This course provides an overview of the different types of algorithms and their design principles, as well as the key concepts in the analysis of their time and space complexity.

Aim of the course is to enable students across all disciplines of the university to obtain an overview of the working principles and effect of digital technologies. Lectures shall explain the foundations in a simple manner and by using examples. After completion of the course the students can better plan their further studies and life-long learning paths, as well as understand the effect of digital transformation and new opportunities in their own disciplines.

Continuous Education

Duration: 01 Jan 2021- 31 Jan 2021

AIProHealth (Practical Artificial Intelligence for Healthcare Professionals) aims to contribute to the implementation of AI in healthcare in the near future. 

While Al undoubtedly creates many opportunities to improve healthcare delivery, it also poses risks and challenges. AIProHealth project brought together experts from all areas and sectors (public and private) of healthcare and the serious gaming communities to co-create a unique program to put the hype of AI in perspective, while removing the fear of use of AI in healthcare practice.
Within the AIProHealth project, experts are developing a customizable AI teaching environment containing a massive open online course (MOOC) and a Face2Face course with as most important aspect a serious game “AI Hospital” developed during AIProHealth project. The total package provides an innovative blended learning experience which enlightens the healthcare professional with valuable information, while considering the patient’s perspective.

MOOC course contains theoretical and practical information about Al including the potential and limitations of Al in healthcare. Using real-world examples, this course will help participants discover the risks, so they can make an informed decision about using Al in their day-to-day work. This course is designed for healthcare professionals who want to better understand AI for healthcare. This includes doctors, nurses, and biomedical researchers. It will also be of interest to medical students, PhD students, and general AI enthusiasts.

Subscribe to course How Artificial Intelligence Can Support Healthcare:

https://www.futurelearn.com/courses/how-artificial-intelligence-can-support-healthcare

Awards: project has received EIT Health quality label 2022.

Funded by EIT Heath

 

Duration: 01 Sep 2019 – 31 Aug 2022

The DATAclinic program offers a curriculum which complies to the need for a healthcare workforce which has the necessary skills regarding data science to contribute to improving the effectiveness of medical research and patient care.

The DATAclinic program consists of 4 complementary courses to learn all about medical data science. If certain subjects align more with your interests, it is also possible to just follow a subset of the available courses.
Courses: 

  • Data Literacy (University of Tartu), 3 EAP
  • Data Stewardship
  • Collaborative Data Science
  • Data Science in Clinical Practice

Web: https://www.dataclinics.eu/

Funded by Erasmus+ 

Duration:

  • 03 Oct 2022 – 08 Nov 2022
  • 04 Oct 2021 – 07 Nov 2021
  • 19 Oct 2020 – 16 Nov 2020

Data Science for Business is a 5-week massive open online course (MOOC). The purpose of  course is to introduce the possibilities of data science in the company and to show the application of data science in decision-making processes.

Within this subject, you can familiarise yourself with the following topics:

  • Data Science Standard Process (CRISP-DM).
  • Data collection, cleaning and modification;
  • Data visualization;
  • Data desktop;
  • Machine learning;
  • Business analytics;
  • Application of data science in decision-making processes;
  • Roles in data science projects;
  • Data Science Ethics and Regulations.

The e-course “Possibilities of data science in a business company” received a 2021 Quality mark from the Estonian Higher and Vocational Education Quality Agency (EKKA). This mark shows the very good level of the e-course and is a recognition of the creator and maker of the e-course who has achieved excellent results using digital technology. The quality mark of the e-course confirms to the learner that the course and the level of its execution meet the quality requirements.

Web: https://courses.cs.ut.ee/2022/atva/fall

Grant from Estonian Ministry of Education and Research (2020, 2021, 2022)

Duration:

  • 14 Mar 2022 – 09 May 2022
  • 15 Mar 2021 – 10 May 2021
  • 06 Apr 2020 –  01 Jun 2020

Artificial Intelligence Entry-level Course is a massive online course with the goal of introducing most important artificial intelligence concepts and areas of application. The course is designed for the learners without any background in programming or artificial intelligence. 

Within this course, you can familiarise yourself with the following topics:

  • machine learning, artificial neural networks;
  • image processing, object recognition;
  • self-driving cars;
  • natural language processing;
  • business analytics;
  • human-robot interaction;
  • artificial intelligence in medicine and health data analysis.

Web: https://courses.cs.ut.ee/2022/tehisintellekti_algkursus/spring

Grant from Estonian Ministry of Education and Research (2021, 2022)

Duration: 

  • 04 Oct 2022 – 08 Nov 2022
  •  04 Oct 2021 – 08 Nov 2021

Data Science for Business for Swedbank Tech University  is a 5-week online course (MOOC) with individual feedback from the course instructors. The purpose of  course is to introduce the possibilities of data science in the company and to show the application of data science in decision-making processes using industry specific examples.

Web: https://moodle.ut.ee/course/view.php?id=11625
Funded by Swedbank

Duration: 17 Jan 2022 – 10 Feb 2022

In the future, artificial intelligence and data science will be an integral part of competitive business processes. But what it actually means, where to start and what are the possibilities that come with it, these are questions that need answers right now. ”AI projects planning and executing at industrial enterprise” is a training program where we present the benefits and opportunities of adopting artificial intelligence and data science in the decision-making processes of industrial companies.
During the training we will talk about:

  • about the nature of artificial intelligence and data science and the possibilities for industrial companies;
  • about various data science techniques (e.g. machine learning and descriptive business analytics);
  • planning of projects implementing artificial intelligence

Web: https://aire-edih.eu/yritus/?30531

Funded by AI&Robotics Estonia