Introduction to Data Science (Sissejuhatus andmeteadusesse) - LTAT.02.002
This course gives a brief overview of the basic concepts, principles and practice of data science. The main goal is to learn to plan and carry out a simple practical data science project. The course covers the main methods for descriptive data analysis and visualization, frequent pattern mining, cluster analysis, principal components analysis, common methods of machine learning for classification and regression (including deep neural networks), managing data and interpreting results of statistical tests. The main stages of data science projects are discussed and available software tools reviewed. Homeworks are to be solved using the programming language Python 3 and its libraries.
- The course starts with the lecture on September 5, 2019. There will be no practice sessions on the first week. The first practice sessions are on September 9 (groups 1 and 2), September 10 (groups 5 and 6) and September 11 (groups 3 and 4). The first homework is due on September 23 at noon.
- Lectures (Meelis Kull):
- Thursday 14:15 - 16:00, J. Liivi 2, room 111
- Practice Sessions:
- Please check your e-mail if you do not speak Estonian and are currently registered in a group that is marked below as planned to run in Estonian if possible. We are trying hard to make everyone happy.
- Group 1: Monday 16:15 - 18:00 (J. Liivi 2, room 206) (Meelis Kull) in English
- Group 2: Monday 16:15 - 18:00 (J. Liivi 2, room 403) (Laura Ruusmann) in Estonian
- Group 3: Wednesday 12:15 - 14:00 (J. Liivi 2, room 404) (Meelis Kull) in Estonian
- Group 4: Wednesday 12:15 - 14:00 (Ülikooli 17, room 220) (Markus Kängsepp) in English
- Group 5: Tuesday 16:15 - 18:00 (J. Liivi 2, room 207) (Markus Kängsepp) in Estonian
- Group 6: Tuesday 16:15 - 18:00 (J. Liivi 2, room 224) (Laura Ruusmann) in English
- Homework deadlines: Monday at noon (12:00)
- Course forum: https://piazza.com/ut.ee/fall2019/ltat02002
We will use Piazza for questions and discussions. In the forum, you can post questions (also anonymously) about homeworks or course organization etc. And we can keep the discussion separate for different topics. After the first lecture, you should all receive a welcome e-mail that invites you to piazza - don't ignore it and register there (it is sent to your address that is in the study information system SIS/ÕIS). If you somehow don't get the e-mail you can register here (just mark that you are a student and press "Join Classes"). Then you have to fill some information about yourself, which is a little annoying, but do it anyway. The home page of the course forum is here and you can click on Q&A to get to the forum part. That's it.
- Lecturer: Meelis Kull (email@example.com)
- Teaching Assistants:
- Markus Kängsepp (firstname.lastname@example.org)
- Laura Ruusmann (email@example.com)
Grading and requirements:
The grade is calculated from the total number of points (max 100). The points can be earned as follows:
- Homeworks (40 points): there will be 10 homeworks, each worth 4 points;
- Group project and presentation at the poster session (20 points);
- Written exam (40 points);
- Additional points can be earned from bonus tasks within homeworks;
- Attending at least 9 of the 12 practice sessions is compulsory: after missing 3 practice sessions each additional missed practice session results in losing 5 points.
In order to pass the course, the student must get at least 50% from homeworks (threshold 20 points), at least 50% from the project (threshold 10 points) and at least 50% from the exam (threshold 20 points).
Links to previous courses: