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.

Course information:

• We do not use Moodle in this course. All information is either here at the course homepage or announced at the course forum (link given below).
• The course will have pre-recorded lectures made available each week. During the lecture timeslot there will be a consultation for those who have questions. The first consultation will be on September 7, 2023. There will be no practice sessions during the first week. The first practice sessions are on September 11 (groups 1 and 2), September 12 (groups 3, 4, 5, 6, and 7) and September 13 (group 8). The first homework is due on September 25 at noon.
• Lectures (Meelis Kull) - all will be pre-recorded. If you have any questions then you can ask them either on course forum or at the consultation held on
  • Thursday 14:15 - 16:00, online - Please log in here on this course homepage to see the link for online participation.
• Practice Sessions:
  • Group 1: Monday 16:15 - 18:00 online (Hasan Tanvir) in English - link is here: https://ut-ee.zoom.us/j/96295464719?pwd=OThMYjVIVjB0ZHVvZzJ4VUtDdnA2UT09
  • Group 2: Monday 16:15 - 18:00 Narva mnt 18, room 2010 (Markus Haug) in Estonian
  • Group 3: Tuesday 10:15 - 12:00 Narva mnt 18, room 1008 (Friedrich Krull) in Estonian
  • Group 4: Tuesday 10:15 - 12:00 Narva mnt 18, room 2048 (Karl Kaspar Haavel) in Estonian
  • Group 5: Tuesday 12:15 - 14:00 Narva mnt 18, room 2048 (Anna Aljanaki) in Estonian
  • Group 6: Tuesday 12:15 - 14:00 Narva mnt 18, room 1022 (Hasan Tanvir) in English
  • Group 7: Tuesday 16:15 - 18:00 online (Novin Shahroudi) in English - - Please log in here on this course homepage to see the link for online participation.
  • Group 8: Wednesday 12:15 - 14:00 Narva mnt 18, room 2010 (Carel Kuusk) in Estonian
• Homework deadlines: Mondays at noon (12:00)

Contacts:

• Course forum: https://campuswire.com/c/GE80372A5 We will use Campuswire 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. By September 11, you should have all received a welcome e-mail that invites you to Campuswire - don't ignore it and register there (it is sent to your address that is in the study information system). If you somehow didn't get the e-mail then please ask your practice session teacher.
• Lecturer: Meelis Kull (meelis.kull@ut.ee)
• Teaching Assistants:
  • Anna Aljanaki (anna.aljanaki@ut.ee)
  • Friedrich Krull (krullfriedrich@gmail.com)
  • Markus Haug
  • Novin Shahroudi
  • Karl Kaspar Haavel
  • Hasan Tanvir
  • Carel Kuusk

Grading and requirements:

The grade is calculated from the total number of points (max 100). The points can be earned as follows:

• Homeworks (20 points): there will be 10 homeworks, each worth 2 points;
• Group project and presentation at the poster session (30 points);
• Written exam (50 points);
• Additional points can be earned from bonus tasks within homeworks;
• Attending at least 8 of the 10 practice sessions is compulsory: after missing 2 practice sessions, each additional missed practice session results in losing 2 points (except for medical reasons, then please contact the practice group's teacher)

In order to pass the course, the student must get at least 50% from homeworks after applying above attendance penalties (threshold 10 points), at least 50% from the project (threshold 15 points) and at least 50% from the exam (threshold 25 points).

Links to previous courses:

2022, 2021, 2020, 2019, 2018, 2017 fall, 2017 spring, 2016, 2015, 2014, 2013, 2012, 2011, 2009