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:

• The course starts with the lecture on September 1, 2022. There will be no practice sessions during the first week. The first practice sessions are on September 5 (groups 1 and 2), September 6 (groups 3, 4, 5, and 6) and September 7 (groups 7 and 8). The first homework is due on September 19 at noon.
• Lectures (Meelis Kull) - all will be held online only and recorded:
  • Thursday 14:15 - 16:00, online - Please log in here on this course homepage to see the link for online participation.
• Practice Sessions - will be held only physically (except if we are requested to go fully online):
  • Group 1: Monday 16:15 - 18:00 Narva mnt 18, room 2010 (Victor Pinheiro) in English
  • Group 2: Monday 12:15 - 14:00 Narva mnt 18, room 1024 (Victor Pinheiro) in English
  • Group 3: Tuesday 10:15 - 12:00 Narva mnt 18, room 1022 (Victor Pinheiro) in English
  • Group 4: Tuesday 10:15 - 12:00 Narva mnt 18, room 1008 (Markus Kängsepp) in Estonian
  • Group 5: Tuesday 12:15 - 14:00 Narva mnt 18, room 1021 (Ingvar Baranin) in Estonian
  • Group 6: Tuesday 12:15 - 14:00 Narva mnt 18, room 2010 (Friedrich Krull) in Estonian
  • Group 7: Wednesday 12:15 - 14:00 Narva mnt 18, room 2034 (Victor Pinheiro) in English
  • Group 8: Wednesday 12:15 - 14:00 Narva mnt 18, room 2045 (Anna Aljanaki) in Estonian
• Homework deadlines: Mondays at noon (12:00)

Contacts:

• Course forum: https://campuswire.com/c/GAF3154B4 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. After the first lecture, 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 SIS/ÕIS). 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:
  • Victor Pinheiro (victor.pinheiro@ut.ee)
  • Anna Aljanaki (anna.aljanaki@ut.ee)
  • Markus Kängsepp (markus.kangsepp@ut.ee)
  • Friedrich Krull (krullfriedrich@gmail.com)
  • Ingvar Baranin (ingvar@tmd.ee)

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 (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 (threshold 20 points), at least 50% from the project (threshold 10 points) and at least 50% from the exam (threshold 20 points). If having a written exam with physical presence turns out to be impossible due to the pandemic, then we will either organize an online exam or multiply the points from the homeworks and project by 1.67.

Links to previous courses:

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