Introduction to Data Analysis
BASIC DATA
course listing
A - main register
course code
ITO8010
course title in Estonian
Sissejuhatus andmeanalüüsi
course title in English
Introduction to Data Analysis
course volume CP
-
ECTS credits
6.00
to be declared
yes
fully online course
not
assessment form
Examination
teaching semester
autumn - spring
language of instruction
Estonian
English
Study programmes that contain the course
code of the study programme version
course compulsory
IADM18/25
yes
Structural units teaching the course
IT - Department of Software Science
Course description link
Timetable link
View the timetable
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Andmeanalüüsi alaste baasoskuste omandamine tasemel, kust on võimalik jätkata iseseisva õppimisega
course aims in English
Acquiring the skills for Data Analysis at the level from where the students are able to continue independently their future studies
learning outcomes in the course in Est.
Kursuse läbinud üliõpilane:
- oskab indetifitseerida ärilisi probleeme, mida saab lahendada andmete / ärianalüüsi tehnikatega;
- tunneb andme- ja ärianalüüsi teooria ning praktika kontseptsioone ja põhimõtteid;
- tunneb andmete eeltöötluse meetodeid ja protsesse;
- teab andmekaeve ja masinõppe meetodeid ja meetodite valikuid;
- oskab iseloomustada andmeid;
- suudab visualiseerida andmeid;
- omab praktilist arusaamist andmeanalüüsi etappidest, nende etappide vajadusest ja etappidevahelistest seostest.
learning outcomes in the course in Eng.
After completing the course, the student:
- is able to identify business problems that can solve using data and Data Analysis;
- understands the meanings of Data Analysis concept,
- knows what data preparation is;
- knows data mining and machine learning methods and is able to choose correct method to solve particular problem;
- is able to describe data;
- is able to visualize data;
- has practical knowledge of the Business Analysis stages and phases and, relationships between these stages and phases.
brief description of the course in Estonian
Kursuse käigus õpitakse põhiteadmisi andmeanalüüsi teostamiseks; andmeanalüüsi lähenemist tänapäevases andmeterikkas majanduses ning võimalusi suurest hulgast andmetest peidetud mustrite, regulaarsuste, seaduspärasuste ning trendide leidmiseks. Õpitakse tundma andmeanalüüsiks vajalike andmete hankimise, eeltöötlemise, talletamise, jms. põhimeetodeid ja -kontseptsioone; kursus annab ülevaate andmeanalüüsi ning andmekaevandamise põhimõistetest, meetoditest ning rakendustest. Aine läbimisel omandavad üliõpilased oskuse analüüsida ettevõtte või asutuse äriandmeid ja pakkuda sobilikke lahendusi ettevõtte või asutuse majandusnäitajate parandamiseks andmete abil.
brief description of the course in English
The course is intended to give a broad introduction to the field of Data Analysis/Business Intelligence. Apart from the theoretical foundation of the field of decision analysis, the course also introduces the foundation of data warehouses, data preparation, data visualization, etc. After completing the course, the student is able to analyze business processes, provide and apply tailor-made data-related solutions to improve the performance of business process/company/institution.
type of assessment in Estonian
Lõpphinne = 0,3 osavõtt + 0,3 iseseisvad kodutööd + 0,4 eksam
type of assessment in English
Final grade = 0.3 participation + 0.3 independent homework + 0.4 exam
independent study in Estonian
Iseseisva kodutööna tuleb üliõpilasel lahendada õppejõu ettevalmistatud praktilised ülesanded. Ülesanded on seotud kursuse sisuga. Arutelud teiste tudengitega lubatud, hindamiseks esitatakse originaalsed lahendused ning ideed.
independent study in English
You are expected to finish practical exercises provided by lecturer. The assignments are directly related to the course content and are designed to link the content with real-world problems and practices. You may discuss homework with other students, but the specific ideas and their expression must be yours and yours alone.
study literature
Aines kasutatav kirjandus on loetletud laiendatud ainekavas ja õppeaine Moodle'i kodulehel.
study forms and load
daytime study: weekly hours
2.5
session-based study work load (in a semester):
lectures
0.5
lectures
6.0
practices
2.0
practices
33.5
exercises
0.0
exercises
0.0
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2025/2026 autumn
Kristian Allikmaa, IT - Department of Software Science
Estonian
    display more
    2024/2025 spring
    Kristian Allikmaa, IT - Department of Software Science
    Estonian
      ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
      2024/2025 autumn
      Kristian Allikmaa, IT - Department of Software Science
      Estonian
        2023/2024 spring
        Kristian Allikmaa, IT - Department of Software Science
        Estonian
          2023/2024 autumn
          Kristian Allikmaa, IT - Department of Software Science
          Estonian
            2022/2023 spring
            Kristian Allikmaa, IT - Department of Software Science
            Estonian
              2022/2023 autumn
              Kristian Allikmaa, IT - Department of Software Science
              Estonian
                2021/2022 spring
                Kristian Allikmaa, IT - Department of Software Science
                Estonian
                  ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                  2021/2022 autumn
                  Kristian Allikmaa, IT - Department of Software Science
                  Estonian
                    ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                    2020/2021 spring
                    Kristian Allikmaa, IT - Department of Software Science
                    Estonian
                      ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                      2020/2021 autumn
                      Kristian Allikmaa, IT - Department of Software Science
                      Estonian
                        ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                        2019/2020 spring
                        Kristian Allikmaa, IT - Department of Software Science
                        Estonian
                          ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                          2019/2020 autumn
                          Kristian Allikmaa, IT - Department of Software Science
                          Estonian
                            ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                            2018/2019 autumn
                            Kristian Allikmaa, IT - Department of Software Science
                            Estonian
                              ITO8010_Introduction_to_Data_Analysis_evaluation_criteria.pdf 
                              Course description in Estonian
                              Course description in English