Data Processing and Visualization
BASIC DATA
course listing
A - main register
course code
EVR0310
course title in Estonian
Andmetöötlus ja visualiseerimine
course title in English
Data Processing and Visualization
course volume CP
-
ECTS credits
3.00
to be declared
yes
fully online course
not
assessment form
Graded assessment
teaching semester
autumn - spring
language of instruction
Estonian
English
Prerequisite(s)
Prerequisite 1
Applied Statistics (RAM0510)Study programmes that contain the course
Structural units teaching the course
EV - Virumaa College
Timetable link
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Aine eesmärk on:
- anda ülevaade andmetöötluse ja visualiseerimise põhiprintsiipidest, andmeanalüüsi ja masinõppe levinumatest meetoditest;
- õpetada vastava tarkvara kasutamist andmete hankimisest ning lihtsamate andmetöötluse, andmeanalüüsi ja andmete visualiseerimise ülesannete lahendamiseks.
- anda ülevaade andmetöötluse ja visualiseerimise põhiprintsiipidest, andmeanalüüsi ja masinõppe levinumatest meetoditest;
- õpetada vastava tarkvara kasutamist andmete hankimisest ning lihtsamate andmetöötluse, andmeanalüüsi ja andmete visualiseerimise ülesannete lahendamiseks.
course aims in English
The aim of the course is to:
- provide an overview of the basic principles of data processing, visualization and the most common methods of data analysis and machine learning;
- teach the use of relevant software for data collecting and solving simpler data processing, data analysis and data visualization tasks.
- provide an overview of the basic principles of data processing, visualization and the most common methods of data analysis and machine learning;
- teach the use of relevant software for data collecting and solving simpler data processing, data analysis and data visualization tasks.
learning outcomes in the course in Est.
Õppeaine läbinud üliõpilane:
- selgitab andmetöötluse põhiprintsiipe ja andmete visualiseerimise ning analüüsimise põhimeetodeid;
- omab teadmisi masinõppe baasmeetoditest;
- valib sobiva meetodi ja rakendab vastavat tarkvara andmete hankimiseks, eeltöötluseks, visualiseerimiseks, analüüsimiseks, vormistab ja interpreteerib saadud andmeanalüüsi tulemusi;
- kasutab SQL keelt lihtsamate päringute tegemiseks.
- selgitab andmetöötluse põhiprintsiipe ja andmete visualiseerimise ning analüüsimise põhimeetodeid;
- omab teadmisi masinõppe baasmeetoditest;
- valib sobiva meetodi ja rakendab vastavat tarkvara andmete hankimiseks, eeltöötluseks, visualiseerimiseks, analüüsimiseks, vormistab ja interpreteerib saadud andmeanalüüsi tulemusi;
- kasutab SQL keelt lihtsamate päringute tegemiseks.
learning outcomes in the course in Eng.
After completing this course, the student:
- explains the basic principles of data processing and the basic methods of data visualization and analysis;
- knows the basic methods of machine learning
- applies the appropriate method and software for data collecting, pre-processing, visualization, analysis, as well as interprets and formats the results of the implemented methods;
- uses the SQL language to write simple queries.
- explains the basic principles of data processing and the basic methods of data visualization and analysis;
- knows the basic methods of machine learning
- applies the appropriate method and software for data collecting, pre-processing, visualization, analysis, as well as interprets and formats the results of the implemented methods;
- uses the SQL language to write simple queries.
brief description of the course in Estonian
Andmete hankimine, eeltöötlus, analüüs ja visualiseerimine vastava tarkvara abil. Andmeanalüüsi kirjeldavad ja järeldavad meetodid. Masinõpe ja selle meetodid. Andmete kasutamine ennustamisel. Avalikke andmebaaside kasutamine. SQL keel.
brief description of the course in English
Data collecting, pre-processing, analysis and visualization using appropriate software. Exploratory and inferential data analysis. Fundamental machine learning methods for basic data analytics problems. Getting data from public databases. SQL language.
type of assessment in Estonian
-
type of assessment in English
-
independent study in Estonian
-
independent study in English
-
study literature
- E-kursuse materjalid.
- Steven S. Skiena, The Data Science Design Manual, Springer, 2017
- Flach, P. Machine learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, 2012
- Steven S. Skiena, The Data Science Design Manual, Springer, 2017
- Flach, P. Machine learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, 2012
study forms and load
daytime study: weekly hours
2.0
session-based study work load (in a semester):
lectures
1.0
lectures
-
practices
0.0
practices
-
exercises
1.0
exercises
8.0
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2025/2026 autumn
Olga Dunajeva, EV - Virumaa College
Estonian