Data Analysis
BASIC DATA
course listing
A - main register
course code
ICM0031
course title in Estonian
Andmeanalüüs
course title in English
Data Analysis
course volume CP
-
ECTS credits
6.00
to be declared
yes
assessment form
Pass/fail assessment
teaching semester
spring
language of instruction
Estonian
English
Prerequisite(s)
Prerequisite 1
Databases (ICM0005)Study programmes that contain the course
code of the study programme version
course compulsory
no
Structural units teaching the course
IC - IT College
Timetable link
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Kursuse eesmärgiks on tagada teadmised ja oskused analüüsimise põhialustest.
course aims in English
The goal is mastering of main concepts of data analysis.
learning outcomes in the course in Est.
Kursuse läbinud üliõpilane:
- suudab organiseerida andmevahetust andmetöötlustarkvara ja andmebaaside vahel;
- teab ja oskab kasutada andmeanalüüsi statistilisi meetodeid;
- teab ja oskab kasutada masinõppe meetodeid;
- oskab adekvaatselt tõlgendada analüüsi tulemusi.
- suudab organiseerida andmevahetust andmetöötlustarkvara ja andmebaaside vahel;
- teab ja oskab kasutada andmeanalüüsi statistilisi meetodeid;
- teab ja oskab kasutada masinõppe meetodeid;
- oskab adekvaatselt tõlgendada analüüsi tulemusi.
learning outcomes in the course in Eng.
The student:
- is able to organize data exchange between data analysing software and data bases.
- knows and masters the statistical methods of data analysis.
- knows and masters methods of machine learning.
- implements adequately the results of analysis.
- is able to organize data exchange between data analysing software and data bases.
- knows and masters the statistical methods of data analysis.
- knows and masters methods of machine learning.
- implements adequately the results of analysis.
brief description of the course in Estonian
Kursusel käsitletakse andmeanalüüsi erinevaid aspekte, kasutades MATLAB-keskkonda.
- Andmete import erinevatest andmekogudest ja –baasidest.
- Andmete visualiseerimine.
- Andmete analüüsimine statistiliste meetoditega.
- Andmekaeve põhimõisted ja –meetodid.
- Masinõppe põhimõisted ja –meetodid.
- Andmete import erinevatest andmekogudest ja –baasidest.
- Andmete visualiseerimine.
- Andmete analüüsimine statistiliste meetoditega.
- Andmekaeve põhimõisted ja –meetodid.
- Masinõppe põhimõisted ja –meetodid.
brief description of the course in English
The different aspects of data analysis will be handled, using MATLAB-environment.
- Import of data from different databases and data sets.
- Visualizing data.
- Analysing data using the statistical methods.
- Main concepts and - methods of data mining.
- Main concepts and - methods of machine learning.
- Import of data from different databases and data sets.
- Visualizing data.
- Analysing data using the statistical methods.
- Main concepts and - methods of data mining.
- Main concepts and - methods of machine learning.
type of assessment in Estonian
Arvestus
type of assessment in English
Pass/fail assessment
independent study in Estonian
-
independent study in English
-
study literature
- Zielesny A. From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence. Springer 2018.
- Gdeisat, M., Lilley, F. MATLAB® by Example: Programming Basics. Elsevier (e-raamat).
(Toomas Lepikult) https://moodle.taltech.ee/course/view.php?id=30091
- Gdeisat, M., Lilley, F. MATLAB® by Example: Programming Basics. Elsevier (e-raamat).
(Toomas Lepikult) https://moodle.taltech.ee/course/view.php?id=30091
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
2.0
practices
-
exercises
0.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2024/2025 spring
Toomas Lepikult, IC - IT College
Estonian