course aims in Estonian
Aine eesmärk on anda üliõpilastele olulised IT-teadmised ja -oskused, mis on vajalikud tehnoloogiliste lahenduste väljatöötamiseks ning rakendamiseks, keskendudes energiasektori asjakohastele probleemidele. Selle eesmärgi saavutamiseks kasutatakse andmeteaduse valdkonna statistilisi ja masinõppe vahendeid.
course aims in English
The objective of the course is to equip students with the essential IT knowledge and skills required to facilitate the development and implementation of technological solutions with a focus on relevant problems in the energy sector. This objective will be achieved by leveraging statistical and machine learning tools from the domain of data science.
learning outcomes in the course in Est.
Õppeaine läbinud üliõpilane:
- teisendab energiasektori probleeme standardseteks andmetöötlusprobleemideks;
- valib, kombineerib ja rakendab sobivaid andmetöötlus-, modelleerimis- ning analüüsimeetodeid;
- uurib ja visualiseerib andmeid;
- rakendab asjakohaseid mõõdikuid ja esitab andmeid tehnilistes aruannetes, et esitada ja põhjendada tulemusi.
learning outcomes in the course in Eng.
Upon completion of the course, the student:
- transforms challenges in the energy sector into standard data science problems;
- chooses, combines, and applies appropriate methods for data processing, modelling, and analysis;
- examines and visualises the data;
- applies appropriate metrics and presents data in technical reports to present and justify the findings.
brief description of the course in Estonian
Kursus on mõeldud üliõpilastele, kes omavad andmetega töötamiseks asjakohaseid teadmisi.
Kursus järgib kombineeritud õppemeetodit, mis koosneb loengutest ja praktilistest töödest. Loengutes käsitletakse andmeteaduse teoreetilisi aspekte ja algoritmilisi aluseid. Teooria demonstreerimiseks kasutatakse energiasektori andmeid, mida rakendatakse peamiselt programmeerimiskeele Python vahendusel. Praktikumide eesmärk on pakkuda üliõpilastele näiteid probleemide lahendamise kohta, et täiendada teoreetilisi teadmisi ja aidata lahendada konkreetseid probleeme või tulevasi väljakutseid.
Kursus hõlmab andmetöötluse põhimõisteid, nagu andmete töötlemine, modelleerimine ja analüüs fookusega aegridadele. Üliõpilased õpivad ka visualiseerimise põhimõtteid ning nende tarkvaralist rakendamist. Lisaks käsitletakse kursusel asjakohaseid energeetikaga seotud probleeme ja tutvustatakse tehnoloogilistele lahenduste leidmisele orienteeritud sobivaid tehnikaid.
Pärast kursuse lõpetamist on üliõpilastel põhjalik arusaam energiasektori oluliste probleemide lahendamiseks vajalikest meetoditest ja tehnikatest. Lisaks sellele arendavad saadud kogemused nii individuaalset kui ka koostöökompetentsi.
brief description of the course in English
The course is intended for students who have relevant knowledge for working with data.
The course follows a blended learning methodology, incorporating a combination of lectures and practical assignments. During the lectures, the course covers the theoretical and algorithmic foundations of data science. Data from the energy sector is utilized to demonstrate the theory, with implementation primarily using the Python programming language.
The purpose of practical sessions is to offer students examples of problem-solving to complement the theoretical concepts and to help in addressing particular issues or challenges they might face.
The course encompasses fundamental data science concepts such as data processing, modelling, and analysis with a focus on times series. Students will learn data visualization principles, along with their software implementation. The course encompasses fundamental data science concepts such as regression, classification, profiling, and time series analysis. Students will learn data processing and visualization principles, along with their software implementation. The course further addresses relevant energy-related problems and introduces appropriate techniques with a focus on technological solutions.
Upon course completion, students will possess a comprehensive understanding of the methods and techniques necessary to address significant challenges in the energy sector. Additionally, they will have developed both individual and collaborative work competencies.
type of assessment in Estonian
Hindeline eksam
type of assessment in English
Graded exam
independent study in Estonian
-
independent study in English
-
study literature
Forecasting: Principles and Practice, R. J. Hyndman, G. Athanasopoulus, 3rd ed, 2021, OTexts
The elements of statistical learning: Data mining, Inference, and Prediction, J. Friedman, T. Hastie, R. Tibshirani, 2nd ed., 2008
Energy Informatics, R. Watson, M.-C. Boudreau, Kindle edition, 2011
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lecturer in charge
Juri Belikov, kaasprofessor tenuuris (IT - tarkvarateaduse instituut)