Data Analysis and Data Visualization (5cr)
Code: 5Y00FD88-3003
General information
- Enrolment period
- 15.11.2021 - 23.01.2022
- Registration for the implementation has ended.
- Timing
- 01.01.2022 - 08.05.2022
- Implementation has ended.
- Credits
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- MD in Data Expertise and Artificial Intelligence
- Campus
- TAMK Main Campus
- Teaching languages
- Finnish
- Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
- Teachers
- Esa Kujansuu
- Pekka Pöyry
- Course
- 5Y00FD88
Objectives (course unit)
The student knows what data analysis and data visualization mean. The student is able to analyze data by various methods and produce data visualizations suitable for the need. The student knows the data of his / her field and its possibilities for analysis and visualization.
Content (course unit)
Theory, methods and techniques of data analysis. Various data visualization techniques. Utilization of a suitable analysis and visualization tool or tools. The student knows what data analysis and data visualization mean. The student is able to analyze data by various methods and produce data visualizations suitable for the need. The student knows the data of his / her field and its possibilities for analysis and visualization.
Assessment criteria, satisfactory (1-2) (course unit)
The student knows what data analysis and data visualization is and knows how to do data analysis and visualization. The student recognizes data related to his / her field.
Assessment criteria, good (3-4) (course unit)
The student is able to analyze data and produce some data visualizations. The student knows the data of his / her field and knows about the possibilities of analysis and visualization.
Assessment criteria, excellent (5) (course unit)
The student is able to analyze data in many ways and produces various data visualizations. The student has a good understanding of the data in his / her field and its analysis and visualization possibilities.
Location and time
moodlessa aikataulu
Exam schedules
ei tenttiä
Assessment methods and criteria
Arvosana muodostuu ennakkotehtävien, harjoitusten ja oppimistehtävän perusteella.
Ennakkotehtävistä ja harjoituksista voi saada 50 pistettä ja oppimistehtävästä 50 pistettä.
Yhteispisteet 100. Seuraavassa pisteet ja arvosana:
0 0
30 1
45 2
60 3
75 4
90 5
Oppimistehtävän toimeksianto julkaistaan tabulassa kurssin aikana.
Assessment scale
0-5
Teaching methods
lähiopetus, ennakkotehtävät, oppimistehtävä, viikkoharjoitukset
Learning materials
moodlessa materiaali
Student workload
7*4 tuntia opetus, 105 h itsenäistä
Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)
Opiskelja tietää, mitä on data-analyysi ja datan visualisointi sekä osaa ohjatusti tehdä datan analyysiä ja visualisointeja. Opiskelija tunnistaa omaan alaansa liittyvän datan.
Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)
Opiskelija osaa analysoida dataa ja tuotaa joitakin datan visualisointeja. Opiskelija tuntee oman alansa datan ja tietää sen analysointi- ja visualisointimahdollisuuksista.
Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)
Opiskelija osaa analysoida dataa monipuoisesti ja tuotaa useita erilaisia datan visualisointeja. Opiskelija ymmärtää erinomaisesti oman alansa datan ja sen analysointi- ja visualisointimahdollisuudet.