Data Analysis and Visualization (7 op)
Toteutuksen tunnus: 5G00FT11-3004
Toteutuksen perustiedot
- Ilmoittautumisaika
- 23.11.2023 - 07.01.2024
- Ilmoittautuminen toteutukselle on päättynyt.
- Ajoitus
- 12.01.2024 - 05.05.2024
- Toteutus on päättynyt.
- Laajuus
- 7 op
- Toteutustapa
- Lähiopetus
- Yksikkö
- Software Engineering
- Toimipiste
- TAMK Pääkampus
- Opetuskielet
- englanti
- Paikat
- 0 - 42
- Koulutus
- Bachelor's Degree Programme in Software Engineering
- Opettajat
- Ossi Nykänen
- Vastuuhenkilö
- Ossi Nykänen
- Ryhmät
-
22I260EADegree Programme in Software Engineering
-
22I260EBDegree Programme in Software Engineering
- Luokittelu
- CONTACT
- Opintojakso
- 5G00FT11
Osaamistavoitteet (Opintojakso)
The student understands basic concepts of Statistics and Classical Data Analysis. The student can collect and preprocess data for analysis and visualization. The student is able make appropriate data analyses and visualizations for the problem. The student can evaluate both the quality and the applicability of the results.
Sisältö (Opintojakso)
Course content is:
- Data collection and Data Preprocessing
- Visual analytics process
- Basic methods of Statistics and Classical Data Analysis
- Data analysis with Analytics Tools and Python
- Visualization models
- Critical evaluation of results
Arviointikriteerit, tyydyttävä (1-2) (Opintojakso)
Student can sufficiently implement data collection and data preprocessing for a given task. Student knows how to implement visual analytics processes. Student knows some basic methods of statistics and classical data analysis. The student can solve some given data analysis problems with analytics tools or python. Student can use given visualization models. Student understands the meaning of the results.
Arviointikriteerit, hyvä (3-4) (Opintojakso)
Student can implement data collection and data preprocessing for a given task. Student can implement visual analytics processes. Student knows and understands basic methods of statistics and classical data analysis. The student can solve given data analysis problems with analytics tools and python. Student knows and can exploit given visualization models. Student can evaluate the meaning of the results.
Arviointikriteerit, kiitettävä (5) (Opintojakso)
Student can implement data collection and data preprocessing with the appropriate methods. Student can implement various visual analytics processes. Student knows and understands in depth basic methods of statistics and classical data analysis. The student can solve versatile data analysis problems with analytics tools and python. Student knows and can exploit visualization models as appropriate. Student can critically evaluate and interpret the meaning of the results.
Tenttien ja uusintatenttien ajankohdat
No exam.
Arviointimenetelmät ja arvioinnin perusteet
Weekly assignments and project work presentation.
Arviointiasteikko
0-5
Opiskelumuodot ja opetusmenetelmät
Contact teaching
Assignments (the primary learning method)
Group work and presentation
Oppimateriaalit
Moodle course with links to additional material.
Opiskelijan ajankäyttö ja kuormitus
See the period timetable. See the Moodle course for instructions when and how to attend the contact teaching hours.
Lisätiedot
Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.