The course is included in these curricula and study modules
- Big data analytics 2016 - Big data analytics
- Big data analytics 2017 - Big data analytics
- Big data analytics 2018 - Big data analytics
- Big data analytics 2019 - Big data analytics
- Big data analytics 2020 - Big data analytics
- Big data analytics 2021 - Big data analytics - specialised professional studies
- Big data analytics 2022 - Big data analytics - specialised professional studies
- Big data analytics 2023 - Big data analytics - specialised professional studies
The course takes place in period
4 (2023-03-20 to 2023-07-31)
Level/category
Teaching language
English
Type of course
Compulsory
Cycle/level of course
Second
Recommended year of study
1
Total number of ECTS
5 cr
Competency aims
The aim is to make student capable of finding AI
opportunities in different domains.
Learning outcomes
The student should be able to ask managerial and
specific questions that can be answered through
the use of AI methods based on machine learning.
Student can implement a project of its own in the
field of big data analytics.
Course contents
The students develop an understanding for
planning the analytical process; data-related
requirement handling, domain knowledge/modelling
expertise and verification of results. Each student
completes an industry cap-stone project as part of
the course.
Prerequisites and co-requisites
Knowledge of analytical methods and data analysis is required. Previous courses in predictive and descriptive machine learning as well as visualization are recommended.
Recommended or required reading
Scientific papers and books recommended during the lectures.
Study activities
- Lectures - 20 hours
- Individual- and group instruction - 10 hours
- Project- and production work/artistic activities - 80 hours
- Internet-based studies - 25 hours
Workload
- Total workload of the course: 135 hours
- Of which autonomous studies: 135 hours
- Of which scheduled studies: 0 hours
Mode of Delivery
Multiform education
Assessment methods
Essays, reports, productions and portfolio
Assessment requirements
To pass the course the student must participate
and do the assignments given during the course.
Teacher
- Björk Kaj-Mikael
- Espinosa Leal Leonardo
- Scherbakov-Parland Andrej
- Westerlund Magnus
Examiner
Westerlund Magnus
Home page of the course
Group size
No limit (29 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2023-03-06 to 2023-04-02
Assessment methods
Date will be announced later - Reports and productions
Date | Time | Room | Title | Description | Organizer |
---|---|---|---|---|---|
2023-04-13 | 13:00 - 18:00 | Analytical Service Development | Zoom link: bit.ly/ASD2223 | Björk Kaj-Mikael Espinosa Leal Leonardo Scherbakov-Parland Andrej Westerlund Magnus |
|
2023-04-14 | 13:00 - 18:00 | Analytical Service Development | Zoom link: bit.ly/ASD2223 | Björk Kaj-Mikael Espinosa Leal Leonardo Scherbakov-Parland Andrej Westerlund Magnus |
|
2023-04-27 | 13:00 - 18:00 | Analytical Service Development | Zoom link: bit.ly/ASD2223 | Björk Kaj-Mikael Espinosa Leal Leonardo Scherbakov-Parland Andrej Westerlund Magnus |
|
2023-04-28 | 13:00 - 18:00 | Analytical Service Development | Zoom link: bit.ly/ASD2223 | Björk Kaj-Mikael Espinosa Leal Leonardo Scherbakov-Parland Andrej Westerlund Magnus |
|
2023-05-04 | 13:00 - 18:00 | Analytical Service Development | Zoom link: bit.ly/ASD2223 | Björk Kaj-Mikael Espinosa Leal Leonardo Scherbakov-Parland Andrej Westerlund Magnus |
|
2023-05-11 | 13:00 - 18:00 | Analytical Service Development | Zoom link: bit.ly/ASD2223 | Björk Kaj-Mikael Espinosa Leal Leonardo Scherbakov-Parland Andrej Westerlund Magnus |