Course syllabus - Data Management and Datafication
Scope
7.5 credits
Course code
DVA256
Valid from
Autumn semester 2021
Education level
First cycle
Progressive Specialisation
G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements).
Main area(s)
Computer Science
School
School of Innovation, Design and Engineering
Ratified
2020-12-15
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
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Other Materials
Handout notes and list of literature will be provided during the course.
Objectives
The course aims to give an introduction to the theoretical foundation of data management, modelling and datafication. It covers the key concepts and insights about data collection, data manipulation, value creation and extraction of knowledge from the data for making meaningful decisions.
Learning outcomes
After completing the course the student shall be able to:
1. describe the basics of data, data collection,data documentation and ethical issues in data handling,
2. describe the principle of data modelling,
3. demonstrate the ability to describe concepts of datafication, and its consequences in meaning-making process
4. demonstrate the ability to explain the challenges associated with structured and unstructured data and
5. demonstrate the ability to design and develop solutions to create value from data.
Course content
- Data and data types, data collection techniques and different standards to be used for data and metadata format,
- Documentation and data management approaches (e.g., plans for archiving data and samples) including ethical issues,
- Concepts of data modelling e.g., relational data model and others,
- Methodology and technology to explore datafication i.e., conversion of qualitative aspects of data into quantified data,
- Different Information retrieval algorithms including statistical and probabilistic information retrieval
- Data transformation methods such as signal processing algorithms e.g., Fourier transform, concept of information entropy and dealing with high dimensional data.
Specific requirements
Programming 7,5 credits, Data structures, algorithms and program design 7,5 credits or equivalent and Vector algebra 7,5 credits, or equivalent.
Examination
Exercise (OVN1), 3 hp, examines the learning outcomes 3-5, marks Fail (U), 3, 4, or 5.
Written assignment (INL1) 3 hp, examines the learning outcomes 1-5, marks Fail (U), 3, 4, or 5.
Seminar (SEM1), 1,5 hp, examines the learning outcomes 1-5, marks Fail (U) or Pass (G).
A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.
Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.
Grade
Pass with distinction, Pass with credit, Pass, Fail