STKD6500 Data Science for Social Innovations I Emneplan

The knowledge obtained in this course can be used to understand better our world, e.g., natural environments (the sciences), social interactions, and engineering systems via data science.

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The last decade or so has seen sizable decreases on costs to gather, store, and process data, creating a fertile environment for the use of data science to solve different problems based on systematic analysis of big-and thick data. Examples are: Increasing productivity and efficiency in business domains by guiding decision processes, improving the experience and interaction between the users and systems, or optimizing different environments (e.g., living space, energy footprint). In health and human welfare, data analytics is key to cost efficiencies and sustainability of the healthcare infrastructure. It also represents a promising approach for devising prognostic interventions and novel therapies.

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In the business domains, big-data analytics can support an increase on productivity or efficiency, by guiding decision processes. Similarly, in interactive and ubiquitous environments, big-data analytics can influence the interaction between the system and the users, to improve their experience (e.g., UX, user engagement), or to optimize their environment (e.g., living space, energy footprint).

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At the same time, we are living in an era of great systemic challenges, such as the Climate Crisis, pandemics, loss of natural biodiversity, and acute economic inequality. This course is aimed at introducing students to Data Science, a set of tools and techniques that are state-of-the-art and commonly used for analysing data; in order to support decision-making and/or to develop digital products. At the same time, the course aims at helping students to map how data science can be used to drive social innovation; to ultimately help tackling the challenges the humanity currently stands for.

This course supports OsloMet's ambition to drive progress on the implementation of the United Nations Sustainable Development Goals (SDGs).

Portfolio produced by the student

One half year of university studies (30 ECTS), in addition to the OsloMet International Summer School’s general requirement. The requirement needs to be met by application deadline.

After completing this course, the student should have the following learning outcome:

Knowledge

On successful completion of this course the student has knowledge of:

• the potential of data analytics for solving different real-life problems, with focus on social innovation
• how data science can be misused or is being misused and common pitfalls/side-effects of it
• modern methods and tools for building real-world data science applications

Skills

On successful completion of this course the student has the ability to:

• apply data science techniques to formulate a hypothesis, collect data, analyze it and reach conclusions about the data
• critically assess and draw conclusions from different sources of data
• plan and design a real-world data analytics application
• identify and define a problem within social innovation, and craft a solution using data analytics

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General Competence

On successful completion of this course the student can apply:

• data analytics principles
• methods/tools for data analytics and data visualization

The course deals with the practical application of laboratory analyses of cells and tissues related to pathological conditions. Basic knowledge of morphological analysis is necessary for a medical laboratory technician to plan, perform, quality assure and assess biomedical analyses. The course emphasises theory related to pre-analytical issues, relevant analyses and micro-examination of cell and tissue specimens. It also covers a selection of histological and cytological methods used to diagnose cancer, including immunological methods. Knowledge of cells and tissues and relevant methods of analysis is important in this part of the medical laboratory sciences field, where assessments are, in part, based on subjective criteria.

The course consists of the following topics, specified below as the number of credits:

• Histopathological techniques 4,5
• Cytology 2,5
• Immunohistochemistry 1,5
• Blood smears 0,5
• Urine microscopy 1,0
• Total: 10

Passed the first year of the programme.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student is capable of

• describing relevant theory and principles behind a selection of analyses
• explaining the connection between analysis results, disease mechanisms and disease progression
• defining relevant quality concepts

Skills

The student is capable of

• assessing the quality of analysis based on defined quality concepts
• using relevant instruments and techniques under supervision
• planning and using selected methods of analysis and assessing their reliability
• performing micro-examination of cell and tissue specimens with a certain degree of independence

General competence

The student is capable of

• demonstrating responsibility in analytical work
• taking responsibility for his or her own and fellow students- learning

Teaching methods mainly comprise practical laboratory work and micro-examination. Relevant theory is linked to practice through lectures, individual assignments and various forms of group assignments in addition to self-study. The student-s portfolio is an important learning tool in the course. Students give each other feedback on some of the written assignments.

The following required coursework must be approved before the student can take the exam:

• a minimum of 90% attendance in laboratory work
• a minimum of 80% attendance in scheduled group work
• laboratory reports in accordance with specified criteria

Exam content: The learning outcomes

Exam form: Individual oral exam, up to 30 minutes

Grade scale A-F