STKD6500 Data Science for Social Innovations I Course description

The knowledge obtained in this course can be used to understand better our world (e.g., following the scientific approach). For example, natural environments (the sciences), social interactions, and engineering systems are often complex, and the underlying causal models are difficult to understand. Data-driven modeling and hypothesis generation is essential for understanding system behavior and interactions.

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 analytics approaches to problem solving based on big-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 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).

In health and human welfare, data analytics is key to cost efficiencies and sustainability of the healthcare infrastructure, as well as a represents a promising approach for devising prognostic interventions and novel therapies.

This course is aimed at teaching students about a set of tools and techniques that are state-of-the-art and commonly used for data analytics in the industry.

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.

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
• modern analytics (methods and tools) for building real-world data analytics applications

Skills

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

• apply data analytics to formulate a hypothesis, collect data, analyze it and reach conclusions about the data
• plan and design a real-world data analytics application
• identify and define a problem and craft a solution using data analytics

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 is organized around a series of lectures, workshops, and a project. The lectures will cover the key topics for the course. The workshops will provide some hands-on experience on the key topics introduced during lectures. The project will help to consolidate the knowledge acquired during the course. The students are expected to attend all the lectures. The project will be focused on solving a real-world problem. The solution will be presented at the end of the course.

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

• An analytics project where a problem is defined and data chosen to solve/answer the problem. The analytics project is to be completed individually or in groups of up to five students.

Examination system:

Oral examination. An oral presentation based on the analytics project completed as part of the coursework requirements. The oral presentation will be completed individually or in groups of up to five students, at the end of the summer school. The presentation should include the demonstration of the data analytics application based on the project, and the student(s) need(s) to deliver the PowerPoint presentation and/or any other supportive material prior the presentation. The analytics project and the oral presentation counts for 100% of the final grade.

The oral presentation cannot be appealed.

All support materials are allowed for both the oral presentation and for the individual project report.

The final assessment will be graded on a grading scale from A to E (A is the highest grade and E the lowest) and F for fail.

The knowledge obtained in this course can be used to understand better our world (e.g., following the scientific approach). For example, natural environments (the sciences), social interactions, and engineering systems are often complex, and the underlying causal models are difficult to understand. Data-driven modeling and hypothesis generation is essential for understanding system behavior and interactions.

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 analytics approaches to problem solving based on big-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 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).

In health and human welfare, data analytics is key to cost efficiencies and sustainability of the healthcare infrastructure, as well as a represents a promising approach for devising prognostic interventions and novel therapies.

This course is aimed at teaching students about a set of tools and techniques that are state-of-the-art and commonly used for data analytics in the industry.

Electronic governance (e-governance), generally understood as the use of information and communication technology (ICT) in various spheres of political life, plays a significant role in democratic societies. Researchers in computer and social sciences have examined e-governance as a tool for strengthening local democracy and realizing direct participation of citizens in political life.

The main topic of this course will focus on the role of ICT in enhancing the accessibility, transparency, and accountability of public services in contemporary democracies. Communication between citizens and government has historically been challenged by inefficiencies and limited opportunities for dialog.

The course will also provide a platform for further research, co-creation and co-production on the following topics:

• empirical analysis of ICT in political systes.
• comparative local e-governance in different types of democracies.
• local ICT policy reforms and their implementation.
• ethical reflections on security, privacy, and surveillance of e-governance solutions cooperation in intercultural and interdisciplinary communication and networkin.