STKD6510 Data Science for Social Innovations II Course description

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.

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.

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).

It is recommended to have completed one full year of university studies (60 ECTS) before the program starts. This course assumes that the student has full working knowledge in the following topics:

• Algebra (matrix operations, solving systems of equations, attributes of a matrix)
• Statistics (descriptive statistics, scatterplots, histograms, how to represent a statistical distribution in a data sample, linear equation, logarithmic scale, understand the concept of probability and odds)
• Python programming (basic data structures such as list, dict, tuples, sets, strings; how to read/write files, define and call functions and libraries)

It is also an advantage to have some knowledge of empirical/experimental research methods

One half year of university studies (30 ECTS), in addition to the 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
• how data science can be misused or is being misused and common pitfalls/side-effects of it
• modern methods and tools for real-world data science applications
• basic visualization techniques to understand and/or communicate data and findings
• programming languages applicable to data analytics including, for example, SQL, Python, Matlab, R, or SPSS

Skills

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

• apply data analytics 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 science
• synthesize primary and secondary data sources the ability to accurately pinpoint trends, correlations and patterns in complex data sets

General Competence

On successful completion of this course the student can apply:

• data science principles
• methods/tools for data science and data visualization
• data analytics visualizations in presentations

The blended learning variant involves four weeks attendance-based teaching, in addition to independent study the online material. It includes lectures, classroom exercises, workshops, and a project.

The online learning variant may be taken as a fully independent study online course. The online variant includes online recorded lectures, workshops and a project.

• The lectures will cover the key topics for the course, and they will be intertwined with classroom exercises. The students are expected to attend all the lectures and participate in the classroom exercises. For the online variant, there will be no classroom exercises, but in the online material the exercises with the answers will be provided for independent study.
• The workshops will provide some hands-on experience on the key topics introduced during lectures. The workshops can be done individually or in groups. Workshops are optional but highly encouraged as they will prepare the student for the final project. For the online variant, there will be scheduled sessions where the instructor can answer questions/provide feedback on the workshop results.
• The project will help to consolidate the knowledge acquired during the course. The project will be focused on solving a real-world problem. The solution will be presented at the end of the course. For both variants, there will be scheduled sessions for providing feedback on the ideas and follow up the progress of the project.

The following coursework is compulsory and must be approved before the student can take the exam:

A data science-based innovation project where a problem is defined within social innovation, and data chosen to solve/answer the problem. The students will formulate a hypothesis, collect data, analyze it by using appropriate data science techniques and reach conclusions about the data. The outcome from this project should be presented during the oral examination and should be documented in a written report.

The project is to be completed individually or in groups of up to five students.

Examination system:

1. 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 science 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 oral presentation counts for 50% of the final grade.
2. Written project report. A 3,000 to 6,000-word project report written individually, which counts for 50% of the grade.

The oral presentation cannot be appealed.

Each exam must be assessed to E or better for the course as a whole to be given a final grade.

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.

Two examiners will be used, one of which can be external. External examiner is used regularly.

The course has 5 ECTS of overlapping content towards Data Science for Social Innovation I.