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., 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.

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 knowledge in algebra, statistics and python programming. 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
• modern analytics (methods and tools) for building real-world data analytics 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 draw conclusions from different sources of data
• plan and design a real-world data analytics application
• analyze real-world data by building an app with a real-world application
• identify and define a problem and craft a solution using data analytics
• construct a data analytics solution from a set of general requirements such as organizational goals or user scenarios.
• synthesize primary and secondary data sources the ability to accurately pinpoint trends, correlations and patterns in complex data sets
• identify new opportunities for organizational change including process improvements, cost reduction or efficiency improvements.

General Competence

On successful completion of this course the student can apply:

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

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 course uses blended teaching: Four weeks of attendance-based teaching class and eight weeks online.

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:

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 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 50% of the final grade.
2. Written project report. A 4,000 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 "Big Data Analytics: Tools and Techniques for Acquiring Insights from Data I"