STKD6500 Data Science for Social Innovations I Course description

Data analytics provides a set of both qualitative and quantitative techniques to analyze data in order to convert information into useful knowledge.

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 empirical (data analytics) approaches to problem solving based on big-data. The range of real-world problems that can be solved is wide:

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. Examples and practical exercises are geared towards demonstrating real-world use cases and make students proficient in using these tools as well as understanding the theory behind them.

It is recommended to have completed one full year of university studies (60 ECTS) before the program starts. It is also recommended that students have basic knowledge of statistics and Programming.

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
• core principles of data analytics (machine learning, statistics)
• main components of data analytics from infrastructure/technical perspectives
• basic visualization techniques to understand and/or communicate data and findings

Skills

On successful completion of this course the student has:

• ability to apply data analytics techniques to formulate a hypothesis, collect data, analyze it and reach conclusions about the data
• ability to critically draw conclusions from different sources of data
• ability to plan and design a real-world data analytics application

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, home work, and a project. The lectures will introduce the topic, the workshops will provide some hands-on experience on the topic, and the home works are aimed at deepening the knowledge and consolidating the skills needed to complete the project. The project will be focused on solving a real-world problem. The solution will be presented at the end of the course in the form of a sales pitch for a startup, as to convince potential investors.

None.

Oral examination of a group project

Each group may consist of 2-5 candidates.

Oral presentations cannot be appealed.

No support material is permitted in the exams.

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 internal examiners will be used. External examiner is used regularly.

The course has 5 ECTS of overlapping content towards STKD6510 Data Analytics: Tools and Techniques for Acquiring Insights from Big Data II.