STKD6510 Data Science for Social Innovations II 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-word 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
• 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:

• 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
• the ability to clearly identify and define a problem and craft a solution using data analytics
• the ability to construct a data analytics solution from a set of general requirements such as organizational goals or user scenarios
• the ability to synthesize primary and secondary data sources the ability to accurately pinpoint trends, correlations and patterns in complex data sets
• able to identify new opportunities for organizational change including process improvemnents, 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 to a real-world problem
• data analytics visualizations in presentations

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 content of the course, the details and depth of the topics will be adjustable according to the level of the students. The same goes for the project, that will give the opportunity to advanced students to choose the right level of challenge for them. 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.

The course uses blended teaching: Four weeks in class and eight weeks online.

None.

1. Oral examination of a group project, which counts for 60% of the grade.
2. A 4,000 to 6,000-word group project report, which counts for 40% of the grade

Each group may consist of 2-5 candidates.

Oral presentations cannot be appealed.

Both exams must be passed in order to pass the course.

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 STKD6500 Data Analytics: Tools and Techniques for Acquiring Insights from Big Data I.