VDJ6100 AI-journalism: Introduction to statistics and data analysis Course description

Because most journalists do not have a solid quantitative background, they face difficulties when analysing data independently. This difficulty represents a major drawback in research. Journalists waste time learning analytical methods by themselves that could be more quickly learned with proper instruction and support. As consequence, the quality of insights and research productivity suffer. This course provides a comprehensive introduction to data science and big data applied to investigative journalism.

The content is designed to train the participants in state-of-the-art techniques in data analysis. This will enable the students to interact independently with the data and draw insights from them. This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real cases; advanced students may choose to use their own data. The students will develop understanding through constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

Language of instruction is English.

None.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student has knowledge of:

• Basic theoretical and practical aspects of data management and data processing for different data types.
• How statistics, dimensionality reduction, and supervised and unsupervised learning may be applied to data analysis in investigations.
• Commonly encountered problems in data science and the most trusted strategies to solve them.
• Basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

Skills

The student can:

• Apply data analysis techniques to collect data, pre-process it, analyse it, and reach conclusions about the data.
• Identify and define a problem and craft a solution using data analytics.
• Critically assess the results as well as justify and explain the methodological choice.

General competence

The student can apply:

• Data analysis principles to data in research.
• Methods and tools for data analysis and visualization.

Teaching and learning are centred around students’ participation in classes and group activities. The course is organized in digital and/or physical gatherings of intensive tuition, with lectures, workshops, group presentations and discussions.

The following coursework requirements must have been approved in order for the student to take the exam:

• Coursework: The students need to complete four online quizzes. One at the beginning of the course, then three quizzes during the course.

The coursework is required to secure the students’ progress through the course.

All required coursework must be completed and approved by the given deadline in order for the student to take the exam. If the coursework requirement has not been approved, the student will be given one opportunity to a new submission by the given deadline.

The exam in the course consists of two parts:

1. An individual term paper and a code repository. The course paper must have a scope of 3.000 to 5.000 words. Font and font size: Arial / Calibri 12 points. Line spacing: 1.5.
2. An individual oral presentation. The presentation must have a scope of approximately 10-15 minutes.

The term paper counts for 60 % of the final mark. The oral presentation counts for 40 % of the final mark. Each part must be passed to be given a final grade. The oral presentation cannot be appealed.

Students awarded a fail grade are given one opportunity to submit an improved version of the assignment for assessment.

Only personal notes written in non-digital media will be allowed during the oral exam. All support materials are allowed in the term paper.

Grade scale A-F

The exam papers are assessed by one internal and one external examiner. At least 25% of the exam papers will be assessed by two examiners. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.

The oral presentation will be assessed by one internal and one external examiner.

The course has 7,5 ECTS of overlapping content towards STKD6810 Neuro-insights: Data Science Approaches in Neuroscience II.