JB3320 Unraveling the Numbers - financial reporting Course description

The ability to effectively interpret and communicate complex economic information is more crucial than ever. This is a specialized course designed for journalism students who aim to master the art and science of financial reporting, with an emphasis on leveraging data science and programming skills to enhance their stories.

Language of instruction is English. 

This course is within the same specialization as the elective course Digital investigative journalism

None

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

Knowledge

The student will:

• Recognize and understand the various sources from which financial data can be gathered, including government databases, company filings, etc.
• Learn essential data science techniques such as data collection, data cleaning, and data visualization.
• Gain a foundational understanding of key financial terms, jargon, and frameworks

Skills

The student will:

• Learn how to use basic programming skills (i.e. Python, R or Excel), to collect and manipulate data for reporting.
• Gain hands-on experience in using data visualization tools to make complex financial data more understandable to the general audience.
• Acquire the skill of narrating complex financial stories in an easy-to-understand manner, including infographics, charts, and other visual aids.

General competence

The student will:

• Understand and apply ethical guidelines in financial reporting
• Develop the ability to critically evaluate financial data, statements, and sources for credibility, bias, and other potential issues.
• Become proficient in communicating complex financial topics in a way that can be easily understood by the general public.

• Lectures
• Workshops
• Group work

The teaching takes place in person, on campus.

Presence will be mandatory (80 percent) given the class's strong practical and collaborative nature. Students who have more than 20 percent absence from class will not be qualified to take the exam.

Assignment 1: Group project. Students are required to analyze a complex dataset that they have gathered. They must develop a hypothesis driven by data, collect and produce an appropriate dataset, and conduct a thorough analysis of it. The project must be carried out in groups of 2-4 students.

In the practical tasks, the scope will vary depending on which types of media or combinations of these the student chooses. The extent will be specified in the assignment texts or individually in collaboration with the subject teacher. More information about the content and deadlines for the assignments can be found in the teaching plan, which is electronically available to the students at the start of the course.

All required coursework must be completed and approved by the given deadline for the student to take the exam. If the coursework requirements have not been approved, the student will be given one opportunity to submit an improved version by a given deadline.

The exam consist of an individual analytical report, 6 - 8 pages where the students engage in a critical discussion and reflection on their findings in the group project. The project must be handed in as an attachment to the exam.

The students must write the exam in English. 

Font type and size: Arial / Calibri / Verdana 12 pt. Line spacing: 1.5  

All aids are permitted, as long as the student complies with the rules for source referencing.

Grade scale A-F

All exam papers are assessed by one internal and one external examiner.