JB3330 Digital investigative journalism Emneplan

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge The student is able to:

• Account for various machine systems that convert energy.
• Account for common components included in such systems.

Skills

The student is capable of:

• Drawing hydraulic wiring diagrams and performing simple system analyzes.
• Designing and dimensioning certain hydraulic systems.
• Calculating working pressure, volume flow and flow velocities in pipelines using Bernoulli's equation and continuity equation.
• Calculating pressure loss and power loss across different types of components in pipelines and finding the operating point of pumps operating in a composite system.
• Calculating power, torque, and different types of efficiencies for systems based on thermal and electrical energy machines.

General competence

The students will gain:

• The ability to produce adequate documentation of their own work.

Lectures, laboratory assignments and supervised exercises.

The estimated workload is four hours of lectures, four hours of supervised exercises and five hours of self-study per week.

Knowledge

• Acquire an advanced understanding of digital research methods and data analytics.
• Approach the use of AI/ML tools for complex investigative scenarios (computer vision, Natural Language Processing, regression, etc).
• Understand the evolving ethical concerns and legal frameworks in digital investigative journalism (privacy concerns, bias in the models, etc).

Skills

• Conduct comprehensive investigations using advanced AI models and data analytics software.
• Use specialized programming languages, such as Python or SQL, to handle complex datasets.
• Produce investigative reports on complex datasets.

General competence

• Present complex investigative findings in an accessible format for the general public.
• Have an understanding of AI models’ limitations, constrains and data requirements.

• Lectures
• Workshops
• Group work

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.

Grade scale A-F.

One internal examiner. External examiners are used regularly.

Fikriye Idil Kaya

The course is based on Physics MEK1400 and Statics and Introduction to Solid Mechanics MAPE1300.