For students who have already completed the overview version of the course and want to deepen their knowledge in a specific direction. The course may be re-enrolled with a focus on a chosen specialization (e.g., AI, cybersecurity, data engineering, cloud, ERP) in place of the standard content. Instruction takes the form of self-study of recognized online courses and certifications, more demanding than the overview version, supplemented by the student's own practical output and knowledge-sharing with other students.

Credit is awarded upon completion of the required online courses/certificates for the chosen specialization, and upon submission of one practical output (application, model, analysis, etc., depending on specialization) and a learning activity for other students. Specific requirements for the given specialization are agreed with the instructor before enrollment. No written or oral exam.

Obligatory:

• Microsoft Learn training catalog [online]. Dostupné z: https://learn.microsoft.com/en-us/training/
• Microsoft Learn, Power Platform [online]. Dostupné z: https://learn.microsoft.com/en-us/training/powerplatform/
• Atlassian University [online]. Dostupné z: https://university.atlassian.com/
• IBM SkillsBuild [online]. Dostupné z: https://skillsbuild.org/
• HubSpot Academy [online]. Dostupné z: https://academy.hubspot.com/
• DataCamp career tracks [online]. Dostupné z: https://www.datacamp.com/tracks/career

The course uses a student-centred, practice-oriented approach combining guided self-study, individual specialization, project-based learning, and peer learning. Students are expected to take responsibility for planning and managing their learning pathway in consultation with the instructor.

Introduction

Choice of specialization and consultation with the instructor

Completion of specialized courses/certifications per the chosen specialization

Creation of a practical output within the specialization

Learning activity for other students

Learning activity from other students

Showcase of created outputs to the class

Learning objectives

• Justify the choice of a specialization track based on career goals and prior competence.

• Apply advanced concepts and techniques from the chosen specialization (e.g., agentic AI, cybersecurity, data engineering, cheminformatics) to real or realistic problems.

• Construct a practical artifact (application, model, analysis, or automation) that demonstrates specialized competence.

• Evaluate the artifact's quality, limitations, and fitness for purpose against professional standards in the chosen field.

• Communicate specialized technical knowledge to a non-specialist peer audience through a self-organized learning session.

• Integrate knowledge gained from peers' sessions across different specializations into their own professional perspective.