Course Content
At around 10 hours each of studying, these topics will introduce you to the main ideas of each subject. With support from your mentor, you will be encouraged to apply this knowledge in your internship, giving you the opportunity to directly implement new skills and understanding.
If your academic background is in any of these topics, you will be able to personalise your study path, with help from your mentor.
- Learning Outcomes
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Define digital transformation and explain its importance in today's world
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Reflect on the potential impact of digital transformation on the workforce, including changes in job roles and required skill sets
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Identify the main aspects of key technologies driving digital transformation: big data, cloud computing, IoT, cybersecurity, and blockchain.
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Reflect on the potential benefits and risks associated with digital transformation, including increased efficiency, improved customer experience, and data privacy concerns.
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Explore personal and professional involvement in digital transformation
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- Learning Outcomes
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Define the concept of a project and simple project management
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Compare traditional and agile project management methodologies
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Explore the relevance of Agile in the modern environment
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Identify the main components of Kanban methodology
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Create a basic Kanban board in digital project management tool: Trello
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Implement Kanban methodology in a personal project
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- Learning Outcomes
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- Define digital marketing
- Identify the different channels and basic principles of digital marketing, including SEO, social media, content, affiliate and mobile marketing
- Present the customer journey or business models in digital marketing
- Reflect on why digital marketing is an important transversal skill
- Learning Outcomes
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Beginners:
- General AI overview
- Risks and myths
- Human-computer interaction
- Ethical implications
Managers:
- General overview
- Innovation in industry, AI technology integration
- AI fields
- Regulatory aspects
Specialists:
- General overview
- Classification and regression different application
- Deep learning
- Computer vision
- Natural language processing
- Multiagent systems
- Statistical learning