BUSINESS-13 · Advanced · ENGLISH · PLANNED
Study an original business case, compare options, make a decision and defend implementation.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Business Administration · BBA/MBA | Information Systems, Digital Business and AI Management
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Sign in to save this learning pathPractical outcome: Design an information-flow map for a fictional growing company.
Explain business processes using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Explain enterprise resource planning using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Explain customer relationship systems using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Explain data warehouses using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Use the first concepts from this section. Design an information-flow map for a fictional growing company. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain business intelligence using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Explain system integration using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Explain data governance using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Explain technology sourcing using a visual and one worked real-world example. Context: Design an information-flow map for a fictional growing company. End with a prediction or check-for-understanding.
Design an information-flow map for a fictional growing company. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Business processes, Enterprise resource planning, Customer relationship systems and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Prepare a transformation roadmap with measurable user benefits.
Explain digital business models using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Explain platform strategies using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Explain process automation using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Explain cybersecurity governance using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Use the first concepts from this section. Prepare a transformation roadmap with measurable user benefits. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain cloud economics using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Explain adoption barriers using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Explain change readiness using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Explain transformation metrics using a visual and one worked real-world example. Context: Prepare a transformation roadmap with measurable user benefits. End with a prediction or check-for-understanding.
Prepare a transformation roadmap with measurable user benefits. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Digital business models, Platform strategies, Process automation and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Evaluate a fictional AI proposal with human review and a bounded experiment.
Explain ai use-case selection using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Explain data readiness using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Explain build-versus-buy decisions using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Explain evaluation metrics using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Use the first concepts from this section. Evaluate a fictional AI proposal with human review and a bounded experiment. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain model limitations using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Explain bias and fairness using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Explain human oversight using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Explain ai operating costs using a visual and one worked real-world example. Context: Evaluate a fictional AI proposal with human review and a bounded experiment. End with a prediction or check-for-understanding.
Evaluate a fictional AI proposal with human review and a bounded experiment. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering AI use-case selection, Data readiness, Build-versus-buy decisions and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Evaluate a fictional AI proposal with human review and a bounded experiment. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Information Systems, Digital Business and AI Management, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Case discussions, business plans, management simulations and original consulting projects. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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