1. Starting Points
1.1. Course Objectives
1.2. Access this Mindmap
1.2.1. Copy this Mindmap
1.3. Download KNIME
2. Key Perspectives
2.1. Uber. Michelangelo Case Study
2.1.1. Uber. Michelangelo Part 2
2.2. McKinsey. Analytics Translator
2.3. McKinsey. Red Flags
2.4. Deloitte. Analytics Maturity
2.5. Use Case Catalogue
2.5.1. Oracle
2.5.2. Informatica
2.5.3. Accenture
2.5.4. Tableau
2.5.5. AWS
2.5.6. Keenfolks
2.5.7. Gartner
2.5.8. Public Sector Network
2.5.9. BCG
2.5.10. DataPine
2.5.11. DataPine
3. Design for Big Data
3.1. Design Primer
3.1.1. Further Reading
3.1.1.1. HBR. Strategic Choices
3.1.1.2. Strategyzer. Canvas
3.1.1.3. Accenture. How to use AI
3.1.1.4. Deloitte. Building the IDO
3.1.1.5. Prolego. AI Canvas
3.2. Strategic Design
3.2.1. Further Reading
3.2.1.1. Doblin. Ten Types of Innovation
3.2.1.2. Deloitte. Strategy Equals Choice
3.2.1.3. McKinsey. Business Impact with Data
3.2.1.4. OKR Examples
3.2.1.5. IFTTT
3.2.1.6. Dataiku. Measuring ROI for AI Efforts
3.2.1.7. McKinsey. Ten Red Flags
3.2.1.8. Deloitte. Cognitive Technologies
3.2.1.9. NNGroup. Customer Journey Maps
3.2.2. Task
3.2.2.1. Jam #1
3.2.2.2. Jam #2
3.2.2.3. Jam #3
3.3. Organisational Design
3.3.1. Further Reading
3.3.1.1. DevTo. Data Science Tech Stack 2020
3.3.1.2. McKinsey. Analytics Translator
3.3.1.3. McKinsey. An Analytics Organization
3.3.1.4. Keyrus. Analytics Center of Excellence
3.3.1.5. McKinsey. The Age of Analytics
3.3.1.6. Uber. Scaling Machine Learning
3.3.2. Task
3.3.2.1. Jam #1
3.3.2.2. Jam #2
3.3.2.3. Jam #3
3.4. Process Design
3.4.1. Further Reading
3.4.1.1. Energy Central. Analytics Value Curve
3.4.1.2. Uber. Scaling Machine Learning
3.4.1.3. Tibco. What is Data Science
3.4.1.4. TDS. ML Workflow
3.4.1.5. Scikit Learn. Scikitlearn Cheat Sheet
3.4.1.6. Medium. Clustering Analysis
3.4.1.7. QuantDate. DL and ML
3.4.1.8. New World AI. What Is Deep Learning?
3.4.1.9. RocketSource. 4 ML Models
3.4.2. Demo
3.4.2.1. Further Reading
3.4.2.1.1. KNIME. Interface
3.4.2.1.2. KNIME. Example Workflows
3.4.2.1.3. KNIME. Random Forest
3.5. Technology Design
3.5.1. Further Reading
3.5.1.1. Forbes. Automated Machine Learning
3.5.1.2. Microsoft. Big Data Architectures
3.5.1.3. Fowler. Data Mesh
3.5.1.4. Talend. Data Fabric
3.5.1.5. Trifacta. Wrangling
3.5.1.6. Fowler. Data Lakes
3.5.1.7. Turck. Big Data Technology
3.5.1.8. AWS. Data Warehouse
3.5.1.9. Microsoft. Analytics Architecture
3.5.1.10. AWS. Big Data Patterns
3.5.1.11. Azure. Big Data Patterns
3.5.1.12. Apache Pipeline
3.5.1.13. Apache. Big Data Projects
3.5.2. Task #1
3.5.2.1. CloudCraft
3.5.2.2. Further Reading
3.5.2.2.1. Simple Microservices Architecture on AWS
3.5.3. Task #2
3.5.3.1. Jam #1
3.5.3.2. Jam #2
3.5.3.3. Jam #3
3.5.3.4. Further Reading
3.5.3.4.1. SG PDPC
3.5.3.4.2. SG PDPA
3.5.4. Reflection
3.6. Product and Service Design
3.6.1. Task #1
3.6.1.1. Jam #1
3.6.1.2. Jam #2
3.6.1.3. Jam #3
3.6.1.4. Further Reading
3.6.1.4.1. NNGroup. Usability 101
3.6.1.4.2. NNGroup. Usability Metrics
3.6.2. Task #2
3.6.2.1. Jam #1
3.6.2.2. Jam #2
3.6.2.3. Jam #3
3.6.2.4. Further Reading
3.6.2.4.1. MAS. FEAT
3.6.2.4.2. IBM. AI Explainability 360
3.6.2.4.3. IBM. AI Fairness 360
3.6.3. Further Reading
3.6.3.1. DLabs. Design Thinking
3.6.3.2. Pichler. Product Management Framework
3.7. Design Review
3.7.1. Task
3.7.1.1. Jam #1
3.7.1.2. Jam #2
3.7.1.3. Jam #3
3.7.1.4. Further Reading
3.7.1.4.1. Oracle. Big Data Use Cases
3.7.1.4.2. Prolego. Use Case Canvas
4. What Does Big Data Do?
4.1. Big Data Propositions
4.1.1. Further Reading
4.1.1.1. Contagious. German Rail and Facebook
4.1.1.2. Hackernoon. Big Challenge in Deep Learning
4.1.1.3. YouTube. AI Playing Super Mario World
4.1.1.4. Trapica. 8 Examples of Deep Learning
4.1.1.5. Deloitte. RPA in Internal Audit
4.1.1.6. TookiTaki. Our Technology
4.2. Task
4.2.1. Jam #1
4.2.2. Jam #2
4.2.3. Jam #3
4.2.4. Further Reading
4.2.4.1. Oracle. Big Data Use Cases
5. What is Big Data?
5.1. Distinctions of Big Data
5.1.1. Further Reading
5.1.1.1. IBM. What is Big Data?
5.1.1.2. Turck. Big Data Landscape
5.1.1.3. AWS Snowmobile