Overview
Master Data-Driven Decisions: Experimentation, Prototyping, MVPs & Performance Analysis
Syllabus
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- Introduction to Data-Driven Decision Making
-- Understanding the Importance of Data in Modern Solutions
-- Basics of Data Literacy
- Data Collection Methods
-- Survey Design and Data Gathering Techniques
-- Observational and Experimental Data Collection
- Data Analysis Fundamentals
-- Statistical Analysis Basics
-- Tools for Data Analysis (Excel, R, Python)
- Experimentation Techniques
-- Principles of Experiment Design
-- A/B Testing and Its Applications
- Developing an Experimenter's Mindset
-- Cultivating Curiosity and Hypothesis-Driven Thinking
-- Embracing Failure as a Learning Tool
- Prototyping Principles
-- Rapid Prototyping Techniques
-- Using Prototypes to Test Hypotheses
- Data-Driven Decision-Making Frameworks
-- Introduction to Decision Trees and Predictive Models
-- Applying Machine Learning in Prototyping Solutions
- Case Studies and Applications
-- Review of Successful Data-Driven Projects
-- Lessons Learned from Failed Data Prototypes
- Tools for Prototyping and Experimentation
-- Overview of Prototyping Software (Figma, Sketch)
-- Data Visualization Tools (Tableau, Power BI)
- Ethics and Responsibility in Data Usage
-- Understanding Privacy Concerns
-- Ethical Decision Making in AI and Data
- Project: Designing a Data-Driven Prototype
-- Project Proposal and Planning
-- Execution and Iteration
-- Final Presentation and Peer Review
- Conclusion and Future Trends
-- The Future Landscape of Data-Driven Solutions
-- Continuous Learning and Adaptation Strategies
Taught by
Robert Barcik, Jana Gecelovska and Patrik Zatko
Tags
On-Demand
