Data Science Hierarchy of Needs
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The document outlines the data science hierarchy of needs, starting from real-world data collection and raw data storage, advancing through data cleaning and structured storage. It emphasizes various analytics stages—descriptive, diagnostic, predictive, and prescriptive—highlighting their respective functions such as reporting and modeling. The hierarchy culminates in advanced methodologies like machine learning and automated systems for optimized decision-making.
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Data Science Hierarchy of Needs
- 1. The Data Science Hierarchy of Needs Real World Data Collection + Raw Data Storage Data Cleaning + Structured Data Storage Descriptive Analytics Diagnostic Analytics Predictive Modeling Prescriptive Optimization What happened? standard reports, KPIs, score cards, ad-hoc reports Why did it happen? self-service analytics, segments, aggregates, strategy, A/B Tests What will happen? modeling, experimentation, predictions What is the best thing to happen? machine learning, deep learning, automated systems