1.5: Key Terms
- Page ID
- 58279
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Key Terms and Definitions
- Data Analytics — The process of examining datasets to draw useful conclusions using techniques and tools.
- Descriptive Analytics — Analytics that summarizes historical data to answer "What happened?"
- Diagnostic Analytics — Analytics that examines data to answer "Why did it happen?"
- Predictive Analytics — Analytics that uses statistical/ML models to forecast "What is likely to happen?"
- Prescriptive Analytics — Analytics that recommends optimal actions: "What should we do?"
- Data-to-Insight Lifecycle — The end-to-end stages data undergoes from collection to yielding meaningful conclusions.
- Big Data — Extremely large, complex datasets that require specialized tools for storage and analysis.
- Business Intelligence — Technologies and practices that transform raw data into meaningful information for business decisions.
- Data Analyst — A professional who analyzes existing datasets, creates reports and visualizations, and answers specific business questions.
- Data Scientist — A professional who uses advanced statistics, ML, and programming to extract insights from complex data and build predictive models.
- Data Engineer — A professional who designs, builds, and maintains the data infrastructure and pipelines.
- ETL — Extract, Transform, Load — a process for moving data from sources into a centralized repository.
- Exploratory Data Analysis — An approach to analyzing data to discover patterns and anomalies before formal modeling.
- Actionable Insight — A data-derived finding that is specific enough to inform a decision or action.