As a data practitioner – whether it says so in your title or not – you and your team have likely already done a lot of work in figuring out how to manage an ever-increasing data set and leverage it for insights into how to do things even better.
Data Management, including:
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- Data Ingestion of structured and unstructured data from sources such as enterprise applications, databases, connected devices, social feeds, email repositories, log files, and JSON objects
- Data Curation to pre-process, transform, and cleanse data sets
- Data Sampling including data mining and understanding patterns
- Data Cataloging to build data repositories that help data scientists explore data-sets
- Extraction and Presentation to selectively use parts of the data set for other workflows and processes
Data Visualization & Modeling
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o Discovery & Exploration, including
- Algorithms
- Operational Research
- Data Models
- Understanding & Visualizing Data Relationships
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Decision Sciences
- Structured Problem Solving and Guided Analytics Workbench
- Decision Support Applications
- Analytics Operationalization Systems
- Intelligent Systems
- Econometrics
