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Data Management

The technologies, architectures, and practices needed to manage data as a critical enterprise asset. It is a broad field, within which there are specialized disciplines.

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TOPICS INCLUDE:

  • Data Quality
  • Data Integration
  • Master Data Management
  • Data Modeling
  • Data Preparation
  • Modernizing data warehouse (Hadoop, Stream, NoSQL, In-Memory db, Data Lakes, etc.)
  • Ecosystem Architectures
  • Data Security

Analytics, Data Science & Business Intelligence

The technologies, techniques, and algorithms for analyzing all kinds of data to derive insights and take action for better decision making and enterprise success.

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TOPICS INCLUDE:

  • OLAP
  • Dashboards
  • Visual Analytics/Discovery
  • Geospatial Analytics
  • Click Stream Analytics
  • Data Mining/Predictive/Machine Learning/Neural Networks
  • Text Analytics/Social Analytics
  • Stream Analytics
  • IoT Analytics
  • Other Big Data Approaches
  • Cognitive Computing

Methodologies & Strategies

The processes and approaches to deliver business value through data and analytics. Includes a strong focus on leadership strategies and organizational management to maximize data management and analytics impact.

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TOPICS INCLUDE:

  • Agile Development
  • Performance Management
  • Change Management
  • Leadership Strategies
  • Organizational Models for Analytics Execution
  • Programming Skills: R, Python, Java for Data Science
  • LifeCycle Management
  • Storytelling with Data
  • Governance
  • Ethics

Development, Deployment & Delivery

Languages and software environments for analytics development as well as analytics deployment and delivery models to help organizations achieve their goals.

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TOPICS INCLUDE:

  • On Premises, Public, Private, Hybrid Cloud
  • As a Service, IaaS, PaaS, SaaS
  • Operationalizing Analytics and Decision Science
  • DevOpps