Final Exam: Data Primer

WHAT YOU WILL LEARN

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  Distinguish between raw data, information, applicable knowledge, and general wisdom
  

  identify examples of semi-structured data
  

  define concepts essential to data science like dataset, database, data analytics, data aggregation, time series
  

  name most common data backup strategies and tools and describe disaster recovery plans
  

  define the functionality behind common data processing languages such as sql and name main commands used in these languages
  

  name and describe common database types used in the industry
  

  describe situations when normalization or denormalization is needed and name key steps of each process
  

  describe the concept of data mart and how it can be used for business decision making through data mining
  

  compare vertical and horizontal scaling of databases and their limitations
  

  describe common use cases and basic principles of data warehousing
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  describe how data warehouse is different from a database and how data warehouses are used for business intelligence
  

  name and define three main tiers of a data warehouse
  

  compare various data warehousing schemas such as star, snowflake etc.
  

  specify how to organize and design your extraction, transformation, and loading (etl) capabilities to keep your data warehouse up to date
  

  name and describe most commonly used etl tools and software
  

  compare key differences in etl (extract transform load) and elt (extract load transform) systems and describe how etl is used with traditional and elt with modern data architectures
  

  list the most commonly used data sources and formats
  

  describe the system and principles of work for a multi-model database
  

  specify why real-time processing is advantageous when dealing with a large amount of data
  

  describe the changing perspectives with respect to analytics and the evolution of data analytics and briefly explain descriptive, diagnostic, predictive and prescriptive analytics