Final Exam: Data Primer

Data    |    Beginner
  • 1 video | 32s
  • Includes Assessment
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Final Exam: Data Primer will test your knowledge and application of the topics presented throughout the Data Primer track of the Skillsoft Aspire Data for Leaders and Decision-makers Journey.


  • 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
  • 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


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