Final Exam: Data Primer
Data | Beginner
- 1 video | 32s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
Distinguish between raw data, information, applicable knowledge, and general wisdomidentify examples of semi-structured datadefine concepts essential to data science like dataset, database, data analytics, data aggregation, time seriesname most common data backup strategies and tools and describe disaster recovery plansdefine the functionality behind common data processing languages such as sql and name main commands used in these languagesname and describe common database types used in the industrydescribe situations when normalization or denormalization is needed and name key steps of each processdescribe the concept of data mart and how it can be used for business decision making through data miningcompare vertical and horizontal scaling of databases and their limitationsdescribe 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 intelligencename and define three main tiers of a data warehousecompare 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 datename and describe most commonly used etl tools and softwarecompare 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 architectureslist the most commonly used data sources and formatsdescribe the system and principles of work for a multi-model databasespecify why real-time processing is advantageous when dealing with a large amount of datadescribe 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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