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Data Analytics Literacy

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What You'll Learn

  • Describe the general analytics workflow
  • Differentiate data types and identify analyses suitable for specific types of data
  • Determine which analysis is appropriate for a specific business problem
  • Apply hypothesis testing to a new business problem
  • Describe the key components of an RDBMS (Relational Database Management System) architecturequery and process data using OLTP (Online Transactional Processing) systemswrite portable SQL queries against datadefine schemasdescribe common database programming constructs (stored procedures, triggers, views, etc)
  • Describe the components of an OLAP (Online Analytical Processing) systemdifferentiate tabular vs cube data modelswriting analytical queriesworking with nested/repeated datadealing with streaming data in an OLAP context
  • Describe the components of a NoSQL (Not Only SQL) database
  • differentiate columnar/wide-column databases vs document databases
  • Identify when each is appropriate
  • Describe common methods for getting data in and out of systems - scripting (including specialty languages such as Pig), bulk loading, streaming inserts
  • Compare and contrast the ETL (extract, transform, and load) workflow with the LET workflow (load, extract, and transform)
  • Describe the “four v’s” of Big Data and how they are used to differentiate Big Data problems from “small data”
  • Describe the pros and cons of using cloud vs on-premise solutions for data management
  • Describe the pros and cons of using “handrolled” Hadoop/Hive/Spark vs proprietary systems like Teradata/Oracle
  • Identify key decision factors between services on AWS, Azure, GCP etc
  • Describe the general analytics workflow
  • Differentiate data types and identify analyses suitable for specific types of data
  • Determine which analysis is appropriate for a specific business problem
  • Apply hypothesis testing to a new business problem
  • Describe the key components of an RDBMS (Relational Database Management System) architecturequery and process data using OLTP (Online Transactional Processing) systems
  • Write portable SQL queries against data
  • Define schemas
  • Describe common database programming constructs (stored procedures, triggers, views, etc)
  • Describe the components of an OLAP (Online Analytical Processing) system
  • Differentiate tabular vs cube data models
  • Writing analytical queries
  • Working with nested/repeated data
  • Dealing with streaming data in an OLAP context
  • Describe the components of a NoSQL (Not Only SQL) database
  • Differentiate columnar/wide-column databases vs document databases
  • Identify when each is appropriate
  • Describe common methods for getting data in and out of systems - scripting (including specialty languages such as Pig), bulk loading, streaming inserts
  • Compare and contrast the ETL (extract, transform, and load) workflow with the LET workflow (load, extract, and transform)
  • Describe the “four v’s” of Big Data and how they are used to differentiate Big Data problems from “small data”
  • Describe the pros and cons of using cloud vs on-premise solutions for data management
  • Describe the pros and cons of using “handrolled” Hadoop/Hive/Spark vs proprietary systems like Teradata/Oracle
  • Identify key decision factors between services on AWS, Azure, GCP, etc.

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    From Pluralsight
    Hours 18
    Instructors Janani Ravi, Axel Sirota
    Language English
    Subjects IT & Networking

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    From Pluralsight
    Hours 18
    Instructors Janani Ravi, Axel Sirota
    Language English
    Subjects IT & Networking

    Careers

    An overview of related careers and their average salaries in the US. Bars indicate income percentile (33rd - 99th).

    Volunteer Big Data Engineer $48k

    Data Scientist - Big Data $68k

    Big Data and AWS Data Lake $73k

    Big Data Developer (Streaming Data) $77k

    Big data developer with AWS $78k

    Research Scientist Big Data $94k

    Big Data Developer Consultant $98k

    Big Data Engineer 6 $107k

    Big data and ETL specialist $121k

    Big Data Specialist $149k

    Principal Big Data Architect $180k

    Senior Big Data Sales $181k

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