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Data Analytics Literacy

Janani Ravi and Axel Sirota

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
    • Read more

    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.
    • Enroll now

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    What's inside

    Six courses

    Representing, Processing, and Preparing Data

    (2 hours)
    This course covers data processing tools, including spreadsheets, Python, and relational databases, and deals with data quality issues and visualizing data for insight generation.
    View more details for this course

    Combining and Shaping Data

    (3 hours)
    This course covers both conceptual and practical aspects of pulling together data from different sources, with different schemas and orientations, into a cohesive whole using Excel, Python, and various tools available on the Azure cloud platform.
    View more details for this course

    Summarizing Data and Deducing Probabilities

    (2 hours)
    This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python.
    View more details for this course

    Experimental Design for Data Analysis

    (2 hours)
    This course covers building and evaluating machine learning models, using data judiciously and accounting for biases.
    View more details for this course

    Interpreting Data with Statistical Models

    (2 hours)
    Data is everywhere, and we often hear about statistics. Over this course, we will shape up our statistical knowledge, going from zero to hero analyzing complex patterns of everyday real-world problems.
    View more details for this course

    Communicating Data Insights

    (2 hours)
    This course covers the key statistical and technical tools needed to convey clear, actionable insights from data to senior executives, including the use of powerful visualizations such as Sankey diagrams, funnel plots, and candlestick plots.
    View more details for this course

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    Time
    17 hours
    Via
    Pluralsight
    Institution
    Instructors
    Janani Ravi
    Axel Sirota
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