DataScience Classroomnotes 03/Mar/2022

Data Literacy

  • Definition: This is term used to describe an individual’s capability to read, understand and utilize data in different ways.
  • It doesn’t require an individual to be an expert such as data scientist or data analyst but rather to show understanding of basic concepts such as
    • Different types of data
    • Common data sources
    • Type of analysis
    • Visualizations
    • Data Hygiene

Key Data Literacy Skills

  1. Data Analysis: This refers to reading and interpreting data to gain insights from it. While analysis can be conducted using statistical models & complex tools or frameworks. Its also about simply reviewing data and drawing conclusions from it
    • Descriptive Analysis: Explain or describe what has happened
    • Diagnostic Analysis: Explain or diagnose why something has happened (gaining insights)
    • Predictive Analysis: Forecast what might happen.
    • Prescriptive Analysis: Which seeks to prescribe a course of action that will lead to desired outcome.
  2. Data Wrangling: This is act of transforming data from raw state into a form that can be more readily used. This practice is also referred as data munging or data cleaning.
  3. Data Visualization: This is the process of creating graphical or visual representations of data and often a curical piece of effectively communicating insights.
  4. Data Eco system: The concept of data eco system refers to all the components an organization leverages to collect, store and analyze data. This includes physical infrastructure (Server space, cloud storage) and non-physical components such as data sources, programming languages, code packages etc..
  5. Data Governance: This refers to processes and practices and organization uses to formally manage its data assets. This can broken down into following categories
    • Quality
    • Security
    • Privacy
  6. The Data team: Data teams can be structured in several ways depending on organizational structure/size. Most data team’s include
    • Data Scientists
    • Data Engineers
    • Data Analysts


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