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
- 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.
- 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.
- Data Visualization: This is the process of creating graphical or visual representations of data and often a curical piece of effectively communicating insights.
- 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..
- 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
- 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
