Data analysis/History

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Introduction[edit | edit source]

This page can be displayed as Wiki2Reveal slides. Single sections are regarded as slides and modifications on the slides will immediately affect the content of the slides.

Objective[edit | edit source]

This learning resource in Wikiversity has the objective to introduce to data analyis starting from

  • the basic principles, so that learners understand the work flow from raw data to information and decision support,
  • understand the requirements and constraints of different methodologies in past and their limitations according to quantities of data the must be processed,
  • understand the applications of methodologies in the past to derive and rethink the methodologies e.g. in the context of big data,

Target Group[edit | edit source]

The target group of the learning resource are students according to the activities and teachers and lecturers that want to use that learning materials (e.g. Wiki2Reveal slides) for introducing the students into the subject of data analysis.

Learning Tasks / Activities[edit | edit source]

What are the earliest evidence in mankind, where humans collected data and derived decisions from the analysis of data? (keep in mind, that data might not be collected processed on computers or IT devices). Justify your examples why humans performed an analysis of the data. Who found the earliest evidence for data analysis in your course?

Activities 1 - IT and Computer Science[edit | edit source]

Explain the role of Computer Science for data analysis.

  • From Data to Graphs: Look at Open Source software like R/R-Studio, libraries like morris.js[1] and explain the task, that computer science has, to transform data in comprehensive visualizations.
  • Data can be connected into semantic networks. Explain what a semantic is and identify the challenges to create semantic networks from given data and keep the semantic network up-to-date.

Activities 2 - Mathematics[edit | edit source]

Explain the role of Mathematics for data analysis,

  • (Statistics) find and explain some examples for statistical approaches in the field of data analysis,
  • (Numeric Analysis) find and explain some examples for numeric approaches in the field of data analysis,
  • (Geometrical Analysis of Data) Assume humans collect data about the length of shadows during different times of the year and provide a geometrical representation of the data for an astronomical observatory. Does that match with the diagram below about data analysis and identify missing items for the workflow.

Activities 3 - Mathematics[edit | edit source]

What are the methodologies that you already know to process and analyze data?

  • What are the requirements to handle spatial data and analyze the data spatially? Identify a use-case in your course where you would collect data that has a geolocation together with the collected data.
  • Explore the concept of Collaborative Mapping. Look for mathematical methodologies that allow the spatial data analysis.

Diagram - Workflow[edit | edit source]

Data visualization process v1.png

References[edit | edit source]

  1. MorrisJS Javascript Library (2016) GitHub Repository URL: - Demo URL: (acessed 2021/12/20)

See also[edit | edit source]

Page Information[edit | edit source]

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Wiki2Reveal[edit | edit source]

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