3 Week Unit

Overview

Week 1: Fundamentals

  • Introduction to Data Visualization and the Ubiquitous Nature of Data
  • You've got data now what?
    1. Understanding the Data Visualization Process: What you should know, and What you should know how to do
  • Introduction to Tableau (Self-study, videos on demand)

Week 2: Hands-on-Exercises working with data

  • Identify/Acquire Training Data
  • Apply the data visualization process to the training data of choice, using Data Visualization Activity Worksheets
  • Data visualization best practices

Week 3: Pulling It All Together, Demonstration of Skills, Taking It to the Next Level

  • Diversity, Equity, and Inclusion in Data Visualization: General Recommendations
  • Storytelling
  • Demonstration of skills
  • Introduction to Geospatial Data

Learning Objectives

  • LO1: Students will explain, in their own words, what data visualization is and why it is important.

  • LO2: Students will demonstrate what happens in each stage of the data visualization process.

  • LO3: Students will identify appropriate chart types for different types of data.

  • LO4: Student will generate/produce data visualizations that provide insight.

Week 1: Fundamentals

  1. Ubiquitous Nature of Data

    Data Literacy is a critical skill in today's world where data is being generated at an unprecedented rate. It involves the ability to read, understand, create, and communicate data as information.

    This competency is similar to literacy in a broader sense, but it focuses specifically on working with data. The concept of data literacy was introduced by Svetla Baykoucheva in her book, "Managing Scientific Information and Research Data". According to her, data literacy is essential for researchers and professionals to effectively analyze and interpret data.

    Baykoucheva, Svetla (2015). Managing Scientific Information and Research Data. Waltham, MA: Chandos Publishing. p. 80. ISBN 9780081001950.


    Data Literacy I

    One aspect of data literacy is visual literacy, which refers to the ability to read, write, and create visual images.

    Visual literacy is an important aspect of data literacy as data is often presented through visual representations such as graphs, charts, and diagrams. To be visually literate, individuals need to be able to understand and interpret visual information accurately. There are several strategies and resources available to help individuals develop their visual literacy skills, such as those provided in the examples linked in this article.

    Overall, developing data literacy skills, including visual literacy, is essential for effectively working with data in today's data-driven world.

    Visual Literacy I     Visual Literacy II

  2. You've got data now what?

    What you should know:
    Data visualization is a crucial process for transforming complex data into valuable insights.
    This multi-stage, non-linear, and iterative process involves a series of steps that aim to extract meaning from raw data. The purpose of data visualization is to facilitate the identification of relationships and patterns in the data that would otherwise be difficult to discern. This, in turn, allows for the synthesis of new knowledge and the ability to make informed decisions.

         1. Data becomes information when it has meaning and we understand context and relationships that exist in data.

         2. Visualization allows for the identification of subtle but informative patterns in the data that provide meaning and purpose.

         3. Data visualization facilitates the examination of relationships that exist in data. Identified relationships in data help to uncover patterns and causality to enable synthesis of new knowledge from what is already known. New knowledge facilitates the ability to make predictions and inform decision making.

    80-90% of the data visualization process is getting the data into a useable format for processing and visualization. The type of data dictates the tool to be used and the visual layout.


    What you should be able to do:

    Stage You should be able to do
    Acquire Identify and obtain data from reputable data sources
    Parse Examine and break data down to its most basic parts (understand data types)
    Mine Identify patterns, prepare the data for further processing
    Filter Extract from data those data that are relevant to the task
    Represent Choose the most appropriate chart type and layout
    Refine Improve the original visualization to provide more insight
    Interact Enable the viewer to engage with the data (Interactive Visualization)

  3. Introduction to Tableau


         1. Getting Started (3 videos; 34 min)
         2. Tableau Prep (12 videos; 67 min)
         3. Connecting to Data (11 videos; 72 min)
         4. Visual Analytics (26 videos; 143 min)
         5. Dashboards and Stories (8 videos; 40 min)
         6. Mapping (12 videos; 47 min)
         7. Calculations (16 videos; 65 min)
         8. Why is Tableau Doing That? (4 videos; 22 min)
         9. How To (10 videos; 39 min)
         10. Publishing to Tableau Online (3 videos; 22 mins)

    Free Training Videos

Week 2: Hands-on-Exercises working with data

  • Identify/Acquire Training Data
        Free Public Data Sets
  • Apply the data visualization process to the training data of choice, using Data Visualization Activity Worksheets
         o Acquire, parse, mine, filter, represent, critique, refine, critique, interact (Process Tab)
  • Data visualization best practices, color and perception, layouts
  • Critique stage: share initial visualization and receive feedback
  • Refine visualization based on feedback received as part of the Critique stage

Week 3: Bringing it all together, Demonstration of Skills, Taking it to the Next Level

  • Diversity, Equity, and Inclusion in Data Visualization:
        General Recommendations
  • Storytelling: Who is your audience, what story are you telling?
  • Taking your data visualization skills to the next level: Introduction to Geospatial Data