The instructor, himself, in a reflection during grad school.
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Visualize yourself

listContents

Project goals
Resources and links
Deliverables
Exemplar self-visualization project
Phase 1: Planning
Phase 2: Tool building
Phase 3: Beta Data gathering

wb_incandescentProject goals

  1. Engage in a full-lifecycle data project (from planning through analysis and sharing) with highly relevant data: data about yourself!
  2. Create visualizations of data which facilitate discovery of trends that would otherwise be obscured by the quantity or type of data gathered
  3. Reflect on an aspect of your life that you might wish to change or adjust somehow and create a customized tool for exploring such changes

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wb_incandescentResources

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wb_incandescentExemplar self visualization project

Background

This course's instructor met Navya Halaswamy in graduate school for information systems. Navya went on to earn another graduate degree in data analytics during which she undertook a project of visualizing information about her own health which supported her making a range of positive changes in her life patterns.

The following sections include links to her Tableau workbook displaying her results and a recorded screen capture of the Spring 2020 DAT-203 students' synchronous online course session in which Navya shares her discovery of the value of data analysis when applied to meaningful patterns in her own life.

Tableau dashboard

Remote presentation and conversation

Iframe beaming in from YouTube

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github

wb_incandescentUniversal phase deliverables

Phase deliverables

  1. Scrubbed dataset (or sample of data): Removal of personal identifying info or sensitive topics data
  2. Data dictionary which explains each of your fields, their data types, possible values
  3. Project Abstract: 200-600 word precis of your project goals, methodology, data gathering, analysis, and conclusions
  4. Visualization figures with captions: Imagine the caption blending fluidly with the content described, pointing out details that are most relevant for your conclusions
  5. Git repository storage location, with appropriate readme.md files written in markdown.
  6. Optional: Suggestions to future students investigating data patterns related to themselves. What would you have changed about your process? To what degree did this process lend insight into some aspect of your life?

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wb_incandescentPhase 1: Planning

Project setup

  1. Create a dedicated git repository for your data vis course work
  2. Send your instructor an email with the URL to the root of your course's repo. Also include the name of an icon from material.io's selection of icons which will accompany your link
  3. Create sub-directories for each phase, 1-7 called "phaseX_[phasename]" so phase 1's directory would be named "phase1_planning"
  4. Create a readme.md file in the root of your repository, and in each of the phase directories with skeleton content which you can edit later. DOn't forget to reference Daring Fireball's Master Markdown Tutorial as you create your documentation

Planning tasks

Open for editing the file readme.md in your phase 1 directory created in the project setup steps. Include thoughtful responses to the following tasks, including figures, links to source code files, etc. as needed.

  1. Project title
  2. Core inquiry questions you're pursuing
  3. Rationale for project: what motivates you to engage with this subject so deeply? What's at stake?
  4. Create a written description of what you hypothesize you might uncover in your data-drive inquiry and why. What uncertainties remain?
  5. Create a cause-outcome relationship diagram underpinning your systems of investigation: Which actions, forces, or events trigger or influence what key outcomes of interest?
  6. As a result of your cause-effect diagramming, identify which one or two variables you hope to track will be of most interest to your analysis
  7. Generate TWO or THREE sample visualizations which display the value of your variables of interest over some time period--probably the length of our study, which is about a month and a half. NOTE: you'll want to study and complete phase 2, tool building to be most thorough at creating a sample visualization using your planning from that phase.

Complete universal deliverables checklist

Each phase culminates in documenting your work, pushing those changes to your public repo, and making an entry in our tracker.

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wb_incandescentPhase 2: Tool building

  1. Identify a subjective component of interest in your project, such as your own self's internal feeling about a thing, person, event, etc. It could also be a quality score of some kind, such as the relative strength of some output of interest, such as practice writing samples.
  2. Create a spreadsheet and create a scoring scale of some range of discrete vales. Name the scale something meaningful
  3. On a dedicated tab, generate a coding guide for each individual value such that as you use this scale to record data your reported values are all mapped to a linguistic representation of this subjective measurement.
  4. On a dedicated tab, create a data entry table with appropriately named columns and declared data types for each column to record the values measured using your scale.
  5. Add additional columns for measurements using other input devices, such as third party behavior tracking applications, inventory control systems, public data reporting agencies--i.e. weather, news.
  6. On a dedicated tab create a data dictionary describing each of the columns in your data gathering tool

Complete universal deliverables checklist

Each phase culminates in documenting your work, pushing those changes to your public repo, and making an entry in our tracker.

Sample quantification scales

The multifaceted nature of inter-human relations lends itself to quantification of some kind, especially when one is attempting to make choices based on relative rankings of individuals such as during a job role selection process or dating.

The following scales were developed to record a person's experience meeting folks in person via a prominent online dating application.

quantification scale for rating in-person online dating encounters quantification scale for rating in-person online dating encounters quantification scale for rating in-person online dating encounters quantification scale for rating in-person online dating encounters quantification scale for rating in-person online dating encounters quantification scale for rating in-person online dating encounters

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wb_incandescentPhase 3: Beta data gathering

  1. Try using your collection instrument created in phase 2 for about a week: see how your scales relate to reality? Adjust as needed.
  2. Prepare to share preliminary results of your beta testing with your peers in person during the mid-term meeting at North

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