Data Studies Badge

Data Studies Badge

The Data Studies Badge recognizes undergraduate students with significant experience with interdisciplinary, humanistic approaches to data and an understanding of the ethics and humanistic complexities of data science. After completing a number of activities and submitting a portfolio, students will be awarded a Data Studies Badge. The award is coordinated by the Digital Innovation Lab as part of its efforts to support digital humanities research and teaching. Badges will be posted on the Credly micro-credentialing website and can be integrated into sites like LinkedIn or online professional web spaces. Badge recipients will also be listed on the Carolina Digital Humanities/Digital Innovation Lab Web site.

Interested in joining our initial Data Studies cohort? Please apply here. 

There are classes available in the University Libraries’ Research Hub at Davis Library that can help you gain many of the skills associated with the badge.

To earn the Data Studies Badge, students must complete activities that demonstrate expertise in each of the five capacities below:

Capacity 1: Understand the foundational aspects of working with data.

Outcomes:

  • Collect, clean & curate.
  • Understand data schemas.
  • Understand storage and infrastructure needs related to data.
  • Demonstrate proficiency in common data tools and methods (e.g. Excel, Python, R).

Requirements: (choose a or b; c is required)

(a) Successful completion of a university course that covers the outcomes above (e.g. STOR 320, INLS 161, or SOCI 252. You may submit the syllabus of another course that would fulfill this requirement).

(b) Successful completion of at least three online modules that cover the outcomes above (e.g. Lynda.com “Cleaning Bad Data in R,” “Data Science Foundations: Choosing the Right Database,” “Python for Data Science Essential Training“).

(c) Portfolio piece: Submit a reflection detailing coursework, training, or other activities and discuss their role in developing an understanding of best practices for working with data using tools of your choice. Include in the reflection concrete examples from experience and evidence of completion of item a or b (i.e., a transcript or certificate screenshot).

Capacity 2: Create and engage with visual representations of data.

Outcomes: 

  • Make visualizations following best practices in design and data representation. 
  • Demonstrate proficiency with common visualization tools. 
  • Understand the ways that visualizations can capture and distort or simplify data complexity. 

Requirements: 

(a) Creation of a collection of visualizations with a tool (such as Tableau) or programming language with at least three visualizations.

(b) Creation of a portfolio of maps or a map-based project using GIS methods. 

(c) Portfolio piece: Submit a reflection that includes samples of your visualizations and explains the ways they help represent information; include reflections on limitations associated with your visualizations.  

Capacity 3: Understand the interpretive complexities and cultural and ethical implications of working with data. 

Outcomes: 

  • Understand types and consequences of data misuse. 
  • Understand implications of data in society with regard to privacy, security, economics, politics, justice, etc. 
  • Interpret and critique others’ work with data. 
  • Interpret and critique your own work with data. 

Requirements: 

(a) Participate in the Digital Innovation Lab’s “Humanistic Approaches to Data Science” Workshop, having completed all required reading.

(c) Portfolio piece: Write a reflection on your participation in the workshop.  

Capacity 4: Engage with interdisciplinary approaches to data.

Outcomes: 

  • Demonstrate familiarity with humanistic engagements with data science and data literacy. 
  • Participate in an experiential learning project that integrates data science methods with the humanities.

Requirements: (choose either only a or both b and c; d is required)

(a) Successful completion of a course emphasizing humanistic approaches with a data-centric experiential learning component  (e.g., ANTH/FOLK 370: “Southern Legacies, The Descendants Project”, ENGL 353: “Rhetoric and Digital Humanities, Becoming Digital Makers”, or HIST 273: “Water in the Middle East.” You may submit the syllabus of another course that would fulfill this requirement).

(b) Read five articles or a book and two articles related to data use in the arts, humanities, or humanistic social sciences (suggested bibliography).

(c) Contribute to a project you have joined or complete a project of your own creation that includes the application of data science methods to a project in the humanities, arts, or humanistic social sciences.

(d) Portfolio piece: Submit a reflection that accounts for your experiences with item a or b by exploring methodological, intellectual, and ethical implications of integrating data science into other disciplines. Include as well a reflection on your participation in an experiential project from item a or c.  

Capacity 5: Share your data experiences with a broad audience.  

Outcomes:  

  • Contextualize and present project findings and /or data activities to public audiences. 
  • Translate knowledge developed through work with data into responsible interpretations and narratives.  

Requirements:  (choose a, b, or c; d is required)

(a) Give a presentation associated with data in a public forum (outside of class; for example, at the Celebration of Undergraduate Research). 

(b) Submit findings or experiences related to data for publication in a journal or other public venue. 

(c) Create a video, poster, or other media item(s) that presents aspects of work with data to the public. 

(d) Portfolio piece:  Submit evidence and materials from your presentation, the text of your publication, or a link to your media items. 

The Director of the Digital Innovation Lab will make final determinations about qualification for the Data Studies Badge.