Data Visualization


Number of Credits : 3

Class Sessions : 3hrs a week

Instructor: Chamil Rathnayake, School of Communications

Meeting Time: Friday 1.30pm- 4.00pm, iLab


Office: Crawford 310

Office Hours: T,TR 11.00am-1.00pm, and by appointment


Recent changes in the information landscape have resulted in drastic changes in the digital media environment. Widespread availability of digital data and increased ability to process large amounts of data have raised the need for data-driven approaches for many fields. During the past decade, many data-driven communication initiatives have emerged and are becoming increasingly popular. Open data initiatives bring governance, civic engagement, and participation to a new level. Organizations use large volumes of data to understand their performance. Moreover, mainstream media organizations increasingly embrace data-driven news production.

Analytics, an approach that focuses on discovering patterns in data, can provide interesting insights that can transform the way governments, business organizations, media, and citizens understand their environments. Analytics and related areas, such as visual analytics and data-driven storytelling, have already permeated many fields. In the context of mainstream media, changes are already significant. For instance, the rise of data-driven journalism is a significant change in the media landscape. Moreover, with the rise of “big data,” a tendency to look at large sets of digital data, organizations increasingly pay attention to data-driven decision-making.

Data-driven approaches for communication range from citizen-level engagement to institutional initiatives. Data-driven communication initiatives take the form of analytical approaches that use widely available data to create visualizations that can lead to meaningful stories and disseminate them to different audiences. This branch of communication, however, demands a new set of skills. For instance, data-driven storytelling requires a wide range of skills including the ability to access, clean, and prepare data, visualize and analyze data, explain emerging patterns, and interpret those patterns in ways that benefit different audiences. Acquiring these skills requires an interdisciplinary approach in which knowledge, skills, and resources in different fields such as data science, network science, and statistics, are framed within a communication focus.

This class focuses on providing knowledge and skills necessary for communication students to start exploring the emerging field of data-driven communication and make them familiar with tools and analytical approaches that can be used for data-driven communication.

Course Objectives and Learning Outcomes

After the completion of this class, students will be able to:

  1. Demonstrate understanding of data-driven content, its scope, and importance in communication.
  2. Critically evaluate data-driven content.
  3. Develop the ability to identify data sources, demonstrate the ability to use appropriate tools to obtain, analyze, visualize them, and write data-driven stories of publishable quality.

The above course objectives help achieve the following Program Student Learning Outcomes:

  1. Design communication and media projects to make meaningful contributions to diverse social, professional or academic communities, communicating effectively orally, in writing, and through digital media.
  2. Reflect critically on communication products such as media productions, research and policy reports and everyday texts.
  3. Demonstrate preparedness for academic and professional careers in communication.

The class is structured based on three important aspects that help students to understand the field and start acquiring skills necessary to build a career in a field that demands data-driven communication. First, we will discuss the scope of data-driven inquiry, understand (blurred) boundaries, and explore the processes used. Second, we will explore practical aspects necessary. This includes searching and obtaining data, processing data, analyzing, visualizing data, and writing data stories. The third aspect of the class will cover examples of data-driven communication and engagement initiatives and provide an understanding of the role they play in society.


We will read several chapters from the following books. We will also read several journal articles and news articles. Reading materials for each week will be available on Laulima.

  1. Borgman, C., L. (2015). Big data, little data, no data: Scholarship in the networked world. MIT Press.
  2. The Data Journalism Handbook: How Journalists Can Use Data to Improve the News. Edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers. O’Reilly. Available free at
  3. Zumel, N. and Mount, John. (2014). Practical Data Science with R. Manning Publication Co. NY.
  4. Borner, K, and Polley, D.E. (2014). Visual Insights: A Practical Guide to Making Sense of Data. The MIT Press.
  5. Kadushin, C. (2012). Understanding Social Networks. Oxford University Press.

Course Structure

This course consists of four main components: 1) Introduction, 2) Fieldwork and Data Preparation, 3) Analysis and Visualization, and 4) Interpretation and Story Writing. Under these four components, we will explore the following 12 topics in this semester.


  1. Rise of the “data Culture”: perils, opportunities, and data journalism
  2. Understanding data, scope, and processes
  3. Brainstorming, searching, and asking data questions

Fieldwork and Data Preparation

  1. Finding data and data formats
  2. Cleaning data, processing, and converting data into readable formats

Analysis and Visualization

  1. Visualizing location data
  2. Visualizing attribute data
  3. Statistical analysis and data visualization
  4. Understanding and visualizing relational data

Interpretation and Story Writing

  1. Writing data stories, techniques, and reasoning
  2. Ethical aspects


Software/platformsrecommended: Tableau Public, CartoDB, Google Fusion Tables, BIRTihub, NodeXL, Gephi, R, and CorelDRAW Graphics.

You can also find a list of useful data analysis and visualization tools on the Tools page of the class website. This list includes “point-and-click” tools as well as programs that require programming knowledge. Feel free to experiment if you have programming experience. However, computer programming experience and statistics knowledge is not required to take this class. I encourage you to learn new tools and share your experience with your classmates.


Week 1- Introduction to COM 480/JOUR 459 and getting to know each other.


Chapter 1, Gray, Bounegru, and Chambers, Data Journalism Handbook.

Week 2- Understanding data, scope, and processes.


Chapter 2, Gray, Bounegru, and Chambers, Data Journalism Handbook, and Chapter 1, Zumel. and Mount. (2014),Practical Data Science with R.

Week 3- Brainstorming, searching, and asking data questions.

In this week we will do several group activities to brainstorm possible topics for data-driven stories and develop appropriate data questions.

Week 4- Finding data, data formats, cleaning, processing, and converting data into readable formats.


Chapter 4 and 5, Gray, Bounegru, and Chambers, Data Journalism Handbook.

Week 5- Approaching analysis and visualization.


Patterson, R.E., Blaha, L.M., Grinstein, G.G., Liggett, K.K., Kavaney, D.E., Sheldon, K.C., Havig, P.R., Moore, J.A.  (2014). A human cognition framework for information visualization. Computers and Graphics. 42: 42-58.

Inanc, B., & Dur, U. (2012). Analysis of data visualizations in daily newspapers in terms of graphic design. Procedia-Social and Behavioral Sciences. 51(278-283.

(We will also prepare your computers for future sessions this Wednesday).

 Week 6- Visualizing location data: We will practice CartoDB during this week. You should have found an appropriate data set by this time to work with.


Ormeling, F. (2013). Cartography as a Window to the World. The Cartographic Journal. 50(2), pp(175-181).

Probasco, N. (2014). Cartography as a tool of colonization: Sir Humphrey Gilbert’s 1583 voyage to North America*. Renaissance Quarterly. 67:425-472.

Week 7- Visualizing attribute data I: We will practice Tableau Public during this week. You should have found an appropriate attribute data set by this week.


Walker, R., Cenydd, L., Pop, S., Miles, H.C., Hughes, C.J., Teahan, W.J. Roberts, J.C. Storyboarding for visual analytics. Information Visualization. 14(1):27-50.

Week 8– Visualizing attribute data II: This week is an Introduction to R in general.


Week 9– Visualizing attribute data III: This week, we will try to learn some commands in the R ggplot2 package.

Week 10- Introduction to relational data.

Readings: Chapter 1 and 2, Kadushin. (2012),Understanding Social Networks.

Week 11- Visualizing relational data. This week, we will practice using the network analysis software Gephi (if you are a PC user, you can try NodeXL which is a popular tool used for network analysis).

Week 12- Open data, analytics, and public engagement.


Valkanova, N. (2015). Public visualization displays of citizen data: Design, impact and implications. International Journal of Human Computer Studies. 81(4-16).

Arribas-Bel, D. (2014). Accidental, open and everywhere: Emerging data sources for the understanding of cities. Accidental Geography. 49: 45-53.

Week 13- Challenges, ethics, and data-driven storytelling.


Messner, M. and Garrison, B. (2007) Journalism’s ‘dirty data’ below researchers’ data. Newspaper Research Journal. 28(4):  88-100.

Grubmuller, V., Gotsch, K., Krieger, B. (2013). Social media analytics for future oriented policy making. European Journal of Futures Research. 1(20): 1-9.

Week 14 and 15- Project presentations

Class Blog

The class blog,, serves as a forum for you to publish data-driven content and it provides you links to various resources that can help you to develop information visualization skills. This website and the Data Readers Facebook page are open for a real-world audience. Your work will be published on the site. You are encouraged to use the as a venue to practice writing data-driven stories for a real audience and a collection of resources that you can contribute to.


  1. Data news summary and evaluation (5 points): In this assignment, you will select one or more data-driven news stories and write a short evaluation of the story.
  2. Searching for data, choosing a rationale for a data story (15 points): You will develop a foundation for your final project in this assignment. For this assignment, you need to find three types of data (location-based, attribute, and relational) that you can use to write stories, and submit a short description on how your data could help write interesting stories.
  3. Data cleaning, analysis, and visualization (20 points): At this stage, you will clean your data, and use to tools introduced in the class to create several visualizations for your stories.
  4. Story writing and presentation (Final paper) (40 points): For the final paper, you are required to write three short stories using your analysis/visualizations.
  5. Participation (20 points): Participation includes classroom activities, weekly reading summaries, and homework assignments.

Assignment Format

Assignments must be submitted typed, double‐spaced in 12‐point Times New Roman font (or a similar font and size). Your name, the course number, date, and the title of the assignment should appear on the top right corner of the paper. All papers should have one-inch margins, and each page should be numbered.

Late submissions

Papers and other assignments must be submitted on the assigned date. Late papers will only be accepted in emergency circumstances. You need to provide documented evidence (e.g., a doctor’s note) if you cannot turn in papers on the assigned date due to an emergency. For a one-day delay, you will lose 20% of the total grade of the assignment. If you are unable to submit your assignment on the due date due to unavoidable circumstances, please contact me in advance.

Extra credit

Extra credit may be offered in the class. Your participation in extra-credit activities is necessary for you to earn extra credit. Extra-credit activities may not be announced in advance.

Academic Integrity

You are expected to follow the rules of conduct outlined in the University of Hawaii Student Conduct Code. Violation of this Code will automatically result in a course grade of “F” and referral to the Chair of the School of Communications.  You need to pay special attention to the originality of your work. All of your writing must be your own. Plagiarism occurs when you use other’s work without sufficiently acknowledging the author. Therefore, please be sure to cite whenever you use the work of others. Please use the guidelines given in the American Psychological Association (APA) style in your writing assignments. More information on the APA style can be found on  It is possible that some other courses cover similar topics. However, you are not allowed to submit work for which you have already earned credit in another class. Please let me know in advance if you want to use such work in your papers.

For more information on academic integrity, please refer to the campus policies page in the UH Manoa catalog. If you have questions concerning citations and other issues in your writing, please contact me by email at any time. See:


We will read a collection of book chapters and articles in this class, and the readings will be uploaded to Laulima.  You are supposed to read the assigned materials before class and come prepared for the discussion. You are also encouraged to find relevant reading materials and share them with your classmates.


Regular class attendance is mandatory for you to be successful in this course. Attendance will be taken during each class session. Please note that three or more unexcused absences will result in the lowering of your grade for participation. If you anticipate being absent due to an emergency, please contact me and make arrangements to complete your work. In case of a medical emergency, you will be required to submit documentation, such as a doctor’s note, to excuse the absence. Being absent does not excuse you from turning in an assignment on time.

Classroom Participation and Laptop Use

Participation is important to create a rich learning environment. Your participation in discussions is graded. We will have weekly discussions on Laulima that will be considered a part of your participation in this class. After each topic, you can log on to Laulima to discuss and share your ideas with your classmates. Disruptive behavior in class will not be tolerated. This includes, among other things, talking when the instructor or your fellow students are talking.

Laptops are allowed in the class. However, you should use laptops and other devices only for the purposes of the class. Please do not use these devices to browse websites, especially social media sites, which might interrupt your attention. Please be sure to turn off/ mute your cell phones before the discussion begins.

Your participation in group exercises and assignments will be observed, and lack of participation will affect your grade. A discussion page will be created for each week, and you can use this page to provide some feedback on the topics discussed in each week. Make sure that you use the discussion forum at least once a week. This is a good venue to discuss possible topics for the final paper, ask questions from the instructor or classmates, and share interesting information.


The following scale will be used to determine your final grade:

Total Points Final Grade
A+ 100
A 95
A- 90
B+ 87
B 83
B- 80
C+ 77
C 73
C- 70
D+ 67
D 63
D- 60
F 59 or less

Your assignment grades will be uploaded to Laulima no later than two weeks after the due date for submission. You are welcome to inquire about your class total throughout the semester.

Chamil Rathnayake