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Data Journalism

Eastern Mediterranean University Faculty of Communication and Media Studies Department of New Media and Communication

Course Outline

This course introduces students to the principles and practices of data journalism. It covers data collection, analysis, visualization, and storytelling techniques, combining theoretical knowledge with practical skills. By the end of the course, students will be equipped to uncover and report data-driven stories effectively.

Course Completion Requirements

Attendance: Students must attend at least 70% of the classes to ensure consistent engagement with the course material and activities.

Submission: The project proposal, midterm exam and final project must be submitted on time.

Exams and Projects

A project proposal, midterm and final project will be applied within the scope of the course.

  • Project Proposal %20
  • Midterm project %30
  • Final project %50

Format of the Lesson

Data Journalism course is going to be conducted to theoretical and practical. Theories are going to be transferred to form a basis for practical studies. All theoretical information will be shared on this site weekly.

Course Outcomes

Upon completion of this course, students will be able to:

  • Demonstrate an understanding of the fundamental principles of data journalism.
  • Collect, cleanse, and analyse data from a range of sources.
  • Create data visualisations using appropriate tools and techniques.
  • Integrate data findings into compelling journalistic narratives.
  • Apply ethical considerations in data journalism.
  • Collaborate effectively in multidisciplinary teams.

Reading Pack

Books

      • “The Data Journalism Handbook” by Jonathan Gray, Liliana Bounegru, and Lucy Chambers (Free online version: The Data Journalism Handbook)

        • <li>”Data Journ

    alism: Insi

de the Global F

    uture” by John Mair, Richard Lance Keeble, and Megan Knight

  • “Data Cleaning” by Ihab F. Ilyas and Xu Chu
  • “Naked Statistics: Stripping the Dread from the Data” by Charles Wheelan
  • “The Visual Display of Quantitative Information” by Edward R. Tufte
  • “Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic
  • “The Truthful Art: Data, Charts, and Maps for Communication” by Alberto Cairo
  • “The Elements of Journalism: What Newspeople Should Know and the Public Should Expect” by Bill Kovach and Tom Rosenstiel
  • “Data Journalism Heist” by Meredith Broussard (selected chapters)
  • “Data-Driven Storytelling” edited by Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, and Sheelagh Carpendale
  • “Collaborative Journalism: Newsroom Partnerships and the Future of the Craft” by Eric Freedman and Robyn S. Goodman
  • “Investigative Data Journalism” by Helena Bengtsson
  • “Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations” by Scott Berinato
  • “Future Journalism: Where We Are and Where We’re Going” by Denis Muller

Articles

  • “What is Data Journalism?” by Paul Bradshaw
  • “How to Collect Data for Data Journalism” by Eva Constantaras</li>&amp;amp;amp;amp;amp;amp;lt;li>”A Practical Guide to Cleaning Data in Excel” by Ann K. Emery&amp;amp;amp;lt;/li>
  • “Introduction to Data Analysis for Journalists” by David Herzog
  • “Data Visualization Best Practices” by Stephanie Evergreen
  • “An Introduction to Tableau for Data Journalism” by Quartz&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/li&gt;</li><li>”How to
  • Tell Stories with Data: A Guide for Data Journalists” by DataJournalism.com
  • “Ethical Guidelines for Data Journalists” by The Center for Investigative Journalism
  • <li&amp;amp;amp;amp;gt;”Creating Interactive Data Visualizations with D3.js” by Scott Murray
  • “Collaborative Data Journalism” by DataJournalism.com
  • “Data Journalism in Investigative Reporting” by Nieman Reports
  • “Communicating Data Findings: The Art of Writing Data-Driven Stories” by The Open Notebook
  • “The Future of Data Journalism” by DataJournalism.com

Weekly Courses Content

1. Week

  • Introduction to Data Journalism: History, Importance, and Key Concepts&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/li>

Introduction to Tools: Excel, Google Sheets

2. Week

  • Data Collection: Sources and Methods

Data Scraping and APIs: Practical Exercises

3. Week

  • Data Cleaning: Techniques and Best Practices

Hands-on with OpenRefine and Python (Pandas)

4. Week

  • Data Analysis: Statistical Concepts and Methods

Using Excel and Python for Basic Data Analysis

5. Week

  • Introduction to Data Visualization: Principles and Types

Creating Charts and Graphs in Excel and Google Sheets

6. Week

  • Advanced Data Visualization: Tools and Techniques

Hands-on with Tableau and Power BI

7. Week

  • Storytelling with Data: Building a Narrative

Case Study Analysis: Successful Data Journalism Stories

8 - 9. Weeks

Midterms

10. Week

  • Ethics in Data Journalism: Accuracy, Privacy, and Transparency

Ethical Scenarios: Group Discussions and Exercises

11. Week

  • Interactive Data Journalism: Web and Mobile

Creating Interactive Visualizations with D3.js and Flourish

12. Week

  • Collaboration in Data Journalism: Roles and Workflow

Group Project: Collaborative Data Story Development

The Entanglements between Data Journalism, Collaboration and Business Models: A Systematic Literature Review

13. Week

  • Investigative Data Journalism: Techniques and Case Studies
  • Communicating Data Findings: Writing and Presentation

<p>

Applying Investigative Techniques to a Case Study

Crafting a Data-Driven Story: Writing Workshop

14. Week

Final project submission

Detailed Grades

Project Proposal

%20

A project idea will be developed to work with the data. When determining a topic related to your field of study, you should prepare your project in such a way that at least 2 years of data will be compared. A presentation will be prepared by filling out the document given for the project. The document should be filled in completely and the presentation should be prepared by considering the following headings.

Presentation

  • Topic
  • Problem Statement
  • Importance of the Project
  • Data
    • Gathering Method
    • Clearing Method
    • Visualisation Method
  • Results

Evaluation Criteria

  • Project Document
  • Presentation

Midterm Project

%30

It will be in the form of writing the content of the project proposal. The content should consist of 1500-2000 words and references should be given in APA format. Visualisations of the data should be included in the text.

Evaluation Criteria

  • Content
  • Data Visualisation

Final Project

%50

By determining a project different from the previous project, it is necessary to prepare the project document, write the content (with visualisations) and make a presentation.
As in the previous project, content should be produced between 1500-2000 words. Likewise, references should be given in APA format.
The presentation should be prepared in the same way.

Evaluation Criteria

  • Project Proposal Document
  • Presentation
  • Content (with visualisation)
Visualization
Data