Data Journalism



Welcome to our Data Journalism course, an exciting and comprehensive journey designed to equip you with the skills necessary to become a proficient data journalist. Over 15 weeks, you will learn about the fundamentals of journalism and data-driven storytelling, master data manipulation and visualization techniques using R and Tableau, and delve into ethical and legal considerations.

Throughout the course, you’ll develop a strong foundation in identifying newsworthy stories, conducting interviews, and fact-checking information. You will also gain hands-on experience in data cleaning, preprocessing, exploratory analysis, and various data visualization techniques, including advanced chart types and interactivity.

By the end of this course, you will have completed a data journalism project incorporating data analysis, visualization, and journalistic storytelling. With a solid understanding of the concepts and tools covered, you’ll be well-prepared to apply your skills in the ever-evolving field of data journalism.


Week 1: Introduction to Journalism and Data Journalism

  • What is Journalism?

  • Fundamentals of news writing and reporting

  • The importance of data-driven storytelling

  • What is Data Journalism?

  • Overview of tools: R, Tableau, and others

Week 2: Finding and Evaluating News Stories

  • Identifying newsworthy stories

  • Generating story ideas

  • Evaluating story angles and potential impact

  • Sourcing data for stories

  • Evaluating data quality and credibility

Week 3: Interviewing and Fact-Checking

  • Principles of journalistic interviewing

  • Preparing for and conducting interviews

  • Fact-checking and verifying information

  • Ethical considerations in interviewing and reporting

Week 4: Data Cleaning and Preprocessing

  • Introduction to data manipulation in R (tidyverse)

  • Data cleaning, filtering, and aggregation

  • Data transformation and handling missing data

  • Data normalization and scaling

Week 5: Descriptive Statistics and Exploration

  • Descriptive statistics in R

  • Exploratory data analysis (EDA) with R

  • Identifying trends, patterns, and outliers

  • Asking the right questions

Week 6: Introduction to Data Visualization with R

  • Introduction to ggplot2

  • Grammar of graphics with ggplot2

  • Customizing plots: themes, scales, labels, and titles

  • Different types of plots and when to use them

Week 7: Advanced Data Visualization with R

  • Advanced ggplot2 techniques

  • Faceting and multi-panel plots

  • Time series and geospatial data visualization

  • Interactive visualizations with plotly or ggplotly

Week 8: Introduction to Tableau

  • Tableau interface and basics

  • Connecting Tableau to data sources

  • Creating and customizing visualizations in Tableau

Week 9: Advanced Data Visualization with Tableau

  • Advanced chart types and techniques in Tableau

  • Creating dashboards and stories in Tableau

  • Interactive and dynamic visualizations in Tableau

  • Geospatial data visualization in Tableau

Week 10: Combining R and Tableau

  • Exporting R data and visualizations to Tableau

  • Integrating R scripts within Tableau

  • Leveraging the strengths of both tools for data journalism

Week 11: Crafting Compelling News Stories

  • Structuring news articles and data-driven stories

  • Balancing text and visualizations

  • Writing clear and concise news copy

  • Incorporating quotes and interview material

  • Ensuring accuracy and transparency

Week 12: Digital Writing and Interactive Media with Shiny and Quarto

  • Introduction to Shiny and Quarto for interactive media

  • Creating Shiny apps and Quarto websites for digital storytelling

  • Embedding data visualizations and interactive elements

  • Best practices for designing engaging and accessible digital content

Week 13: Legal and Ethical Considerations

  • Data privacy and security

  • Copyright and licensing issues

  • Responsible data reporting and fact-checking

  • Avoiding bias and promoting inclusivity in journalism

Week 14: Team project consultation

Week 15: Project presentation

  • Students present their data journalism projects

  • Projects should incorporate data analysis, visualization, and journalistic storytelling

  • Feedback and discussion on projects

  • Reflecting on the course and potential future applications in the field of data journalism