Macquarie University - Data Journalism
In this course, students are introduced to Python as a powerful tool for data-driven storytelling. Python's versatility allows media practitioners to conduct initial analyses on a wide range of socio-cultural phenomena, transforming raw data into compelling narratives. Throughout this course, students learn the fundamental concepts and techniques of using Python to analyze diverse datasets common in modern journalism. They gain hands-on experience in data collection, cleaning, and basic statistical analysis using Python libraries such as pandas. By the end of the course, students will have the foundational skills to use Python for exploring datasets, identifying trends, and uncovering stories that might otherwise remain hidden in the numbers.
You can run all the notebooks for this course using Colab (link above). If you prefer to work locally, I recommend using Jupyter notebooks which are easily accessible using the Anaconda interface.
| Week | Topic | Materials |
|---|---|---|
| 0 | Preparation | notebook |
| 1 | Introduction to Data Journalism | slides |
| 2 | Press Freedom, FOI, Open Data, and Ethics + Foundation of Statistics | slides |
| 3 | Foundation of Statistics + Code Foundations | slides + notebook |
| 4 | Data Gathering and Cleaning | slides + notebook |
| 5 | Data Analysis: Exploring your data | slides + notebook |
| 6 | Combining Data | slides + notebook |
| 7 | Visualizing Data | slides |
| 8 | OSINT, Geojournalism, Maps, Crowdsourcing, and Social Media Data | slides |
| 9 | Platform Journalism | slides |
| 10 | Due Diligence and Follow the Money | slides |
| 11 | AI and Journalism | slides |
The datasets used in this course can be found here.
Students can also use these datasets(https://github.com/mromanello/ADA-DHOxSS/tree/master/data) to test their knowledge. They are from the Applied Data Analysis course.
See the assignments folder.
- Bradshaw, P. (2007). The inverted pyramid of data journalism. 1–7. http://onlinejournalismblog.com/2011/07/07/the-inverted-pyramid-of-data-journalism/
- Tong, Jingrong, and Landong Zuo. 2021. “The Inapplicability of Objectivity: Understanding the Work of Data Journalism.” Journalism Practice 15 (2): 153–69. https://doi.org/10.1080/17512786.2019.1698974.
- de-Lima-Santos, Mathias Felipe, Aljosha Karim Schapals, and Axel Bruns. 2021. “Out-of-the-Box versus in-House Tools: How Are They Affecting Data Journalism in Australia?” Media International Australia 181 (1): 152–66. https://doi.org/10.1177/1329878X20961569
- Mahmoud, Raghda, and Shaima’a Z. Zoghaib. 2023. “The Effects of Different Data Visualisation Formats on News Recall and Comprehension.” Media Watch, March, 097609112311587. https://doi.org/10.1177/09760911231158746
- Jansen, Yvonne, Pierre Dragicevic, Petra Isenberg, Jason Alexander, Abhijit Karnik, Johan Kildal, Sriram Subramanian, and Kasper Hornbæk. 2015. “Opportunities and Challenges for Data Physicalization.” Conference on Human Factors in Computing Systems - Proceedings 2015-April (April): 3227–36. https://doi.org/10.1145/2702123.2702180
- Lenzi, Sara, and Paolo Ciuccarelli. 2020. “Intentionality and Design in the Data Sonification of Social Issues.” Big Data & Society 7 (2): 205395172094460. https://doi.org/10.1177/2053951720944603
- Boss, Katherine, and Meredith Broussard. 2017. “Challenges of Archiving and Preserving Born-Digital News Applications.” IFLA Journal 43 (2): 150–57. https://doi.org/10.1177/0340035216686355
- Heravi, Bahareh, Kathryn Cassidy, Edie Davis, and Natalie Harrower. 2021. “Preserving Data Journalism: A Systematic Literature Review.” Journalism Practice, March, 1–23. https://doi.org/10.1080/17512786.2021.1903972
- Broussard, Meredith, and Katherine Boss. 2018. “Saving Data Journalism.” Digital Journalism 6 (9): 1206–21. https://doi.org/10.1080/21670811.2018.1505437
A good companion for this course is John Canning, Statistics for the Humanities, 2014. Also recommended are Melanie Walsh, Introduction to Cultural Analytics & Python, 2021 and Karsdorp, Kestemont, Riddell, Humanities Data Analysis: Case Studies with Python, 2021.
Mathias-Felipe de-Lima-Santos (Ph.D.) is a Lecturer (aka Assistant Professor) at Macquarie University. He is also a research associate in the Digital Media and Society Observatory (DMSO) at the Federal University of São Paulo (Unifesp), Brazil. Previously, he was a postdoctoral researcher in the Human(e) AI and AI4Media projects at the University of Amsterdam, Netherlands and a researcher at the University of Navarra, Spain, under the JOLT project, a Marie Skłodowska-Curie European Training Network funded by the European Commission’s Horizon 2020. He was also a Visiting Researcher at the Queensland University of Technology (QUT) in Brisbane, Australia. Mathias-Felipe is co-editor of the book “Journalism, Data, and Technology in Latin America” and the upcoming two-volume book “Fact-Checking in the Global South” both by Palgrave Macmillan. Mathias-Felipe is currently part of the editorial board of Digital Journalism. His research interests include the changing nature of communications driven by technological innovations, particularly in journalism, media, and online social networks.
Everything in this repository which is not already attributed to someone else is released under CC BY 4.0.