Data Literacy β What is it and why does it matter?
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Data Literacy β What is it and why does it matter?
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There are 3 modules in this course
You might already know that data is not neutral. Our values and assumptions are influenced by the data surrounding us - the data we create, the data we collect, and the data we share with each other. Economic needs, social structures, or algorithmic biases can have profound consequences for the way we collect and use data. Most often, the result is an increase of inequity in the world. Data also changes the way we interact. It shapes our thoughts, our feelings, our preferences and actions. It determines what we have access to, and what not. It enables global dissemination of best practices and life improving technologies, as well as the spread of mistrust and radicalization. This is why data literacy matters.
A key principle of data literacy is to have a heightened awareness of the risks and opportunities of data-driven technologies and to stay up-to-date with their consequences. In this course, we view data literacy from three perspectives: Data in personal life, data in society, and data in knowledge production. The aim is threefold: 1. To expand your skills and abilities to identify, understand, and interpret the many roles of digital technologies in daily life. 2. To enable you to discern when data-driven technologies add value to peopleβs lives, and when they exploit human vulnerabilities or deplete the commons. 3. To cultivate a deeper understanding of how data-driven technologies are shaping knowledge production and how they may be realigned with real human needs and values. The course is funded by Erasmus+ and developed by the 4EU+ University Alliance including Charles University (Univerzita Karlova), Sorbonne Unviersity (Sorbonne UniversitΓ©), University of Copenhagen (KΓΈbenhavns Universitet), University of Milan (UniversitΓ degli studi di Milano), and University of Warsaw (Uniwersytet Warszawski).
If you use Google and have a look at your Google Dashboard you will probably be amazed by how much data this company has collected about your online activities. Now think about all the other internet services, social media sites, and databases that may have a file on you, your health, your actions and inclinations. In this module, we will explore user tracking and information harvesting, define personal data and discuss the limits in managing your personal data disclosure. Consequently, we will present the legal framework for data protection and processing.
What's included
4 videos9 readings3 assignments2 discussion prompts
4 videosβ’Total 40 minutes
- 1.1 Introduction to the Courseβ’4 minutes
- 1.2 Revealing the Infrastructure of Digital Advertisingβ’9 minutes
- 1.3 Personal Data & the Problems of Empowermentβ’15 minutes
- 1.4 Legal Aspects, Security and Privacyβ’13 minutes
9 readingsβ’Total 90 minutes
- Internet Service Providers Are Collecting -and Sharing- Vast Amounts of Information About Customersβ’5 minutes
- A Look at What ISPs Know About Youβ’7 minutes
- 1.1 Further Reading and Resourcesβ’0 minutes
- Digital AdTech: The Complete Guideβ’8 minutes
- Web Tracking's Opaque Business Model of Selling Usersβ’20 minutes
- Empowering Resignation: There's an App for Thatβ’25 minutes
- Education on Cyber Security Issues Under EU Lawβ’5 minutes
- Measuring the GDPR's Impact on Web Privacyβ’20 minutes
- 1.4 Further Reading and Resourcesβ’0 minutes
3 assignmentsβ’Total 30 minutes
- 1.2 Quizβ’9 minutes
- 1.3 Quizβ’12 minutes
- 1.4 Quizβ’9 minutes
2 discussion promptsβ’Total 30 minutes
- Your view on data literacyβ’15 minutes
- Who tracks you?β’15 minutes
In this module we expand on Module 1 by looking at how networked data and algorithms affect the way we see the world. From global dissemination of best practices and life improving technologies to the spread of hate and radicalization, we trace the mechanisms by which data-driven technologies can add value to peopleβs lives, and how they can exploit human vulnerabilities.
What's included
3 videos6 readings2 assignments3 discussion prompts
3 videosβ’Total 45 minutes
- 2.1 The Attention Economyβ’13 minutes
- 2.2 Journalism, Data and Democracyβ’18 minutes
- 2.3 How to Find the Truth in the Networkβ’14 minutes
6 readingsβ’Total 75 minutes
- The Attention Economyβ’20 minutes
- 2.1 Further Reading and Resourcesβ’0 minutes
- Clarifying Journalism's Quantitative Turnβ’30 minutes
- 2.2 Further Reading and Resourcesβ’0 minutes
- Educating for Misunderstandingβ’25 minutes
- 2.3 Further Reading and Resourcesβ’0 minutes
2 assignmentsβ’Total 12 minutes
- 2.2 Quizβ’6 minutes
- 2.3 Quizβ’6 minutes
3 discussion promptsβ’Total 65 minutes
- Should information be regulated?β’20 minutes
- Can you think of more examples?β’20 minutes
- How would you rate these websites?β’25 minutes
Big data and novel computational methods have revolutionized the way we create knowledge. We will show by example how this knowledge is used and what it implies for the future of humanity. We look at AI-research, computational social science, machine learning and education, and through these examples, we will try to cultivate a deeper understanding of how data-driven technologies are shaping the social fabric, how they augment human capabilities, and may improve our stewardship of spaceship earth.
What's included
6 videos12 readings4 assignments2 discussion prompts1 plugin
6 videosβ’Total 87 minutes
- 3.1a Can Algorithms Become Humane? (Part 1)β’16 minutes
- 3.1b Can Algorithms Become Humane? (Part 2)β’16 minutes
- 3.2 Algorithms Improving Infrastructuresβ’16 minutes
- 3.3 Machine Learning for Achieving SDGs: An Ecosystem Monitoring Case β’11 minutes
- 3.4 Computational Social Scienceβ’16 minutes
- 3.5 Computer Science for All, and as an Educational Endeavorβ’12 minutes
12 readingsβ’Total 119 minutes
- How AI can be used as a source for goodβ’7 minutes
- Myths, mis- and preconceptions of artificial intelligence: A review of the literatureβ’16 minutes
- 3.1 Further Reading and Resourcesβ’0 minutes
- Algorithmic Game Theory: Introduction and Examplesβ’20 minutes
- 3.2 Further Reading and Resourcesβ’0 minutes
- Understanding Machine Learningβ’20 minutes
- 3.3 Further Reading and Resourcesβ’0 minutes
- Computational Social Scienceβ’6 minutes
- Manifesto of Computational Social Scienceβ’25 minutes
- Seymour Papert- Father of Educational Computingβ’6 minutes
- Developing Computational Thinking in Compulsory Education- Implications for policy and practiceβ’8 minutes
- Relations between mathematics and programming in school: juxtaposing three different casesβ’11 minutes
4 assignmentsβ’Total 51 minutes
- 3.2 Quizβ’12 minutes
- 3.3 Quizβ’18 minutes
- 3.4 Quizβ’9 minutes
- 3.5 Quizβ’12 minutes
2 discussion promptsβ’Total 35 minutes
- Are you scared or hopeful?β’15 minutes
- Data literacy: importance, necessary actions, and challengesβ’20 minutes
1 pluginβ’Total 3 minutes
- Video: Counting Trees in Africa with Computer Scienceβ’3 minutes
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Reviewed on Jun 10, 2026
This is a great learning platform, you can learn at your own pase, moreover, the information is very rich yet presented in a simple manner that can be easily understand
Reviewed on Apr 8, 2026
The module were easy and understandable for something that may help us in future and be aware of what is good and bad.
Reviewed on Mar 30, 2025
The resources are so useful and easy to digest. Also for a person without big screen device I was able to get reading resources on the parts where my device couldn't open videos.
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