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Informatie over Minor: Data Wise: data science in society
Hieronder staan het programma en de vakomschrijvingen van Minor: Data Wise: data science in society Klik op de naam van een vak in een schema om naar de omschrijving te gaan.
| » Jaar 3 | |||||||
| Periode | Type | Code | Naam | Taal | ECTS | Uren | |
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| semester I | verplicht | SOMINDW02 | Collaborative Data Project | Engels | 12.5 | variabel | |
| verplicht | SOMINDW03 | Dynamics of multi-disciplinary teamwork | Engels | 2.5 | 2 | ||
| semester I a | verplicht | SOMINDW01 | Introduction to Data | Engels | 7.5 | 7 | |
| keuze | SOMINDW04 | Data as evidence | Engels | 2.5 | 4 | ||
| keuze | SOMINDW05 | Introduction to Programming | Engels | 2.5 | 8 | ||
| keuze | SOMINDW06 | Opinion dynamics on the internet | Engels | 2.5 | 2 | ||
| keuze | SOMINDW07 | Machine learning | Engels | 2.5 | 4 | ||
| keuze | SOMINDW08 | Data in Practice | Engels | 2.5 | variabel | ||
| keuze | SOMINDW09 | Data visualization | Engels | 2.5 | 2 | ||
| 1 | Collaborative Data Project | SOMINDW02 | ||||||||||||||||||||||||
| The Collaborative Data Project is central to the minor Data Wise. The projects are “real” projects proposed by external parties. For instance, a data science project proposed by the Policy, Gemeente Groningen, or a company. One company that has already committed to pursuing a project with students from the minor is “Trust the Source”; a company that tries to develop a program for journalists in which they can check the likelihood of particular information to be “fake news”. Many more options for projects will be presented in the first weeks of the Minor. In each project, students work with data provided by the external partner in order to answer a complex question or solve a practical problem. Multidisciplinary teams of around 5 students will work on this project for 16 weeks. Students collaborate intensively, regularly reporting to the project supervisor and to the external partner. Students will use the knowledge obtained in the general course "Introduction to Data" to address the role of data in society, techniques of data science and data infrastructure, legal and ethical issues, and principles of responsible data management in the practical context of their project. The intensive teamwork in this project will be supported by the course Dynamics of Multi-disciplinary Teams. Additional skills that students/groups need to fulfill the project can be gained through the elective courses. | ||||||||||||||||||||||||||
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| 2 | Data as evidence | SOMINDW04 | ||||||||||||||||||||||||
| In this course, we will focus on the recent changes in how we use data as evidence. We will analyze the recent growth of types and amount of data (datafication) and the different ways data can be used as evidence. By the end of the course, you will master the following concepts: causation, correlation, description, features, probability, sampling, model, population. You will also learn to analyze data cycles and to map out knowledge production systems. A number of current issues will also be examined from the perspective of data: fake news, bubbles, algorithmic discrimination. Through the combination of conceptual tools and practical work, you will be well equipped to assess data sets and to address what you can and cannot do with them. | ||||||||||||||||||||||||||
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| 3 | Data in Practice | SOMINDW08 | ||||||||||||||||||||||||
| Excellent (data) science starts with excellent planning. In this course, you will learn why data management is an important activity in all stages of Data Science: from its design phase, to the end of a project, and thereafter when preserving and sharing the data. You will learn about Open Science, how you can contribute to future innovations by creating data that are Findable, openly Accessible, Interoperable and Reusable (FAIR), and how to write a Data Management Plan. You will experience possible issues and challenges of data handling in practice by weekly hands-on assignments. The course focuses on knowledge and skills for which demand is rapidly emerging inside and outside academia, while training is still scarce. It will therefore give students a unique asset. | ||||||||||||||||||||||||||
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| 4 | Data visualization | SOMINDW09 | ||||||||||||||||||||||||
| In this course, you’ll learn how to effectively and beautifully visualize your data and communicate your results. Through lectures and practicals you learn about common types of visualization, their pros and cons, and how to create them yourself. The course starts with visualizing single variables and end with more complex visualizations like reactive graphs and dashboards. You’ll also learn how to work with datasets and transform your data in such a way that you’ll be able to make any visualization you’d like. You’ll learn that visualizations are an ideal way to assess data quality. While making these visualizations, you’ll be gently exposed to programming, and already after the first lecture you will be able to program your first graphs. At the end of the course, you will be able to make much better visualizations than is common in science currently. | ||||||||||||||||||||||||||
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| 5 | Dynamics of multi-disciplinary teamwork | SOMINDW03 | ||||||||||||||||||||||||
| Working in multi-disciplinary teams is a crucial part of the Collaborative Data Project that is at the core of the minor. This course provides you with conceptual frameworks and skills that will also be applicable in your future career. Teamwork that is more effective will result in improved quality of your data project and future endeavours. In the course, we will learn about multi-disciplinarity and related concepts (inter/cross/mono-disciplinarity). We will also learn about what multi-disciplinary collaboration entails and how to pursue it successfully. We will address different types of collaboration and of expertise and skills needed in pursuing a data project. We will reflect on how these relate to your team and specific project in the minor. In terms of skills, you will learn to identify and solve common issues around • Building and maintaining support • Assigning team roles • Creating accountability, trust and inclusion in the team • Managing accountability to and interaction with stakeholders, clients and users • Setting up communication and decision-making in a team • Applying project management techniques | ||||||||||||||||||||||||||
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| 6 | Introduction to Data | SOMINDW01 | ||||||||||||||||||||||||
| The main aim of this course is to get students from diverse backgrounds “on the same page” and to have similar levels of knowledge by introducing them to fundamental concepts surrounding data. At the end of the course, the students will be ready to start their projects and to interact with students from different disciplines in terms of data. In the first four weeks, students will have daily meetings on diverse topics covered in different blocks (several days dedicated to one topic). Block 1: panel-discussion on diverse research into data, data science, and big data that is done at this university and beyond. Block 2: Dynamics of data in a digital society, in which students learn about the prominent role data plays in society, datafication, and data cycles. It also addresses the sociology of technology use. Block 3: Data management, in which students learn how to store data in such a way that in confirms to legal requirements, prevents person identification, but is also findable and usable for others. Block 4: Legal & ethics, in which students get acquainted with rules and regulations surrounding data use and the ethical questions that surround it. Block 5: Data science techniques, in which students get acquainted with the different forms of data that exist and standard tools of data science (programming, visualization, machine learning). Block 6: Business challenges and opportunities, in which students learn about the ways that big data are used in business and innovation. Block 7: Infrastructure, in which students learn what resources (e.g., people, software, hardware) are required to safely and efficiently store and use data. | ||||||||||||||||||||||||||
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| 7 | Introduction to Programming | SOMINDW05 | ||||||||||||||||||||||||
| During the course you will learn in Python 3 the basic building blocks of programming, like conditions, recursion, loops, various data objects and importing and exporting simple data. | ||||||||||||||||||||||||||
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| 8 | Machine learning | SOMINDW07 | ||||||||||||||||||||||||
| This course will provide the theoretical and practical basis for running machine learning experiments in a variety of fields and tasks, where one requires data manipulation towards making predictions. The students will be exposed to both theory and tools, and will experiment with actual datasets. This will make them acquainted with the settings of machine learning experiments, with data manipulation, with experimental choices and most importantly with evaluation and analysis of results. | ||||||||||||||||||||||||||
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| 9 | Opinion dynamics on the internet | SOMINDW06 | ||||||||||||||||||||||||
| The Internet has become an arena for public debate, providing users with unprecedented means of communicating their opinions via online fora, tweets, Facebook posts, and the like. Many fear that this new technology changes public debate in ways that endanger societal cohesion and democracy, pointing to phenomena like filter bubbles or fake news. This course covers the computational social science approach to this topic, highlighting the opportunities and challenges that come with learning about human behavior in an increasingly data driven society. Specifically, we discuss theories and empirical research on opinion dynamics on the Internet, and focus on computational models of opinion dynamics in networks and their application to online (social media) platforms. We discuss how social influence on the Internet can be studied empirically with experiments and the analysis of digital trace data, but stress the importance of theoretically well-informed models when doing so. | ||||||||||||||||||||||||||
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