Visualisation in Data Science (4142) |
| Language of instruction : English |
| Credits: 4,0 | | | | | Period: semester 2 (4sp) | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | Exam contract: not possible |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Programming in Python (3306)
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5.0 stptn |
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As data becomes easier and cheaper to generate, we are moving from a hypothesis-driven to data-driven paradigm in scientific research. As a result, we don't only need to find ways to answer any questions we have, but also to identify interesting questions/hypotheses in that data in the first place. In other words: we need to be able to dig through these large and complex datasets in search for unexpected patterns that - once discovered - can be investigated further using regular statistics and machine learning. Interactive data visualization provides a methodology for just that: to allow the user (be they domain expert or lay user) to find those questions, and to give them deep insight in their data. Content - Background and context of data visualization and visual data analysis
- Design as a process: framing the problem, ideation, sketching, design critique, ...
- Programming visualizations: static and dynamic
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Lecture ✔
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Project ✔
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Small group session ✔
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Period 2 Credits 4,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 100 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Recommended reading |
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Visualization Analysis and Design,Tamara Munzner,A K Peters,Much of the course material is taken from this book
Making Data Visual,Danyel Fischer & Miriah Meyer,O'Reilly,Very good introductory text
Gamestorming: A Playbook for Innovators, Rulebreakers, and Changemakers,Dave Gray, Sunni Brown, James Macanufo,O'Reilly |
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Learning outcomes Master of Statistics and Data Science
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- EC
| The student can work in a multidisciplinary, intercultural, and international team. | | | - DC
| The student is able to extract user tasks from domain experts. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | - EC
| The student knows the societal relevance of statistics and data science. | | | - DC
| The student can reflect on and explain the societal relevance of a task, particularly within the programme specialization |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
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1st year Master Bioinformatics
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J
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1st year Master Biostatistics
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J
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1st year Master Data Science
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J
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1st year Quantitative Epidemiology
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J
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exchange bachelor informatica K
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J
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exchange master informatica K
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J
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Exchange Programme Biology
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J
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Exchange Programme Mathematics
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J
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Exchange Programme Physics
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J
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Exchange Programme Statistics
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J
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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