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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 DL (3587)
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5.0 stptn |
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Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Data Science - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- 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 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 |
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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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Distance learning ✔
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Project ✔
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Small group session ✔
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Semester 2 (4,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 100 % |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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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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| 1st year Master Bioinformatics - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 1st year Master Quantitative Epidemiology - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- 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 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 | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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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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Distance learning ✔
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Project ✔
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Small group session ✔
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Semester 2 (4,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 100 % |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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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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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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