| Language of instruction : English |
| | | Exam contract: not possible |
|
Sequentiality
|
| |
|
No sequentiality
|
There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Data Science - distance learning | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical |  |
|
| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is able to efficiently acquire, store and process data. | | | - DC
| ... selecting and using the best data management options | | | - DC
| ...maintain provenance of data, analyses and results | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective oral communicator in their own field. | - EC
| The student can put research and consulting aspects of one or more statistical fields into practice. | | | - DC
| The student can put the research aspects of one or more statistical fields into practice.
| - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to extract new knowledge and insights from datasets in the application domain. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
This course aims to integrate knowledge and skills acquired in other courses (data management, programming courses, statistics and data visualisation). It takes the form of a group project assignment. No regular lectures are given, but rather a few seminars are organised. No new theory is provided by the seminars, but rather skills that are helpful for bringing the project assignment to a good end. With the project, students learn to integrate the different aspects of data science.
|
|
|
|
|
|
|
|
|
Collective feedback moment ✔
|
|
|
|
Project ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 75 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
| Oral evaluation during teaching period | 25 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | The student may use all course materials and her/his report, presentation and notes. |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The student must pass (1) for the total of the paper and the self-reflection assignment, and (2) for the oral exam. |
|
|
|
| Consequences | If the student fails for one of these two components, the final mark will by the minimum of
- 9
- the total score of all evaluation components. |
|
|
|
|
|
| Compulsory course material |
|
|
|
|
|
1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
|
| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
|