From Data to Decisions (9771) |
| Credits: 3,0 | | Study load hours: 81 | Period: semester 1 (3sp)  |
| Language of instruction: English | | Exam contract: not possible |
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Advising sequentiality bound on the level of programme components
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Following programme components are advised to also be included in your study programme up till now.
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Concepts of Probability and Statistics (9767)
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6.0 stptn | |
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Data Management (9769)
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3.0 stptn | |
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Programming in R (4406)
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3.0 stptn | |
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The student has knowledge of R and concepts of statistics and probability.
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This course aims to integrate knowledge and skills acquired in other courses (Concepts of Probability and Statistics, Data Management and Programming in R). It takes the form of a group project assignment. No regular lectures are given, but rather a few seminars are organised. No new statistical theory is provided by the seminars, but rather skills that are helpful for bringing the group project assignment to a good end. Apart from data exploration andanalysis skills, the course also focuses on collaborative skills, reporting and scientific integrity.
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| Compulsory course material |
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All course materialls will be available on Blackboard. |
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| Recommended course material |
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Course material related to the courses Concepts of Probability and Statistics, Linear Models, and Programming in R. |
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| Mandatory software |
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Collective feedback moment ✔
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Lecture ✔
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Project ✔
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Response lecture ✔
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Semester 1 (3,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 50 % |
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| Transfer of partial marks within the academic year | Yes, with condition |
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| Conditions transfer of partial marks within the academic year | Participation in the group work. Having a pass mark for the report. |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | The student may use all course materials and her/his report, presentation and notes. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | The student must participate in all parts (i.e. group work/peer review, paper and oral exam) of the evaluation. The student should pass: paper and oral exam. Participation in the group work is taken into account in the score of the paper. |
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| Consequences | If the student fails for the paper or/and the oral exam, the final mark will by the minimum of
- 9
- the total (weighted) score of all evaluation components. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | If the student received a pass mark for the paper, then the student only
needs to redo the oral exam. If the student did not pass the paper, the
student will get a new project assignment that she/he needs to do in
group (or individually if no other student has to retake the course).
The student will also have to redo the oral exam. |
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Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Statistics and Data Science
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- 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 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.
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| 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 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 correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.
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| The student is able to efficiently acquire, store and process data. | | | - 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. | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | | | - DC
| The student acts according to societal and ethical standards in general and particularly within the fields of statistics and data science. | | | - DC
| The student can apply basic principles regarding ethics and integrity to the fields of statistics and data science. | - EC
| The student routinely monitors his/her own learning process and adjusts and improves it accordingly. |
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| Included in these programmes | Tolerance3 |
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Y
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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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