Project: Learning from Data DL (3222) |
| Language of instruction : English |
| Credits: 5,0 | | | | | Period: semester 2 (5sp) | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | Exam contract: not possible |
|
Sequentiality
|
| |
|
Advising sequentiality bound on the level of programme components
|
| |
| |
| |
Following programme components are advised to also be included in your study programme up till now.
|
| |
|
Concepts of Probability and Statistics DL (3220)
|
5.0 stptn |
| |
|
Data Management DL (4431)
|
5.0 stptn |
| |
|
Linear Models DL (3577)
|
5.0 stptn |
| |
|
Programming in R DL (4432)
|
3.0 stptn |
| |
|
|
|
The student has knowledge of R and linear models.
|
|
|
|
This course aims to integrate knowledge and skills acquired in other courses (Concepts of Probability and Statistics, Data Management, Programming in R and Linear Models). 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. Apart from data management and data analysis skills, the course also focuses on collaborative skills, reporting, ethical and societal aspects, and scientific integrity.
This course is organised in the last two weeks of the 1st semester.
|
|
|
|
|
|
|
|
|
Collective feedback moment ✔
|
|
|
|
Distance learning ✔
|
|
|
|
Project ✔
|
|
|
|
|
|
|
|
Discussion/debate ✔
|
|
|
|
Group work ✔
|
|
|
|
Paper ✔
|
|
|
|
Porfolio ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 2 Credits 5,00
| 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 participate in all three parts of the evaluation. The student should pass paper and self-reflection and oral exam. Participation in the group work is taken into account in the score of the paper. |
|
|
|
| Consequences | If the student fails for the paper and self-reflection or/and the oral exam, the final mark will by the minimum of: - 9 - the total score of all evaluation components. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | If the student received a pass mark for the paper and self-reflection,
then the student only needs to redo the oral exam. The scores of the
other aspects will be carried over to the second chance exam.
Otherwise, the student will get a new project assignment
(statistical analysis plan, paper and self-reflection) 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. |
|
|
|
|
|
| Compulsory course material |
| |
All course materialls will be available on Blackboard. The R software will be used in this course. |
|
|
| Recommended course material |
| |
Course material related to the courses Concepts of Probability and Statistics, Linear Models, and Programming in R. |
|
|
Learning outcomes Master of Statistics and Data Science
|
- 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 critically appraise methodology and challenge proposals for and reported results of data analysis. | - 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 can work in a multidisciplinary, intercultural, and international team. | - EC
| 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 oral communicator in their own field. | | | - 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 knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. | - EC
| The student routinely monitors his/her own learning process and adjusts and improves it accordingly. |
|
|
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
|
1st year Master Bioinformatics - distance learning
|
J
|
|
1st year Master Biostatistics - distance learning
|
J
|
|
1st year Master Data Science - distance learning
|
J
|
|
1st year Master Quantitative Epidemiology - distance learning
|
J
|
|
|
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.
|
|