| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Specification sequentiality Master's thesis/Bachelor's thesis/internship
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| Degree programme | | Study hours | Credits | P2 SBU | P3 SBU | P3 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Bioinformatics | Compulsory | 648 | 24,0 | 162 | 486 | 24,0 | Yes | No | Numerical |  |
| 2nd year Master Bioinformatics - icp | Compulsory | 648 | 24,0 | 162 | 486 | 24,0 | Yes | No | Numerical | |
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| | | 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 design methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the international nature of the field of statistical science and data science. | - 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 | | | - DC
| The student can reflect on societal tendencies, particularly within the programme specialization. | - 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 can explain basic principles regarding ethics and integrity in general. | | | - DC
| The student can apply basic principles regarding ethics and integrity to the fields of statistics and data science. | | | - DC
| The student can explain ethical issues and dilemmas within the fields of statistics and data science. | | | - 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 respects the privacy of data, people and organizations with whom he/she comes into direct or indirect contact. | - 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 writer, 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 oral communicator, both within their own field as well as across disciplines. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can identify relevant stakeholders and their interests, particularly within the programme specialization. | | | - DC
| The student can respond to the interests of relevant stakeholders, particularly within the programme specialisation. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. | | | - DC
| The student can, when building an argumentation, consider different perspectives and interests. | | | - DC
| The student can explain the consequences of his/her work for relevant stakeholders. | - 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 correctly use the theory methodologically, thus contributing to scientific research within the field of application. | | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.
| | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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On-site project work: during at least a two-month period, the student undertakes an internship at a company, research institute, university, or governmental institute. Depending on the content of the project and the preferences of the eventual external partner, the student may be required to spend some time at the external partner's premises.
An internal supervisor (Hasselt University or visiting faculty) is assigned, together with an external supervisor (responsible for the student at the project place).
The project should be representative for the work done by an applied statistician in a real-life environment.
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Period 3 Credits 24,00
| Evaluation method | |
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| Other exam | 100 % |
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| Other | Master thesis defence: Students are evaluated on their thesis text (report), the oral presentation and the replies to the question of the jury. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | Students are required to present their progress on the master thesis during a midterm presentation and feedback session.
Regarding the final thesis defence: students should score at least 30% on each of the evaluation components (thesis text, presentation, Q&A) of the master thesis. The final score is based on the average of the score of the thesis text, the score on the presentation and the score on the Q&A |
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| Consequences | Students that have not presented their progress on the master thesis during the midterm presentation and feedback session, will not be allowed to defend their master thesis in the June/July exam period.
Students that have not scored 30% or more on each of the evaluation components (thesis text, presentation, Q&A) of the master thesis, will receive a maximum score of 9 on 20 for their master thesis. |
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| Additional information | The subject of the master thesis is only valid for one academic year and will hence expire if the student does not pass the master thesis during that academic year (retake exam period included), unless the Examination Board decides otherwise.
Students can find more information about the thesis regulations on Blackboard. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Depends on the specific project. |
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| Exchange Programme Statistics | Optional | 648 | 24,0 | 162 | 486 | 24,0 | Yes | Yes | Numerical | |
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On-site project work: during at least a two-month period, the student undertakes an internship at a company, research institute, university, or governmental institute. Depending on the content of the project and the preferences of the eventual external partner, the student may be required to spend some time at the external partner's premises.
An internal supervisor (Hasselt University or visiting faculty) is assigned, together with an external supervisor (responsible for the student at the project place).
The project should be representative for the work done by an applied statistician in a real-life environment.
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Period 3 Credits 24,00
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | Students are required to present their progress on the master thesis during a midterm presentation and feedback session.
Students should score at least 30% on each of the evaluation components (thesis text, presentation, Q&A) of the master thesis. |
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| Consequences | Students that have not presented their progress on the master thesis during the midterm presentation and feedback session, will not be allowed to defend their master thesis in the June/July exam period.
Students that have not scored 30% or more on each of the evaluation components (thesis text, presentation, Q&A) of the master thesis, will receive a maximum score of 9 on 20 for their master thesis. |
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| Additional information | The subject of the master thesis is only valid for one academic year and will hence expire if the student does not pass the master thesis during that academic year (retake exam period included), unless the Examination Board decides otherwise.
Students can find more information about the thesis regulations on Blackboard. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Depends on the specific project. |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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