Inference for Statistics and Data Science DL (4581) |
| Credits: 3,0 | | Study load hours: 81 | Period: semester 1 (3sp)  |
| Language of instruction: English | | Exam contract: not possible |
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The student knows the basics of statistical inference and probability and linear models.
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In this course students will learn about some more advanced and state-of-the art statistical inference issues and techniques that are relevant for modern applications that go beyond the scope of traditional statistical methods:
- prediction versus association
- observational versus experimental studies
- basics of causal inference and causal machine learning.
Examples (with R code) will also be discussed.
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| Compulsory course material |
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Course notes or slides will be made available on Blackboard. |
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Distance learning ✔
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Project ✔
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Semester 1 (3,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 60 % |
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| Transfer of partial marks within the academic year | Yes, no resit exam |
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| Written exam | 40 % |
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| Transfer of partial marks within the academic year | Yes, no resit exam |
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| Multiple-choice questions | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | All course materials and own notations may be used. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | To get a pass mark, the student must pass for both the evaluation during the teaching period and the evaluation during the exam period. |
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| Consequences | If the condition is not met, the final mark will by the minimum of: - 9 - the total 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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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 design methodology. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - 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 is capable of acquiring new knowledge. | - 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 explain ethical issues and dilemmas within the fields of statistics and data science. | - EC
| The student knows the international nature of the field of statistical science 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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second year Data Science - distance learning
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Y
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second year Master Bioinformatics - distance learning
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Y
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second year Master Biostatistics - distance learning
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Y
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second year Quantitative Epidemiology - distance learning
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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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