Informatie opleidingsonderdeel

Inference for Statistics and Data Science DL (4581)


Coordinating lecturer :	Prof. dr. Olivier THAS

Co-lecturer :	Prof. dr. Inigo BERMEJO DELGADO

Language of instruction : English

Credits: 3,0

Period: semester 1 (3sp)

2^nd Chance Exam¹: Yes

Final grade²: Numerical


Exam contract: not possible

Sequentiality

Mandatory sequentiality bound on the level of programme components


Following programme components must have been included in your study programme in a previous education period
	Concepts of Probability and Statistics DL (3220)	5.0 stptn
	Linear Models DL (3577)	5.0 stptn

Prerequisites

The student knows the basics of statistical inference and probability and linear models.

Content

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.

Organisational and teaching methods

Organisational methods
	Distance learning ✔
	Project ✔
Teaching methods
	Group work ✔
	Paper ✔

Evaluation

Period 1 Credits 3,00

Evaluation method

Written evaluaton during teaching periode

50 %

Transfer of partial marks within the academic year

✔

Homework

✔

Report

✔

Written exam

30 %

Transfer of partial marks within the academic year

✔

Paper

✔

Take-home assignment

✔

Oral exam

20 %

Transfer of partial marks within the academic year

✔

Debat

✔

Use of study material during evaluation

✔

Explanation (English)

All course materials and own notations may be used.

Evaluation conditions (participation and/or pass)

✔

Conditions

To get a pass mark, the student must pass for each of the following parts: project, paper and oral exam.

Consequences

If the condition is not met, the final mark will by the minimum of: - 9 - the total score of all evaluation components.

Compulsory course material

Course notes or slides will be made available on Blackboard.

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 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.

EC = learning outcomes DC = partial outcomes BC = evaluation criteria

Offered in	Tolerance³
second year Data Science - distance learning	J
second year Master Bioinformatics - distance learning	J
second year Master Biostatistics - distance learning	J
second year 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.