PROJ0021-1

Duration

5h Th, 120h Proj.

Number of credits

	Master of Science (MSc) in Data Science	5 crédits
	Master of Science (MSc) in Data Science and Engineering	5 crédits

Lecturer

Christophe Debruyne, Maxime Fays, Pierre Geurts, Gilles Louppe

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The purpose of this course is for the students to apply and integrate the knowledge and skills acquired throughout the data science program to a project involving actual data in a realistic setting. During the project, the students will engage in the entire process of solving a real-world data science problem: formalizing the problem, collecting and processing data, applying appropriate analytical methods and algorithms, deploying a solution, and presenting the results of their study.

Learning outcomes of the learning unit

The project aims at developing the students' ability to carry out a realistic, complex, and incompletely defined data science project from the conceptual to the operational phase.

The students will furthermore hone project management skills. These skills include project and team leadership, reporting, oral presentations, and question answering.

Prerequisite knowledge and skills

Students are expected to have previously followed

INFO0009 Databases
ELEN0062 Introduction to Machine Learning
DATS0001 Foundations of Data Science

(or equivalent if the student comes from another institution).

Planned learning activities and teaching methods

Students will acquire and develop competencies through a substantial project that is representative of the responsibilities of a data scientist. Next to the project, the following activities are planned to support and evaluate the process and deliverables:

Introductory lectures by the instructors
Project reviews in February, March and April, including oral presentations and short reports;
Feedback on technical progress and project management;
Writing of a final report;
Defense of the project in May.

Mode of delivery (face to face, distance learning, hybrid learning)

Blended learning

Additional information:

Monthly review meetings;
The project is mainly carried out remotely.

Recommended or required readings

Depending on the nature of the project and the student's needs, references to scientific articles, technical reports, and support material will be shared on eCampus.

Exam(s) in session

Any session

- In-person

oral exam

Written work / report

Continuous assessment

Additional information:

The evaluation will be based on:

the intermediate review meetings (progress achieved, quality of project management)
the final report, the final oral defense, and the overall solution where the solution's originality, methodology, clarity, reproducibility, and technological choices will be mainly assessed.

The weights of the various presentations and deliverables will be communicated at the start of the semester.

The project defense consists of an oral presentation, followed by a question/answer session. The final grade takes account of the amount and quality of the completed work, the written report and the oral presentation, as well as the relevance of the provided answers.

Students will be awarded a grade for the overall project which will be adjusted to each student's performance that can deviate at most 2 points from that grade (unless a student clearly underperforms or outperforms).

Intermediate review meetings are a mandatory learning activity. Failure to attend those will result in an absence note for both the first and second sessions.

In case of failure in June, a resit is possible. Students may improve their projects based on a requirements sheet. Students will have to submit an updated report and a description of the improvements and individual contributions. A defense will be organized in August/September.

Work placement(s)

Teams of up to 4 students.
Presence at the intermediate reviews is mandatory.
The final report must be submitted by mid-May.
The defenses will be scheduled in mid-May.
Intermediate deadlines will be announced throughout the year.

Data science project