Big data project

Duration

25h Th, 180h Proj.

Number of credits

Lecturer

Bertrand Cornélusse, Pierre Geurts, Gilles Louppe, Gilles Louppe

Language(s) of instruction

English language

Organisation and examination

All year long

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/project is for the students to apply the knowledge acquired in the Data Science and Engineering 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.
The students will work in groups to carry out a practical project over a big dataset, aiming at using the available software and hardware systems for retrieving a specific kind of information from the dataset. The project will be carried out within modern distributed computing and storage environments, using state-of-the-art analytical methods.

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 also learn and practice actively project management, including project and team leadership, reporting, oral presentations and defence, thereby improving their autonomy, their abilities to work efficiently in teams, and their communication and writing skills.

This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, II.3, III.1, III.2, III.3, III.4, IV.1, IV.2, IV.3, IV.4, VI.1, VI.2, VI.3, VI.4, VII.1, VII.2, VII.3, VII.4, VII.5, VII.6 of the MSc in data science and engineering.

Prerequisite knowledge and skills

Planned learning activities and teaching methods

• Regular project reviews, including oral presentations and short reports;
• Feedback on technical progress and project management;
• Writing of a final report;
• Defence of the project.

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

Assessment methods and criteria

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 quality of the final report, the quality of the final oral defence, and the overall solution where the originality, methodology, clarity, reproducibility and technological choices of the solution will be mainly assessed. 

Work placement(s)

Organizational remarks

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

Contacts

Coordinators:

• Gilles Louppe
• Pierre Geurts
• Bertrand Cornélusse