6 ECTS credits
160 h study time

Offer 1 with catalog number 4023167ENR for all students in the 2nd semester at a (E) Master - advanced level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Social Sciences & SolvayBusinessSchool
Department
Business Technology and Operations
Educational team
Sam Verboven (course titular)
Emilie Grégoire
Activities and contact hours
24 contact hours Lecture
16 contact hours Seminar, Exercises or Practicals
120 contact hours Independent or External Form of Study
Course Content

The course teaches the fundamental principles of data science. Students are taught “data-analytic thinking” to extract valuable knowledge and value from data using modern data science techniques. With this knowledge, the student can select and apply an appropriate data science solution to various business problems.
During the lectures, the basic principles of data science are introduced: the data mining process, data preparation, unsupervised learning, supervised learning and evaluation. Furthermore, building upon these basic principles, advanced concepts from the state of the art in ‘Deep Learning’, ‘AI Ethics’ and ‘Causality’ are introduced. Should expert guest lecturers be invited, attendance is mandatory for these lectures.
During the practice sessions, the students will be taught how to use data science to tackle real business problems using Python. Furthermore, the students will make assignments where they develop their own data science solutions. These assignments are designed to test the students’ data-analytic thinking and aptitude to solve business problems using the data science fundamentals covered in class.

Course material
Handbook (Required) : Data Science for Business, What You Need to Know about Data Mining and Data-Analytic Thinking, Provost, Foster and Fawcett, Tom, Latest Edition, O'Reilly Media, 9781449361327, 2013
Course text (Required) : Selection of papers that is updated every year
Handbook (Recommended) : Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, Concepts, Tools, and Techniques to Build Intelligent Systems, Géron, Aurélien, 2nd, O'Reilly Media, 9781492032649, 2019
Additional info

Attentance is mandatory for guest lectures.

For possible company visits attendance is mandatory. Should such a visit take place, further information will be distributed via Canvas.

Learning Outcomes

Algemene Competenties

  • The student has insight into how a company can extract value from data;
  • The student can explain basic techniques in unsupervised and supervised learning;
  • The student can analyze a business problem and develop an effective data science solution in Python;
  • The student can compare the strengths and weaknesses of modern techniques in data science.

Grading

The final grade is composed based on the following categories:
Oral Exam determines 75% of the final mark.
Other Exam determines 25% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

  • Oral Exam with a relative weight of 100 which comprises 75% of the final mark.

    Note: oral exam about the overall course material (all lectures and WPOs)

Within the Other Exam category, the following assignments need to be completed:

  • Assignments with a relative weight of 100 which comprises 25% of the final mark.

    Note: hands-on assignments, putting into practice the fundamentals of data science

Additional info regarding evaluation

Evaluation is through the student assignments and an oral exam. Depending on the number of students taking this course, the assignments may be individual or group projects. The oral exam covers the material seen during the lectures, self-study and the exercise sessions. The oral exam may feature multiple choice questions, open questions, exercises, questions about the assignments, etc.
 
Partial results of the assignments can optionally be transferred to a subsequent exam period only within the same academic year. Transfer of oral exam results is not possible.

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Business Engineering: Standaard traject (only offered in Dutch)
Master of International Business: Standaard traject
Master of Business Engineering: Business and Technology: Standaard traject