MGT-432 / 6 crédits

Enseignant: Younge Kenneth

Langue: Anglais

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Remark: MA3 only


Summary

Students will learn the basic concepts of Data Science so that they can make better business decisions. Students will also learn how to apply these concepts to real programming problems.

Content

Keywords

Data science; data analysis; business analytics; python; data-driven management

Learning Prerequisites

Required courses

All students must have the following prerequisites:

Statistics: Prior to taking this course, all students must complete at least one course in statistics. You should have a basic understanding of descriptive statistics, the OLS linear regression model, and multiple regression.

General Programming: Prior to taking this course, all students must complete at least one course in a general computer programming language.

Python Programming: Prior to taking this course, all students must know (or quickly familiarize themselves with) the syntax and data structures of the Python 3 programming language. There are numerous online tutorials and short courses for learning Python. We recommend one of the following options:

  • The 7-hour Python tutorial at Kaggle: https://www.kaggle.com/learn/python. This course provides a fast overview of Python, but it does assume basic knowledge of computing and programming languages. If this course is too advances or moves too quickly, then you should select the next option.

     

  • The JetBrains Academy track on Python: https://hi.hyperskill.org  This is the best approach for an absolute beginner and it comes in interactive, bite-sized exercises. It also helps you track your progression of study with:  https://hyperskill.org/knowledge-map  However, this option may take up to 34 hours to complete.

     

If you do not know Python, and you cannot complete one of the two tutorials above by the end of the second week of class, then you should delay in taking this course until you have the necessary skills.

Important concepts to start the course

Descriptive statistics

The OLS linear regression model

Multiple regression

Learning Outcomes

By the end of the course, the student must be able to:

  • Formulate prediction models
  • Assess / Evaluate the performance of prediction models
  • Describe their findings to others

Transversal skills

  • Access and evaluate appropriate sources of information.
  • Take feedback (critique) and respond in an appropriate manner.
  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Assess one's own level of skill acquisition, and plan their on-going learning goals.
  • Assess progress against the plan, and adapt the plan as appropriate.
  • Collect data.

Teaching methods

Weekly lectures, demonstrations, assignments, and exercises.

Expected student activities

Attending class regularly to both acquire content and to review problem sets and exercises. Take home, open-book exams will be given on the day of regularly scheduled class

 

Assessment methods

100 points       Qualifying Exam

500 points       Assignments

200 points       Business Use Case

200 points       Take home Final Exam

 

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Textbook: "Data Science for Business" by Provost & Fawcett. (2013) Publisher: O'Reilly Media; ASIN: B017PNWLKQ


A list of additional readings will be distributed at the beginning of the course.

Ressources en bibliothèque

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science for business
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • TP: 1 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22