Fiches de cours

Data science for business

MGT-432

Enseignant(s) :

Younge Kenneth

Langue:

English

Withdrawal

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

Remarque

only for MA3

Summary

The course introduces students to the methods and tools used by data scientists to model prediction problems for business. Students will also learn how to apply these concepts to real programming problems.

Content

This course introduces students to some of the programming tools used by data scientists to address real world business analytics problems. Accordingly, the course objectives are three fold: (1) to develop an understanding of how Data Science methods can support decision making in business environments; (2) to gain familiarity with how Data Science tools function through experience in addressing real-word problems and programming real-world solutions; (3) to evaluate the strengths and weaknesses of alternative approaches. The course is particularly applicable for students interested in working for, or learning about, data-driven companies.

Keywords

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

Learning Prerequisites

Required courses

Prior to the start of class, all students must complete a comprehensive course in statistics covering descriptive statistics, analysis of variance, and the OLS linear regression model. Additionally, students must have prior experience with at least one programming language, and familiarize themselves with the Python 3 programming language.

Recommended courses

It is strongly recommended that students familiarize themselves with the syntax and data structures of the Python programming language before the start of class. There are numerous online MOOCs and/or tutorials that can serve this need. It also is strongly recommended that students take a masters-level statistics course, over-and-above the required foundational course in statistics, before the start of class.

Learning Outcomes

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

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. Exams will be given during regularly scheduled class hours.

 

Assessment methods

50% Individual Assignments  -  Five assignments at 10% each

25% Semester Project  -  Group Project completed in teams

25% Final Exam  -  Written exam administered during final class period

Resources

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
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • 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
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • Cours
      3 Heure(s) hebdo x 14 semaines
    • TP
      1 Heure(s) hebdo x 14 semaines
  • Gestion de l'énergie et durabilité, 2019-2020, Master semestre 1
    • Semestre
       Automne
    • Forme de l'examen
       Pendant le semestre
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • Cours
      3 Heure(s) hebdo x 14 semaines
    • TP
      1 Heure(s) hebdo x 14 semaines
  • Gestion de l'énergie et durabilité, 2019-2020, Master semestre 3
    • Semestre
       Automne
    • Forme de l'examen
       Pendant le semestre
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • 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
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • 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
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • 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
    • Crédits
      6
    • Matière examinée
      Data science for business
    • Nombre de places
      50
    • Cours
      3 Heure(s) hebdo x 14 semaines
    • TP
      1 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10BC03    
10-11    
11-12   CHB331
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Cours
      Exercice, TP
      Projet, autre

légende

  • Semestre d'automne
  • Session d'hiver
  • Semestre de printemps
  • Session d'été
  • Cours en français
  • Cours en anglais
  • Cours en allemand