Coursebooks

Data science for business

MGT-432

Lecturer(s) :

Younge Kenneth

Language:

English

Withdrawal

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

Remarque

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

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

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:

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:

Transversal skills

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

In the programs

    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2020-2021, Master semester 1
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2020-2021, Master semester 3
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      100
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10BC03    
10-11    
11-12    
12-13     
13-14    BC03
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German