Coursebooks

Data science for business

MGT-432

Lecturer(s) :

Younge Kenneth

Language:

English

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.

In the programs

    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      50
    • 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
      50
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2019-2020, Master semester 1
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      50
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2019-2020, Master semester 3
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      6
    • Subject examined
      Data science for business
    • Number of places
      50
    • 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
      50
    • 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
      50
    • 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
      50
    • 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   CHB331
12-13     
13-14     
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