Coursebooks 2017-2018

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Data science for business

MGT-432

Lecturer(s) :

Younge Kenneth

Language:

English

Remarque

only for MA3

Summary

Students will learn core concepts from the field of Data Science that managers can use to make better business decisions. Students will also learn how to apply those 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.  

Recommended courses

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

Learning Outcomes

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

Transversal skills

Teaching methods

Weekly lectures, problem sets, and exercises.

Expected student activities

Attending class regularly to both acquire content and to review problem sets and exercises. Quizzes will be given during regularly scheduled class hours.

 

Assessment methods

Supervision

Office hours Yes
Assistants Yes
Forum No

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

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10DIA 004    
10-11    
11-12    
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

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