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Data Science in Business Analytics

  • Teacher(s):   T.Vatter  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2018-2019, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Management, Orientation Marketing

    Master of Science (MSc) in Management, Orientation Business Analytics

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

 

Objectives

Upon completion of that course the students will be able to

- Manage and analyze data,

- Develop data products,

- Use data science in a business context.

Contents

The aim of this course is to learn the most important tools to use data science in a business context, and includes concepts from statistics and computer science:

"Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data." – Hadley Wickham

The course will cover the following topics:

1) Explore

  1. Data visualization
  2. Data transformation
  3. Exploratory data analysis

2) Wrangle

  1. Tidy data
  2. Relational data
  3. Strings, factors, dates and times

3) Model

  1. The basis
  2. Model building
  3. Many models

4) Communicate

  1. Literate programming
  2. Graphics for communication

The class will be hands-on and centered around data: bring your laptop to lectures!

References

There will be no mandatory reading. However, the following references will be useful:

Wickham, H., & Grolemund, G. (2016). R for Data Science. O’Reilly Media.

Wickham, H. (2014). Advanced R. Chapman & Hall/CRC The R Series.

Pre-requisites

No prior knowledge of data science is necessary. However, students are assumed to have a firm command of basic statistics and to be comfortable with (or at least interested in) computer programming.

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

There will be four assignments (50%) and a final project (50%). For each assignment as well as the project, students will have to provide detailed written reports. Additionally, for the project, students will give a presentation during the last lecture.

Retake

Exam:
Without exam (cf. terms)  
Evaluation:

The retake consists in asking the failing students to make modifications or additions to their written reports and to present them.



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