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Eléments de programmation

  • Teacher(s):   M.Cherubini   T.Estier   A.Métrailler  
  • English title: Programming elements
  • Course given in: French
  • ECTS Credits: 4.5 credits
  • Schedule: Spring Semester 2020-2021, 2.0h. course + 2.0h exercices (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Bachelor (BSc) in Economic Sciences

    Bachelor of Science (BSc) in Economics

    Bachelor of Science (BSc) in Management

[warning] This course syllabus is currently edited by the professor in charge. Please come back in a few days. --- For your information only, here is the old syllabus :


The goal of this course is to provide students with knowledge and expertise on the semantics and syntax of a programming language. One specific aim of this course is to provide students basic techniques and expertise to create interactive scripts with which they will be able to analyze large amount of data, create visualization and implement algorithms to perform advanced analytical analyses.

The course will develop three separate axes: the first axis is to provide knowledge and expertise with one particular programming language, Python. The second is to bring students to develop algorithmic thinking skills, a way of getting to a solution through the clear definition of the steps needed. The third is that of providing students the ability to develop their own tool to analyze large volumes of data and provide their own creative solutions to problems they will face.

A core element of this course is the course home assignments. Through incremental home assignments, students will be guided in the development of a tool to analyze a large dataset containing a time series of stock values and make predictions for future values.

Learning outcomes

At the end of the course students should be able to:

  1. Understand basic Python syntax and semantics and being able to produce Python scripts to analyze datasets comprising hundreds of thousands of data points;

  2. Apply algorithmic thinking to problem solving: decomposing a complex problem into simpler chunks;

  3. Create own tool for data analysis: implement analysis technique on actual financial datasets with the aim of informing decision making.


Course structure

Time of the course will be devoted to lecture, guided examples, practice and correction of exercises (in class). Teaching will be organized in short modules followed by practical exercises and correction by the instructor.

Each week students will be assigned a practical work to do at home. The work will be returned using a learning platform provided and graded.

At the end of the course there will be a final written exam consisting of a multiple-response quiz.


All the supporting material will be available on the website of the class (Moodle).

Students are expected to bring their laptop to the class.

The class will share content and examples from the following book:

Swinnen, G (2012). Apprendre à programmer avec Python 3, Eyrolles eds., 3rd edition, (ISBN 978-2-212-13434-6) The book is available under pdf form here.





First attempt

Written 2 hours
Not allowed
Not allowed

Written exam.



Written 2 hours
Not allowed
Not allowed

Written exam.

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