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Bachelor's degree Économie et gestionUE Computer science

Content

Goals

This course aims at introducing students without Computer Science background to programming, algorithms, data structures and Economics-oriented Big Data. The programming language employed during these lectures is Python.

Course outline

• Programming 101 o The “why this ?” question o Understanding concepts : algorithms, algorithmics, programming language, program vs. algorithm o Python o Variables o Operators o Printing o Input

• Python2 o Reading from files o Writing to files o Lists

• Python3 o Lit comprehension o Loops o Comparisons

• Python4 o Tests : definition, types, multiple tests o Modules : Import, help, sys, os

• Python5 o Dictionaries and tuples o Functions : principles, definitions, argument passing o numpy : presentation, useful functions

Professional skills

  • Understand the usefulness of programming
  • Understand the algorithmic thinking
  • Learn how to build an algorithm
  • Learn python programming
  • Python tricks for data science (Big Data)
  • Learn how to self-improve the programming/development skill set

Language used

Main language used by this course: Français.

Bibliography

  • « Introduction to Algorithms » (1989) by Thomas H. Cormen, Charles E. Leiserson, Roland L. Rivest and Clifford Stein.
  • « The Algorithm Design Manual » (1997) by Steven S. Skiena
  • « Think Python » (2012) by Allen B. Downey
  • « Learning Data Mining with Python » (2015) by Robert Layton

Recommended prerequisites

  • Basic notions of programming and algorithms
  • Basic knowledge of operating systems (Windows, Linux)
  • Data structures
  • Another programming language (R, C, Java, etc.)

Structure and organisation

  • Interactive lectures with broad discussions on examples
  • Exercises
  • Small projects related to economics
  • Evaluation : written exam (70%) and projects (30%)

Volume of teachings

  • Lectures: 8 hours
  • Tutorials: 16 hours

The trainings which use this course