Code
MGFE INF 4402
Level
M1
Field
Informatique
Language
Anglais/English
ECTS Credits
1
Class hours
18
Total student load
20
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
Familiarize students with the basics of programming in Python and give them the tools to manipulate, analyse and visualize data.
Learning objectives/Intended learning outcomes
- 6.2 - Optimise the use of tools adapted to different areas of management, and define and interpret relevant KPIs in order to measure and guarantee sustainable value creation for all stakeholders.
- 6.3 - Produce and analyse key summary documents to ensure optimal, sustainable management, ensuring alignment with the organisation's vision, mission and values.
Rubrics
— (DQ15) Content creation and computational literacy: Synthesizing, creating, and producing information, media, and technology in an innovative and creative manner.
— (DQ23) Data and AI literacy: Generating, processing, analyzing, presenting meaningful information from data and developing, using, and applying artificial intelligence (AI) and related algorithmic tools and strategies in order to guide informed, optimized, and contextually relevant decision-making processes.
— (CPS1) Data/information management: Gathering information from various sources to understand a problem; Classifying and categorizing data to identify patterns and relationships, Assessing the quality, relevance, and significance of information, and Applying reasoning, deduction, and induction to make sense of information and reach conclusions.
Content : structure and schedule
Session 1: Introduction to Python
* Notebook and Markdown
* Getting started with Python
Session 2: Data structures in Python
* Collections: lists, tuples, dictionaries and sets
* Pandas dataframes
Session 3: Python control flow
* Loops, Conditions, Functions
Section 4: Data analysis with Python
* Practical work (TD) with real world data
Sustainable Development Goals
Ce cours contribue à l'ODD 4 en dotant les étudiants de compétences numériques avancées et de capacités d'analyse critique des données, essentielles dans la société actuelle. En favorisant l'acquisition de connaissances techniques transversales, le module participe à la formation d'une main-d'œuvre qualifiée capable de répondre aux défis complexes du monde professionnel, soutenant ainsi une éducation de qualité inclusive et axée sur l'avenir.
Learning delivery
synchrone
Evaluation and grading system and catch up exams
- Continous assessment (3 homeworks + practical session report): 75%
- Final exam on paper: 25%
Rattrapage: Final exam on paper (100%)
Textbook Required and Suggested Readings
Severance, C. (2016). Python for everybody: Exploring Data using python 3. Charles Severance.
Keywords
Python, Jupyter notebook, data science