Code
MUFE MIS 3402
Level
L3
Field
Systèmes d’information
Language
Anglais/English
ECTS Credits
3
Class hours
18
Total student load
60
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
This course provides an introduction to the fundamental concepts of data science using R and R studio. Students will learn how to manipulate, analyze, and visualize data to extract meaningful insights. The module covers key stages of the data lifecycle, from collection and cleaning to the application of basic statistical models to support decision-making.
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
S1 : Introduction to Statistics. Introduction to
programming with R (Variables, Data types, Vector List,
Function). Installation R and R Studio
S2 : Statistical measures
S3 : Programming with R II (Conditionals, Loops, Vector
Matrix, Data frame). Economic Data
S4 : R/RStudio Tips. Data frames : Data analysis,
interpretation, and management
S5 : Descriptive statistics
S6 : Data analysis, interpretation, and management, Data
visualization
S7 : Correlation and t-test : Correlation vs causation.
Hypothesis testing. Binomial
S8 : Linear model, R-square, common assumptions :
normality, linearity, multicollinearity, and outliers
S9 : Presentation and discussion
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.
Pedagogical methods
enseignement interactif avec les étudiants et l'enseignant, les étudiants travail en group sur la supervision de l'enseignant
Evaluation and grading system and catch up exams
Continous assessment (3 homeworks + practical session report 30%) + Written report (50%) + Make a presentation (last class) in groups of 2 (20%)
Rattrapage: Written report individuelle (100%)
Textbook Required and Suggested Readings
Dalpiaz, D. (2021). Applied statistics with R.
Thulin, M. (2024). Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling. Chapman and Hall/CRC.
Keywords
R studio, Jupyter Notebook, Data Science