Learning outcomes

At the end of this learning activity, the student should be able to:
 
Technical skills:
  • Clean and prepare databases for analysis
  • Use Python libraries such as pandas for importing, cleaning, transforming and aggregating data.
  • Create clear and effective visualizations using Python to communicate analytical results.
  • Use statistical techniques to describe datasets and discover underlying trends and patterns.
  • Build simple predictive models and understand their implications.
 
Ethical and managerial skills:
  • Identify and explain ethical issues related to the collection, analysis and use of data, as well as propose solutions to protect the rights of individuals
  • Demonstrate how data analysis can contribute to sustainable development and reflect on the influence of data on society
  • Popularize the results of a data analysis for a non-specialist audience, ensuring that the information presented is understandable and facilitates informed decision-making.
  • Use data analysis techniques to create value through the use of data.

Goals

The Data Analytics course has a dual objective.
 
On the one hand, to train students in the technical skills needed to carry out a data analysis project in Python. The objectives related to these technical skills are to lead students to:
  • Understand how data manipulation techniques can help solve problems
  • Realize that well-designed visualizations can make complex data accessible and understandable to a wide audience
  • Realize that data analysis can reveal essential information that can guide public policies
  • Understand the importance of predictive modeling to anticipate future trends and make proactive decisions in various fields, such as health or the economy
On the other hand, to raise awareness among students about the impact of data on society and inspire them to use their skills in an ethical and positive manner. The course objectives related to these ethical and managerial skills are to lead students to:
  • Understand that the ethical use of data is crucial to maintaining public trust and protecting the rights of individuals
  • Realize that data analysis skills can be used to solve critical societal problems and improve quality of life
  • Understand they can use their data analytics skills for beneficial causes, such as sustainable development or social justice
  • Emphasize the importance of making data analyses understandable to enable informed decision-making and democratize access to information
  • Promote a culture of innovation and problem-solving that values ​​the positive impact of data on society and the economy

Content

Part 1. Introduction to Data Analytics.

Part 2. Descriptive and Exploratory Data Analysis.

Part 3. Introduction to Predictive Analytics.

Teaching methods

The course is made up of ex-cathedra sessions, practical and reflective exercises as well as a group project.

Assessment method

First session evaluation:
 
A data analysis project carried out in group (50%). The project assessment focuses on ethical reflections related to data analysis as well as on the identification and communication of managerial recommendations resulting from this analysis. The applied methodology and chosen algorithms are also part of the project assessment.
 
A written exam (50%) testing the skills developed in class, in practical sessions and during the project.
 
Second session evalution:
 
The group project score remains the same but counts for 25%.
 
The student does another written exam testing the skills developed in class, in practical sessions and during the project. This second session exam counts for 75%.

Language of instruction

English
Training Study programme Block Credits Mandatory
Bachelor in Business Engineering Standard 0 4
Bachelor in Business Engineering Standard 3 4