Code
MGFE MIS 5203
Level
M2
Field
Systèmes d’information
Language
Anglais/English
ECTS Credits
3
Class hours
18
Total student load
60
Program Manager(s)
Department
- Technologies, Information et Management
Educational team
Introduction to the module
Business intelligence (BI) refers to collecting, analyzing, and interpreting data to gain insights and make informed decisions within an organization. This course seeks to develop students abilities to appreciate how business intelligence can be obtained through the use of information systems. It includes an overall knowledge of the various analytical tools used for bringing such value to the organizations with manipulation skills for certain digital tools. Fundamentals on predictive analytics and machine learning are also introduced in this module.
Learning goals/Programme objectives
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Objectifs d'apprentissage
- 1.1 - Audit advanced and specialised uses of digital intelligence tools in order to deploy them appropriately, taking into account the strategic context of organisations.
- 1.2 - Use digital intelligence tools efficiently to support the societal, digital, energy and environmental transformations of organisations, ensuring their sustainable and responsible impact.
- 2.3 - Conduct a reflective and detached analysis that takes into account the challenges, issues and complexity of a request or situation in order to propose appropriate and/or innovative solutions in line with regulatory developments.
Rubrics
-Expliquer les principes de la visualisation de données et illustrer leur utilité avec des exemples
-Choisir et adapter des types de graphiques en fonction des données et du message à transmettre
-Créer et organiser des rapports interactifs simples avec Power BI
-Analyser des données et identifier des tendances pertinentes
-Comparer les rôles, missions et outils du Data Analyst et du Data Scientist
Content : structure and schedule
- What is data visualisation
- Tools and techniques for data visualisation
- Power BI Overview, benefits, and main components (Power BI Desktop, Power BI Service, Power BI Mobile).
- Value creation via Business Intelligence
- Data Analyst vs. Data Scientist: What’s the Difference?
Sustainable Development Goals
Goal 9 - Industry, innovation and infrastructure
The course teaches advanced techniques and tools for developing innovative data-based solutions in all sorts of industries
Number of SDG's addressed among the 17
1
Learning delivery
synchrone
Pedagogical methods
Lectures
Practical exercises
Case study and project development
Evaluation and grading system and catch up exams
100% Project-based with a component of individual evaluation.
Catch-up exam via a small project development
Module Policies
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
● The tolerated delay is 5 minutes. Attendance will be declared on Moodle during these 5 minutes via a QR code provided by the teacher at each course start.
● Student should arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Textbook Required and Suggested Readings
1. Anouncia, S. M., Gohel, H. A., & Vairamuthu, S. (Éds.). (2020). Data Visualization : Trends and Challenges Toward Multidisciplinary Perception. Springer. https://link.springer.com/book/10.1007/978-981-15-2282-6
2. Godfrey, P., Gryz, J., & Lasek, P. (2016). Interactive Visualization of Large Data Sets. IEEE Transactions on Knowledge and Data Engineering, 28(8), 2142‑2157. https://doi.org/10.1109/TKDE.2016.2557324
3. Post, F. H., Nielson, G., & Bonneau, G.-P. (Éds.). (2002). Data Visualization : The State of the Art. Springer Science & Business Media. https://link.springer.com/book/10.1007/978-1-4615-1177-9
Keywords
Business Intelligence, BI tools, data analytics, data driven decisions, data and organisational value.
Prerequisites
None