AI, data & software instructor
Meriam Mbindyo
Instructor for AI, data, DevOps, Agile and software modules, with experience across Paris-based IT and business schools.
Artificial intelligenceMachine learningData mining
Module content
Course description
YEAR 1: AI & DATA FOR BUSINESS DECISION-MAKING
Program: Applied Artificial Intelligence for Business & Enterprise Systems
Semester 1 (36h):AI & Business Intelligence Foundations
Program Structure
This course is part of a two-year academic program comprising four semesters.
This course corresponds to: Semester 1 of 4.
Each semester includes 12 instructional sessions, with each session lasting 3 hours, for a total of 36 hours per semester.
The complete program represents 144 hours of structured instruction, combining lectures, applied workshops, and project-based learning.
Primary anchors (from our catalogue):
-
DSCI 220 – Artificial Intelligence in Business (https://linguistic-communication.com/courses/dsci-220-artificial-intelligence-in-business/)
-
TID10 – Intro to Business Intelligence & Analytics (https://linguistic-communication.com/courses/intro-to-business-intelligence-analytics-2/)
-
DIT-010 – Digital Transformation (https://linguistic-communication.com/courses/digital-transformation/)
Semester intent
Understand how AI supports business decisions, before touching advanced data or automation.
Course Overview
This semester introduces students to how artificial intelligence and business intelligence are used to support decision-making in modern organizations. Rather than focusing on technical implementation, the course emphasizes understanding data, interpreting analytics, and identifying where AI creates real business value.
Students explore core concepts such as KPIs, dashboards, descriptive and diagnostic analytics, and AI-assisted decision support across functions like marketing, finance, operations, and HR. The semester also places AI within the broader context of digital transformation, helping students understand why many AI initiatives fail when business fundamentals are ignored.
By the end of the semester, students will be able to confidently evaluate AI opportunities, communicate with technical teams, and frame AI initiatives in business terms.
Assessment structure
-
Workshop 1 (Midterm): Business AI use-case analysis
-
Workshop 2 (Midterm): BI dashboard & insight interpretation
-
Final Project: AI-enabled business decision proposal
COURSE SCHEDULE – SEMESTER 1
| Session | Focus | Weekly Session Content |
|---|---|---|
| 1 | AI in Business Overview | What AI is (and isn’t) in organizations |
| 2 | Business Intelligence Basics | KPIs, dashboards, descriptive analytics |
| 3 | Data for Managers | Data types, quality, limits |
| 4 | Analytics for Decisions | Turning data into insight |
| 5 | AI Use Cases | Marketing, finance, ops, HR |
| 6 | Digital Transformation | AI as part of transformation |
| 7 | Workshop 1 | AI use-case mapping (midterm) |
| 8 | Ethics & Bias | Risks of data-driven decisions |
| 9 | AI Decision Support | Augmentation vs automation |
| 10 | Business Case Studies | Successes and failures |
| 11 | Workshop 2 | BI interpretation & insight defense |
| 12 | Final Project | AI-supported business decision proposal |
Target Audience
-
Business and management students with no AI background
-
Digital transformation and strategy students
-
Consultants, analysts, and project managers
-
Professionals seeking AI literacy for business decision-making
Class reference: SEM1/AI
Form Updated on: 22/01/2026 (Version 1)
Last Modified on: 22/01/2026
Program Information:
This program is continuously updated to reflect the latest AI tools, business applications, and regulatory frameworks.
Brief pédagogique en français
YEAR 1: AI & Business intelligence Foundations est présenté ici en version synthétique française afin que les équipes pédagogiques puissent évaluer rapidement l'intérêt du module.
Le module s'inscrit dans la famille Analytics et intelligence artificielle. Il peut être adapté au calendrier de l'école, au niveau Tous niveaux, au volume horaire 36 h et aux modalités d'évaluation prévues.
Objectif d'intervention
Ce module vise à relier les outils data, IA et automatisation à des usages professionnels concrets.
Livrables et activités possibles
- cas d'usage, prompts, scénarios d'automatisation ou analyses data
- évaluation critique des résultats, limites et risques
- communication claire des choix techniques et business
Adaptation école
LC peut ajuster le déroulé, la langue d'enseignement, les supports, les exercices et les critères d'évaluation selon la promotion, le diplôme, le niveau d'autonomie attendu et les contraintes de planning.
Pour une version détaillée du syllabus en français, LC confirme le programme final après cadrage du niveau, des heures, du calendrier et des livrables attendus.
