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 2 (36h):Big Data, Analytics & Business Value
Program Structure
This course is part of a two-year academic program comprising four semesters.
This course corresponds to: Semester 2 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)
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TID-020 – Introduction to Big Data Architecture (https://linguistic-communication.com/courses/big-data-architecture/)
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TID-030 – Data Mining Workshop (conceptual) (https://linguistic-communication.com/courses/data-mining/)
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EXP/10 – Multi-Source Data Extraction & Visualization (https://linguistic-communication.com/courses/exp-10-multi-source-data-extraction-visualization/)
Semester intent
Help business students understand where AI data comes from, how it’s prepared, and how it creates value.
Course Overview
This semester focuses on how data is collected, prepared, analyzed, and transformed into business insight at scale. Students learn where organizational data comes from, how multiple data sources are combined, and how analytics supports strategic and operational decisions.
Rather than technical deep dives, the course emphasizes data quality, interpretation, visualization, and communication of insights to executives and stakeholders. Students develop the ability to question data, identify limitations, and translate analytical results into business recommendations.
The semester prepares students to work effectively with data teams while remaining firmly rooted in business objectives.
Assessment structure
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Workshop 1: Data sourcing & quality evaluation
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Workshop 2: Analytics interpretation for executives
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Final Project: Data-driven business insight report
COURSE SCHEDULE – SEMESTER 2
| Session | Focus | Weekly Session Content |
|---|---|---|
| 1 | Big Data Concepts | Volume, velocity, value |
| 2 | Big Data Architecture | Data lakes & pipelines |
| 3 | Business Data Sources | APIs, platforms, reports |
| 4 | Multi-Source Data | Combining datasets |
| 5 | Data Preparation | Cleaning & structuring |
| 6 | Intro to Data Mining | Patterns & predictions |
| 7 | Workshop 1 | Data quality & sourcing audit |
| 8 | Analytics Interpretation | Reading results as a manager |
| 9 | Visualization | Communicating insight |
| 10 | Big Data Business Use | Scale & cost trade-offs |
| 11 | Workshop 2 | Executive analytics briefing |
| 12 | Final Project | Business analytics case study |
Target Audience
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Business students interested in analytics and data-driven strategy
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Future consultants and product managers
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Marketing, finance, and operations profiles
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Students preparing for advanced AI and automation topics
Class reference: SEM2/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: Big Data, Analytics & Business Value 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.
