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Establishment
Language of instruction
English
Teaching content
MANAGEMENT OF INFORMATION SYSTEMS
This course occurs in the following program(s)
IESEG Degree - Programme Grande École
- Crédits ECTS: 2.00
Training officer(s)
T.JAMES
Stakeholder(s)
T.JAMES
Présentation
Prerequisite
Intermediate knowledge in Microsoft Excel.
Critical thinking and interpretation skills.
Verbal presentation and discussion skills (in English).
Critical thinking and interpretation skills.
Verbal presentation and discussion skills (in English).
Goal
At the end of the course, the student should be able to:
1. Analyze a context, identify objectives, assess the situation, determine the analysis goals, and produce a project plan for an analysis that leads to actionable scientific or business outcomes.
2. Differentiate between the phases of the cross-industry standard process for data mining
3. Discuss each phase, and identify the crucial tasks of each phase
4. Identify, describe, and explain the mechanics behind major statistical approaches and machine learning algorithms
5. Apply major statistical approaches and machine learning algorithms within the CRISP-DM framework to a real data mining problem
6. Locate, implement, and use data manipulation, statistical, and machine learning software.
7. Carry a project through the phases/tasks of CRISP-DM and synthesize the process and analyses into a well-organized, informative report with actionable outcomes highlighted for the company or organization
1. Analyze a context, identify objectives, assess the situation, determine the analysis goals, and produce a project plan for an analysis that leads to actionable scientific or business outcomes.
2. Differentiate between the phases of the cross-industry standard process for data mining
3. Discuss each phase, and identify the crucial tasks of each phase
4. Identify, describe, and explain the mechanics behind major statistical approaches and machine learning algorithms
5. Apply major statistical approaches and machine learning algorithms within the CRISP-DM framework to a real data mining problem
6. Locate, implement, and use data manipulation, statistical, and machine learning software.
7. Carry a project through the phases/tasks of CRISP-DM and synthesize the process and analyses into a well-organized, informative report with actionable outcomes highlighted for the company or organization
Presentation
Business is emphasizing the power of data analytics to support decision making. Artificial intelligence provides the tool set that can be applied to analyze large-scale business data. To be able to plan, organize, and analyze data to produce useful information to effectively support decision making is a valuable skill. In this course, you will learn basic principles and techniques of artificial intelligence. The course will include hands-on experience using statistics and algorithms for data analysis and decision making. Class exercises and a semester project will give you the opportunity to apply foundational skills in artificial intelligence to create a report that effectively and efficiently illustrates your analysis, conclusions, and recommendations to assist decision making.
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours interactif | 10,00 | ||
| Travaux dirigés | 15,00 | ||
| Autoformation | |||
| Lecture du manuel de référence | 10,00 | ||
| Travail personnel | |||
| Group Project | 15,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
40%: In-Class Exercises and Activities
10%: Readings and Written Assignments
50%: Final Group Project
10%: Readings and Written Assignments
50%: Final Group Project
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Autres | |||
| Projet Collectif | 0,00 | 0 | 50,00 |
| Rapport écrit | 0,00 | 0 | 10,00 |
| Contrôle continu | |||
| Exercices | 0,00 | 0 | 40,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
Readings will be provided in online. -