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Course : Certifying course Building and implementing big data and AI models

Skills block of RNCP title 40573

Practical course - 27d - 189h00 - Ref. ZBG
Price : 6950 CHF E.T.

Certifying course Building and implementing big data and AI models

Skills block of RNCP title 40573


New course

This training path represents the fourth block of skills making up the state-recognized level 7 certified qualification (Bac +5) " Expert in IT and information systems".


INTER
IN-HOUSE
CUSTOM

Practical course in person or remote class
Disponible en anglais, à la demande

Ref. ZBG
  27d - 189h00
6950 CHF E.T.

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This training path represents the fourth block of skills making up the state-recognized level 7 certified qualification (Bac +5) " Expert in IT and information systems".


Teaching objectives
At the end of the training, the participant will be able to:
Understand basic statistical tools and how to calculate them
Acquire the technical skills needed to manage complex, unstructured and massive data flows
Use statistical parameters to understand a data series
Create selections and rankings from large volumes of data to identify trends
Implementing an application with MongoDB
Understand the concepts of Machine Learning and the evolution towards Deep Learning (deep neural networks).
Master neural network implementation methodologies and the strengths and limitations of these tools
Identify and use data mining tools

Intended audience
Anyone wishing to build and implement big data and AI models.

Prerequisites
Être titulaire d’un diplôme ou titre de niveau 6 (équiv. Bac + 3/4) en spécialité informatique ou justifiant d’une expérience professionnelle équivalente.
Être titulaire d’un diplôme ou titre de niveau 7 (équiv. Bac + 5) en spécialité scientifique ou justifiant d’une expérience professionnelle équivalente.

Certification
Each skills block is validated by a written exam in the form of a case study. Skills block "Building and implementing Big Data and AI models", part of the "Expert en informatique et systèmes d'information" professional certification, issued by 3W ACADEMY. Registered in the répertoire national des certifications professionnelles, under number 40573, by decision of the Director General of France Compétences dated 30/04/2025.

Course schedule

1
Descriptive statistics, introduction

  • Definition.
  • Mathematical formalization.
  • Statistical processing of one-dimensional data.
  • Random variables.
  • Two-dimensional descriptive statistics: contingency tables.
  • Case study: using participants' data.

2
Big Data, methods and practical solutions for data analysis

  • Understand the concepts and challenges of Big Data.
  • Big data technologies.
  • Manage structured and unstructured data.
  • Big data analytics techniques and methods.
  • Data visualization and concrete use cases.

3
Statistical modeling, the essentials

  • Reminders of the fundamentals of descriptive statistics.
  • Statistical analysis approach and modeling.
  • Position and dispersion parameter.
  • Tests and confidence intervals.
  • Overview of tools.

4
Data Analytics with Python

  • Introduction to modeling.
  • Model evaluation procedures.
  • Supervised algorithms.
  • Unsupervised algorithms.
  • Component analysis.
  • Text data analysis.

5
MongoDB, getting started and development

  • Introduction to MongoDB.
  • Connecting to and using MongoDB.
  • Modeling and indexing.
  • Driver management.
  • Introduction to replication and sharding.
  • Performance management and diagnostics.
  • MongoDB extension.

6
Machine learning methods and solutions

  • Introduction to Machine Learning.
  • Model evaluation procedures.
  • Predictive models, the frequentist approach.
  • Bayesian models and learning.
  • Machine Learning in production.

7
Deep Learning and neural networks: the basics

  • Introduction to AI, Machine Learning and Deep Learning.
  • Fundamental concepts of a neural network.
  • Common Machine Learning and Deep Learning tools.
  • Convolutional Neural Networks (CNN).
  • Recurrent Neural Networks (RNN).
  • Generational models: VAE and GAN.
  • Deep Reinforcement Learning.

8
Data mining in practice

  • The Data Mining project.
  • Data mining techniques.
  • Statistical tools.
  • Data visualization.
  • Analysis of qualitative and textual data.
PARTICIPANTS
Anyone wishing to build and implement big data and AI models.

PREREQUISITES
Être titulaire d’un diplôme ou titre de niveau 6 (équiv. Bac + 3/4) en spécialité informatique ou justifiant d’une expérience professionnelle équivalente.
Être titulaire d’un diplôme ou titre de niveau 7 (équiv. Bac + 5) en spécialité scientifique ou justifiant d’une expérience professionnelle équivalente.

TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.

ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.

TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.

TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.

ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.

This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.

Dates and locations

Dernières places
Date garantie en présentiel ou à distance
Session garantie
From 28 to 29 September 2026
FR
Remote class
Registration
From 7 to 8 December 2026
FR
Remote class
Registration
From 13 to 14 May 2027
FR
Remote class
Registration
From 13 to 14 May 2027
EN
Remote class
Registration
From 14 to 15 October 2027
FR
Remote class
Registration
From 14 to 15 October 2027
EN
Remote class
Registration

REMOTE CLASS
2026 : 28 Sep., 7 Dec.

2027 : 13 May, 13 May, 14 Oct., 14 Oct.



This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.