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Description

Big Data and AI: Understanding Architectures, Preparing Data and Framing a Project

Two days to understand what happens between data collection and the model that puts it to work

  • 2 days — 14 h
  • In-person or virtual
  • Foundation
  • Up to 6 participants

Data accumulates in systems that do not talk to each other: ERP extracts, log files, departmental spreadsheets. Every new analysis request reopens the same debate about whether the figures can be trusted, and artificial intelligence initiatives stall before they even begin.

These 14 hours provide a complete overview without requiring a technical background. Participants learn to position the building blocks of a data architecture, to distinguish batch processing from streaming, to assess the quality of a dataset and to frame a first realistic use case within their own scope.

Learning objectives

  • Position the building blocks of a data architecture and their respective roles
  • Distinguish batch processing from streaming processing
  • Assess the quality and completeness of a dataset
  • Identify the machine learning use cases suited to your data
  • Frame a first project taking data collection constraints into account

What makes this programme different

An overview of architectures illustrated through diagrams reviewed live in the session
A quality diagnosis carried out on a dataset brought in by the group
A framing sheet completed for a use case specific to each participant

Programme

1Sources and volumes

Where the data actually comes from

  • Typology of structured and unstructured data
  • Collection from business applications and sensors
  • The concepts of volume, velocity and variety
  • Storage cost and retention periods

2Processing architectures

Warehouses, lakes and processing pipelines

  • Data warehouse and data lake
  • Extraction and transformation pipelines
  • Batch processing and stream processing
  • Data catalogue and metadata management

3Quality and preparation

What determines whether a model can be trusted

  • Missing values, duplicates and format inconsistencies
  • Standardisation and alignment of reference data
  • Sampling and collection bias
  • Documenting the transformations applied

4From dataset to use case

Framing before modelling

  • Model families and the data they require
  • Feasibility criteria for a use case
  • Roles to involve around the project
  • Personal data protection and traceability requirements
  • Success indicators defined before launch

Who is it for

Department heads · project managers · analysts and decision-makers dealing with growing data volumes.

Prerequisites

No prerequisites

Dates & locations

36 scheduled dates between November 2026 and December 2027. Seats are confirmed in the order enquiries are received.

November 2026

December 2026

January 2027

February 2027

March 2027

April 2027

May 2027

June 2027

September 2027

October 2027

November 2027

December 2027

None of these dates suit you? We open additional sessions on request, and any programme can be run privately for your team.

Practical details

Before the programme
Online positioning questionnaire. Your development objectives are shared with the trainer, who tailors the practical case studies to your context.
Teaching methods
Theoretical input, workshops and practical case studies. Digital course materials and method sheets provided.
Assessment
Multiple-choice tests and role-play exercises. Assessment of learning at the start and end of the programme, with immediate and 60-day follow-up evaluations.
After the programme
One year of access to the e-learning platform. Self-assessment of the skills acquired and a 30-day follow-up session with your trainer.
How to register
Registration online or on the basis of a quotation.
Lead time
11 working days after confirmation of registration.
Accessibility
Accessible to people of determination. Contact our accessibility coordinator to design a suitable solution: contact@mpf-academy.ae
Start dates
Rolling intake: in addition to the scheduled sessions, this programme can start on request.