Data Scientist - Foundations & Machine Learning (1178EN)
Description
At the end of the training, participants will be able to:
- Install and use a Python environment for data science
- Manipulate data with fundamental tools (Anaconda, Jupyter, Pandas)
- Master the mathematical foundations necessary for machine learning
- Implement and evaluate different supervised learning models
- Work on artificial neural networks
- Apply unsupervised learning
- Visualise multidimensional data
- Complete a comprehensive data science project and defend it
Module 1 | Software basic programming |8h - Anaconda - Jupyter - Python Module 2 | Mathematics | 8h Module 3 | Machine Learning | 40h - Introduction Basics concepts and notations - Basics models for supervised learning - Multilayer Artificial Neural Networks - Unsupervised Learning - Visualising High-dimensional Data Module 4 | Final Project | 17 h
> The approach combines theory, hands-on exercises, and an individual project, with interactive discussions to encourage practical application of the skills learned.
> No formal qualification is required. However, candidates should have a basic understanding of computer science, logic, mathematics (including matrix algebra), and programming (algorithmic thinking, control structures, variables, data types, functions, and Python). These prerequisites are assessed through the entry exam, and admission to the programme is based on the results obtained.
> Employees, adults in career transition, or professionals aiming to move into roles such as Data Scientist, Advanced Data Analyst, or Machine Learning Engineer; profiles from IT, science, mathematics, engineering, or business analytics backgrounds.
Certification du Ministère de l’Education nationale, de l'Enfance et de la Jeunesse
SkillsBridges
The CNFPC reserves the right to cancel or postpone a training course if the number of duly registered participants is insufficient, for organisational reasons, or in the event of force majeure.
Cancellation: In the event of cancellation or absence from the training course, the full registration fee remains payable unless the registration is cancelled no later than 72 hours before the start of the course. Absences duly justified by a medical certificate entitle the participant to a full refund of the registration fee.
Sessions
Courses:
- September : Thursday 24
- October : Thursday 1, Thursday 8, Thursday 15, Thursday 22, Thursday 29
- November : Thursday 12, Thursday 19, Thursday 26
- December : Thursday 3, Thursday 10
Additional information
ENTRY EXAM
- To ensure that participants have the necessary prerequisites to successfully complete the training programme, an entry exam is organised before the start of the training.The entry exam covers the following areas:• Fundamentals of matrix mathematics• Fundamentals of software programming- Basic algorithmic concepts- Control structures, including conditional statements and loops- Variables, data types, and functions- Reading, understanding, and writing simple Python scripts
- The entry exam is completed using a CNFPC laptop and/or a paper-based test, depending on the format selected for the training session.
- Admission to the training programme is subject to the results obtained in the entry exam. Candidates are selected based on their performance in the assessed areas. This selection process ensures that participants have the knowledge and skills required to successfully complete the training programme.
- TRAINING SCHEDULE
- 24/09/2026 – Entry Exam
- 01/10/2026 – Mathematics
- 08/10/2026 – Software Programming
- 15/10/2026 – Machine Learning
- 22/10/2026 – Machine Learning
- 29/10/2026 – Machine Learning
- 12/11/2026 – Machine Learning
- 19/11/2026 – Machine Learning
- 26/11/2026 – Individual Project Implementation
- 03/12/2026 – Individual Project Implementation
- 10/12/2026 – Final Assessment
Organisation
Organizer:
- CNFPC Esch
- +352 558 987 - 1
- inscription.esch@cnfpc.lu
Language: English
Expertise Area: Digital & Data
Public: Business and independent, Salaried employee, Job seeker
Duration: 75 hours
Mode: Face-to-face
Sessions