Data Science Trainings

Data science and data engineering in French-speaking Switzerland

Data science has become a major differentiation lever for Swiss companies. Private banks, insurance companies, international organisations, retailers and public actors are now structuring their data teams around profiles able to administer critical data platforms, build machine learning models and put deep learning solutions into production. In Geneva, Lausanne and across French-speaking Switzerland, demand for data engineer, data scientist and MLOps profiles remains strong, particularly in finance, insurance and healthcare.

Following a recognised data or deep learning training opens access to highly valued roles and to reference Microsoft certifications. The DP-300 certification (Azure Database Administrator Associate) validates cloud relational database administration skills, particularly appreciated in organisations scaling on Azure. Mastery of TensorFlow 2.0 opens the way to roles oriented towards deep learning, computer vision and natural language processing.

ITTA offers several training courses dedicated to data science and data engineering, complemented by neighbouring sub-domains (cloud computing, AI and LLM, database design). Each session is built around hands-on labs on real environments, with limited group sizes to enable personalised support.

Whether you are a data engineer, data scientist, AI engineer, data architect or cloud DBA, our training in Geneva and Lausanne provides the practical skills expected on the Swiss market to drive Azure data platforms and start in deep learning with TensorFlow.

Skills covered by our data science catalogue

Azure SQL and scalable database solutions (DP-300)

The Implement scalable database solutions using Azure SQL (DP-300) training covers the entire scope of the Microsoft Database Administrator Associate certification. The programme addresses the planning and implementation of Azure SQL environments, security configuration, performance monitoring and optimisation, high availability, disaster recovery, and administration task automation. This training suits SQL Server DBAs moving to Azure cloud, data engineers managing managed relational databases, and data architects designing modern Azure platforms.

Deep Learning with TensorFlow 2.0

The Deep Learning with TensorFlow 2.0 training covers deep learning fundamentals and hands-on practice on the TensorFlow framework. The programme addresses neural networks (perceptron, MLP, CNN, RNN, transformers), optimisation techniques (gradient descent, regularisation, dropout), main tasks (classification, regression, computer vision, natural language processing), and model deployment. This training targets Python developers wishing to start in deep learning, AI engineers ramping up, and data profiles joining a deep learning project.

Data modelling and performance

The transversal skill of data modelling remains central in data science. Our training courses cover relational and NoSQL schema design, analytical modelling for reporting, complex query optimisation and capacity planning. This skill is particularly expected in organisations processing large volumes or scaling on cloud-native platforms.

Data pipelines and MLOps

Putting machine learning models into production requires an engineering discipline comparable to DevOps applied to software. This MLOps dimension covers pipeline orchestration (data preparation, training, evaluation, deployment), model version management, drift monitoring in production and user feedback. Our data engineer and data scientist training courses progressively integrate these best practices to prepare profiles for the real requirements of production projects.

Build your data science path

A SQL Server DBA moving to Azure cloud follows DP-300 to validate ramp-up and obtain the Microsoft Database Administrator Associate certification. A Python developer wishing to start in deep learning follows Deep Learning with TensorFlow 2.0 to acquire the practical basics of the field. A data engineer or data architect combines DP-300 with Azure Data Engineer (DP-203) or Azure AI Engineer (AI-102) certifications available in the cloud sub-domain. A confirmed data scientist enriches their path with the AI, database design and object-oriented programming sub-domains to structure complete production projects.

Featured courses in this catalogue

Here is a selection of reference training courses in this catalogue, accessible directly:

• Implement scalable database solutions using Azure SQL (DP-300)
• Deep Learning with TensorFlow 2.0

Data science and related skills

Data science fits into a broader data ecosystem. The database design and development sub-domain covers SQL, NoSQL and Apache Kafka, fundamental bricks to structure a data platform. Cloud computing training brings Azure (DP-203 Data Engineer, AI-102 AI Engineer, DP-100 Data Scientist) and AWS (Data Analytics Specialty, Machine Learning Specialty) certifications. The data analysis and databases sub-domain covers SQL Server and Azure SQL on the infrastructure side. Data analysis BI training brings Power BI for results visualisation. The programming languages domain deepens Python, Scala and R, essential for data engineering and modelling. Artificial intelligence training broadens towards LLMs, generative AI and AI agents.

Data science trends in 2026

Several evolutions are shaping data science in 2026. Data lakehouses (Databricks, Snowflake, Microsoft Fabric, BigQuery) integrate batch and streaming into a unified architecture, which simplifies putting data projects into production. MLOps establishes itself as a dedicated discipline, with specialised tools (MLflow, Weights and Biases, Vertex AI). Foundation models and LLMs transform machine learning usage, particularly for natural language processing and document analysis. Data governance and GDPR compliance impose fine controls (lineage, classification, masking, audit), particularly important for Swiss advertisers and organisations processing European data. Our pedagogical content regularly integrates these evolutions to remain aligned with current company practices.

Data science training in Geneva, Lausanne and online

All our data science training courses are available on-site in our Geneva (Route des Jeunes 35) and Lausanne (Avenue de Mon-Repos 24) centres, as well as in interactive virtual classroom with a live trainer. Our sessions are organised in 5-week cycles, which makes registration fast and planning smooth for working developers and engineers. Each session includes hands-on labs on Azure environments and Python notebooks with TensorFlow. Customised corporate training is also possible at your premises, in Geneva, Lausanne, Vaud and across French-speaking Switzerland, with a programme adapted to your internal data stack. Several professional funding paths can be considered depending on your profile and employer.

Why choose ITTA for your Azure SQL or TensorFlow training

ITTA is a certified training centre based in French-speaking Switzerland, official partner of Microsoft and Google. Our data and AI trainers are engineers active in data projects for Swiss and international companies, allowing them to share current concrete cases rather than purely theoretical materials. The catalogue covers Azure data certifications and deep learning with TensorFlow, complemented by neighbouring sub-domains (cloud, AI, database design). Our pedagogical team supports you in choosing the right path, preparing the Microsoft exams and identifying the funding solutions that fit your professional situation.

FAQ

Is SQL experience required before DP-300 training?

Yes. The DP-300 certification targets relational database administrators. Prior experience in SQL Server or Azure SQL administration is highly recommended. Beginner profiles can start with the SQL Fundamentals training in the database design sub-domain before tackling DP-300.

Do I need to know Python before Deep Learning with TensorFlow?

Yes. Basic Python mastery (variables, functions, classes, common libraries like NumPy and Pandas) is necessary to follow the Deep Learning with TensorFlow 2.0 training. Profiles completely new to Python can follow a Python training in the programming languages sub-domain before tackling deep learning.

What is the difference between data engineering and data science?

Data engineering builds and maintains the pipelines, databases and platforms that make data available, performant and reliable. Data science leverages this data to produce statistical models, predictions or recommendations. Both profiles are complementary and frequently work as a team on production machine learning projects.

Are your data science courses available for companies?

Yes, our data and deep learning training courses are available in-house, in Geneva, Lausanne and in virtual classroom, with a programme adapted to your internal stack (Azure, AWS, GCP, Databricks, Snowflake). Our team builds the specifications with you and organises sessions according to your calendar.