Data Science and Applied AI Trainings

Data Science and Applied AI: Turning Data into Competitive Advantage

Data is at the heart of any high-performing artificial intelligence strategy. Without quality data, well structured and correctly analysed, even the best AI models produce disappointing results. Data science and applied AI is the domain that bridges raw data collection and AI-augmented decision-making. This domain is aimed at analysts wishing to upskill, data scientists wanting to formalise their practice and technical teams building AI solutions on real-world data.

Our learning path covers a broad spectrum: from leveraging data with AI for analytical profiles, through to deep learning with TensorFlow for engineers building neural networks. In between, machine learning, Copilot-augmented Power BI and Microsoft Azure certifications (DP-100, DP-604) mark out a progressive path adapted to every level.

Leveraging Data with Artificial Intelligence

The training Leveraging Data with Artificial Intelligence is the ideal entry point for professionals who work with data but have no advanced technical background. It covers using generative AI to analyse, visualise and interpret data, methods for formulating relevant analytical questions with AI assistants, and the basic principles of data quality and governance. This training is suitable for business analysts, management controllers and operational managers.

Power BI and AI: Augmented Analytics with Copilot

Power BI is the most widely used data analytics tool in Swiss companies. The training Power BI and AI: Augmented Analytics with Copilot covers the integration of Copilot in Power BI to generate reports in natural language, create complex DAX measures with AI assistance, and automatically interpret trends in data. Microsoft Fabric is also covered in the training Work Smarter with Copilot in Microsoft Fabric (DP-3029).

From Data to Decision with Data Science and AI

The training From Data to Decision with Data Science and AI covers the complete pipeline: data cleaning and preparation, statistical exploration, predictive modelling and presenting results to decision-makers. This training is based on Python and the standard data science libraries (Pandas, Scikit-learn, Matplotlib) and integrates new generative AI capabilities into the analytical workflow.

Machine Learning in Business: Understanding and Applying Algorithms

The training Understanding and Applying Machine Learning in Business demystifies the most commonly used machine learning algorithms in a professional context: regression, classification, clustering, decision trees and random forests. It also addresses model evaluation methods, overfitting management and production deployment best practices.

Azure Data Science Certifications: DP-100 and DP-604

For data scientists wishing to validate their Azure expertise, two Microsoft certifications are available. The training Designing and Implementing a Data Science Solution on Azure (DP-100) is the reference certification for Azure data scientists. It covers Azure Machine Learning, training and deploying models in the cloud, MLOps and model lifecycle management. The training Implement a Data Science and Machine Learning Solution for AI in Microsoft Fabric (DP-604) covers using Microsoft Fabric for large-scale data science and machine learning workflows.

Deep Learning with TensorFlow and Neural Networks

For engineers who want to master neural network architectures, the training Deep Learning with TensorFlow and Neural Networks covers the fundamentals of deep learning, CNN architectures for vision, recurrent networks for sequences, transfer learning and fine-tuning of pre-trained models. The training AWS Deep Learning on Amazon Web Services provides a cloud perspective with SageMaker services and Amazon GPU accelerators.

Azure Databricks for Advanced Machine Learning

Azure Databricks is the platform of choice for data teams working at scale. The training Implementing a Machine Learning Solution with Azure Databricks (DP-3014) covers using Databricks to train and deploy ML models with MLflow, manage data pipelines with Delta Lake and leverage Databricks’ generative AI capabilities.

Links with Other Domains for Data Scientists

Data professionals who wish to extend their skills towards Azure AI services will find dedicated training in our Azure AI Platforms domain (Azure AI Services, AI-102, Azure AI Foundry). To build applications that leverage your models via APIs, our Development with LLMs and Agents domain covers LangChain, OpenAI APIs and RAG architectures. For decision-makers who want to govern data and AI usage, the AI Governance and Responsible AI domain provides the necessary frameworks.

Data Science and AI Training in Geneva, Lausanne and Virtual Classroom

These training courses are aimed at analytical and technical profiles: analysts, data scientists, data engineers, developers and any professional who works regularly with data. Prerequisites vary by course, ranging from basic Excel skills for analytical training to Python for machine learning and deep learning courses. Sessions take place in person in Geneva and Lausanne or as virtual classroom sessions. They are eligible for Temptraining funding for employees residing or working in Switzerland.