Data Engineering

In most organizations, a data engineer is the primary role responsible for integrating, transforming, and consolidating data from various structured and unstructured data systems into structures suitable for building analytics solutions. In this training module, we will demonstrate useful techniques and tools for building efficient data platforms. As a data analyst or data engineer, you will also learn how to ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints.

Data Analytics & Visualization

A data analyst enables businesses to maximize the value of their data assets through visualization and reporting tools. During this training module, data analysts learn how to design and build scalable and effective data models, enable and implement advanced analytics capabilities into reports for analysis, and turn raw data into relevant and meaningful insights.

Data Governance

Data governance encompasses a collection of processes, policies, roles, metrics, and standards that ensure the efficient and effective use of information. It also helps in establishing data management practices that safeguard data security, privacy, accuracy, and usability throughout the entire data lifecycle. Various targeted services and methodologies are available for the design and implementation of data governance, in line with the challenges of the modern era.

Machine Learning

This module provides a comprehensive introduction to machine learning (ML), covering key concepts, techniques, and algorithms. It begins with an overview of ML's history and applications, and then delves into supervised, unsupervised, and reinforcement learning. Topics include decision trees, ensemble methods (Random Forest, XGBoost), and neural networks, with a focus on Convolutional Neural Networks (CNNs) for image processing and Transformers for natural language processing, including models like GPTs. Practical sessions cover data collection and processing, ML frameworks (TensorFlow, PyTorch), and model evaluation. The module concludes with real-world deployment, optimization, and a focus on responsible ML, addressing privacy, security, and ethical considerations.

Machine Learning Operations

This module explores the topic of MLOps (Machine Learning Operations), focusing on how to efficiently and scalably operate ML systems in real business environments. Through both in-person and online lectures, practical examples will be used to demonstrate the most important methods and techniques in MLOps. Participants will first be introduced to the fundamentals of ML system design, with a particular emphasis on aligning business objectives with ML goals. Following this, the best practices for the individual components will be covered. Key topics include data engineering, data preparation, feature extraction, model development, evaluation, and deployment, as well as monitoring data drift. The module also addresses continuous learning, MLOps tools and infrastructure, and the human aspects of machine learning, such as user experience, team structure, and responsible AI practices.

Language Models and Generative Artificial Intelligence

This module covers a series of sessions on AI, machine learning, and prompt engineering. It introduces key concepts like AI models, sequence-to-sequence networks, and large language models (LLMs). Topics include practical and theoretical foundations of prompt engineering, its role in code generation, data processing, and debugging. Additional areas include RAG systems, AI safety, responsible code generation, and the latest in generative models and APIs. Optional topics explore generative art, sentiment analysis, and ethical concerns in AI-generated content.

Using Artificial Intelligence in Corporate Environment

This module explores the potential of AI assistants, specifically Microsoft Copilot, across various corporate sectors. It covers practical use cases for business managers, executives, legal advisors, and HR roles. Additionally, it demonstrates how generative AI (GenAI) can be applied throughout the software development lifecycle, from requirements analysis to maintenance, using tools like ChatGPT and GitHub Copilot to support and automate tasks such as coding, testing, and deployment.

Legal and Compliance Aspects of Artificial Intelligence

This module focuses on the current legal regulations surrounding artificial intelligence, with particular emphasis on the new EU AI Regulation and its compliance requirements. The course provides a detailed overview of technology regulation, identifying key practical issues and challenging areas. Participants will gain a comprehensive understanding of the legal regulatory environment surrounding AI and, with the knowledge acquired during the course, will be better equipped to navigate and manage the complex compliance challenges posed by this rapidly evolving field. The module also facilitates the provision of AI literacy, ethical, and legal training, as required by the AI Regulation for both AI system providers and users.