This course introduces the core concepts and workflow of analytics and ML (Machine Learning) through a hands-on approach. You learn how an analytics process is implemented from problem definition, data understanding, and preprocessing to modelling, interpretation, visualization, and the practical use of results. The course focuses especially on predictive time series problems and forecasting with machine learning for regression. You also learn how to validate forecasting results by calculating and interpreting forecasting errors. Through real-life examples and practical exercises, the course builds a strong foundation for further studies in analytics, covering analytics systems, Python development, feature engineering, time series forecasting, visualization, and evaluation.

This course gives you a practical and structured introduction to predictive machine learning with static data. You learn how to move from data acquisition and preprocessing to model selection, parameter tuning, evaluation, and comparison of results. The course covers core approaches for both classification and regression, including linear models, neural networks, and numerical methods for representing and processing text. You also develop an understanding of how to build optimized ML solutions that are robust, scalable, and ready for production-oriented use.

This course focuses on the principles of data visualization as a way to turn analysis into insight, communication, and action. You learn to design effective and user-centered visualizations by combining core ideas from data visualization with interface and interaction design. Topics include visual perception, data and chart types, dashboards, interaction basics, and geospatial visualization. The course has a strong practical emphasis on producing clear and meaningful graphical material, while also helping you understand how visual analytics can support communication, storytelling, and data-informed decision-making in organisations.

This course introduces the concepts and methods behind modern deep learning and foundation models. You learn the principles of gradient descent and backpropagation, and you gain hands-on experience in building neural networks from scratch as well as fine-tuning pre-trained models. The course also develops your ability to handle large-scale data programmatically for training and testing deep learning systems. You learn how DL (Deep Learning) can be applied to demanding analytics tasks and how model outputs can be integrated into the design of intelligent services. 

This course provides an overview of how ML and big data workflows are supported by cloud computing, scalable infrastructure, and modern data engineering practices. You are introduced to descriptive and predictive modelling in smaller-scale settings and then learn how similar approaches can be adapted to large-scale data environments. The course covers the analytical process from data-related requirement handling and domain understanding to modelling, tooling, and verification of results. It also highlights how computing resources and engineering choices affect performance, scalability, and the deployment of analytics solutions.

This course introduces service design as a strategic and human-centered approach to innovation, business transformation, and sustainable value creation. You learn the main theories, methods, and tools of service design, and develop the ability to analyse needs, transform processes, and create service solutions in collaboration with users, partners, and interdisciplinary teams. The course also strengthens your skills in strategic planning, service design leadership, and critical communication, with a strong emphasis on sustainability, innovation, and future-oriented thinking.