An old English adage says "a picture is worth a 1,000 words", meaning that complex ideas are often easier to convey visually. This lecture is about the craft of creating informative images from data. Starting from the basics of the human visual perception, we will learn how visualizations are designed for explorative, communicative or confirmative purposes. We will see how data can be classified, allowing us to develop algorithms that apply to a wide range of application domains. The lecture covers the following topics: - data abstraction (data types, data set types, attribute types), - perception and mapping (marks and channels, effectiveness, pre- attentive vision, color maps), - task abstraction and validation (actions and targets), - information visualization tools (HTML, CSS, JavaScript, React, D3), - information visualization methods (tabular data, networks, trees), - scientific visualization methods (volume rendering and particle visualization), - scientific visualization tools (VTK, ParaView), - view manipulation (navigation, selection, multiple views), - data reduction (filtering, agreggation, focus and context), - lies in visualization (human biases and rules of thumb), - applications (deep learning, medical visualization, optimization) The lecture is accompanied by voluntary exercises. Theoretical exercises concentrate on the classification of data and the design and analysis of visualizations, while programming exercises using web-based technologies give examples of their implementation.