In this course I make an attempt to provide a systematic review of the scientific illustrations surrounding us in everyday life.

This course is not indented to teach some particular software like Photoshop or SPSS. This course is about making the end product, namely a scientific illustration, in a proper way. Students can choose and use their own tools for making figures whatever they want. The point of departure will be Excel, which I expect to be known by every participant.

It will consist of the following chapters:

1. What makes an illustration scientific? Basic features, brief history, current use. What is the purpose of the illustration?
2. Major types of scientific illustrations. What the difference, which one is my favorite? How we chose the best option? Continuous and discrete variables, many or few points. Trend or no trend.
3. Data sources for illustrations. Spreadsheets and other supporting file formats. Advantages and limitations. Build-in options for illustrations. Fitting data. Adding statistics.
4. Visual details. Colors, sizes, compositions. Basic rules of making better illustration. Fonts, consistency of scales. Colors and sizes as informative features, composite figures, descriptions, using space.
5. Functions and macros. Making heatmaps. Making networks. Using programming languages. Brief introduction to graphic packages in Python, R, JavaScript
6. Extra time for extra questions and general discussion.