Course content

Research shows that humans are better at analyzing visual information compared to plain numbers. Effective visualization can help to better understand the data, underlying patterns, trends, and connections. Visualization is one important part of Data Science. However, data often comes in a raw form, is usually noisy, inaccurate, incomplete, and sometimes contradictory. Therefore, it is important to familiarize oneself with the preparation phase, to clean and preprocess data, and to extract information from it. Visualization is a central part of the whole data analysis cycle: from visual analytics during the cleansing process to rich and interactive visualizations.

The course consists of two main parts - data processing and data visualization, with the following topics:

1. Data processing:

• Typical problems of raw data.
• Data cleansing methods.
• Different data formats, conversion, and aggregation.
• Simple visualization and statistical tools for data quality inspection.
• Regression modelling
• Introduction to analog and digital signal processing.
• Signal sampling and synchronization.

2. Data visualization:

• User interface design principles based on Human perception
• Charts and their applicability for different data types.
• Interactive visualizations.
• Modern visualization tools.
• Visualization of specific data sets (spatial data, temporal data, graph data, etc).

Learning outcome

Knowledge:

• Recall the basic principles of human perception.
• Recall fundamental data and signal processing methods.
• Understand how to clean and validate data.
• Understand how data visualization guidelines depend on human perception.

Skills:

• Apply data and signal processing and visualization tools to clean and visualize a data set.
• Experiment with different visualization techniques.
• Reflect on the choice of data processing and visualization techniques and results.

General competence:

• Analyze data using interactive exploration and visual analytics.
• Evaluate and choose the best visualization tools and techniques for a given data set.
• Evaluate a data visualization created by others.
• Create a story using a visualization of a data set.
• Interact with AI tools about the data processing and visualization topics.

Learning methods and activities

The course will include lectures and self-study materials on the course topics, as well as individual assignments on data processing and visualization. The students will work with several data sets, process them, and visualize the results. There will be mandatory assignments and a group project. The specific number and schedule of the assignments will be given at the start of the course.

Compulsory assignments

• Obligatoriske innleveringer

Specific conditions

Recommended previous knowledge

Fundamental programming knowledge (Python or another language).

Course materials

Will be announced at the beginning of the course.