Universidade Nova de Lisboa

Universidade Nova de Lisboa

NOVA Information Management School

Data Visualization

Code

200162

Academic unit

NOVA Information Management School

Credits

7.5

Teacher in charge

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Objectives

This course will teach how to create visualizations that effectively communicate the meaning behind data through visual perception. Concepts about human perception of graphic information as well as different ways of mapping different forms of quantitative and qualitative data will be addressed. We will use MS Excel and R software to complete data visualization exercises. Tableau Public (or equivalent) will be used to explore visual interaction with data for analysis.

Learning outcomes:

·       Describe how computer graphics are used to visualize data.

·       Understand how user process information through visual displays.

·       Understand the impact of colors on perception.

·       Know different techniques for visualizing different forms of data.

·       Use techniques for visualizing database and large datasets.

·       Use MS Excel to explore data visualization.

·       Use R toolkit with packages such as ggplot2 and ggmap to compute and generate statistics and visualizations.

·       Use Tableau Public (or equivalent) for data preparation and visualization.

Prerequisites

None.

Subject matter

1.     Introduction to visualization

2.     Data Abstraction and data types

3.     Marks and channels

4.     Evaluation. Rules of Thumb.

5.     Visualizing Tables.

6.     Spatial Data.

7.     Graphs. Networks and Trees.

8.     Color.

9.     Reduce items and attributes

10.  Storytelling

Bibliography

Tamara Munzner (2014). Visualization Analysis and Design. CRC Press.

 

Teaching method

Theoretical-practical classes related with Data Visualization concepts and specific software (Excel, R, Tableau Public).

Evaluation method

1st phase

·       3 exercises per group of maximum of 3 students (5% each, up to a total of 15%)

·       Data Visualization case-study presentation per group of maximum of 3 students (10%)

·       Project report and presentation (25%)

·       Test during final class (50%) (40 multiple choice and T or F questions, no minimum grade)

 

2nd phase

·       Project report and presentation (30%)

·       2nd phase Exam (70%) (mimum grade is 9.5 points, 3 open questions and 40 multiple choice and T or F questions).

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