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Data Visualization

Objectives

This course will combine the skills learned from data curation with essential data visualization techniques to showcase how to communicate
effectively with data in a business setting. Students discover the power of storytelling with data through real-world business applications and
gain hands-on programming experience using Python and its visualization packages.

General characterization

Code

2491

Credits

3.5

Responsible teacher

João Carlos Gomes Moura Pires / Susana Dias Brandão

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

N/A

Bibliography

This course does not require any textbook, because data science is a rapidly changing field and no textbook may cover all materials we will
teach in the course. However, the following book is recommended for your reference:
Storytelling with Data: A Data Visualization Guide for Business Professionals
The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures

Teaching method

Students are required to bring own laptops for in-class exercises and quizzes. Students are expected to have basic Python programming
experience or other languages such as R, Matlab, Java, etc.
This course adopts learning-by-doing culture that allows students to implement data visualization process through programming in Python. Most
of class material will be in the Jupyter notebooks to facilitate reproducible practices.

Evaluation method

The overall evaluation of performance consists of 3 parts
Class participation through 4 quizzes (20%)
Group project (30%)
Final exam (50%)
Students need to participate in class quizzes for at least 3 times. If students are present in all quizzes, 3 out of 4 quizzes with highest points will be counted.
Students need to propose a course project using public dataset and create a dashboard/notebook to present a non-obvious, compelling data story.
Students are responsible to structure the analysis and decide what visualization techniques to be used. The course project will be discussed with
and evaluated by the instructor every two weeks.

Subject matter

Data visualization plays an essential role in the understanding of both small and large-scale data. This course covers the fundamentals of
statistical exploration and visualization of data. We will show how to go beyond conventional tools to reach the root of data, and how to use data
to create an engaging, informative, compelling story. Students learn how to produce specialized visualizations to explore data in a detailed and
statistics-oriented manner.
We expect students to have some programming experience in Python (preferably have taken Data Curation course) and are willing to write code.
Week 1: Introduction to Data Visualization. We will review foundations and value of data visualization.
Week 2: Different Types of Visualization. We will show different types of plots and its practical uses for different
data types, such as categorical plots, distribution plots using Python standard plotting tool: Matplotlib and the Pandas
ecosystem libraries, e.g., Seaborn
Week 3: Data Exploration and Visualization. We will show how to perform explorative data analysis to discover
new insights through data visualization, such as regression plots, time-series plots and geographical plots
Week 4: Interactive visualization. We will show how to create interactive visual representation of data
Week 5: Storytelling with Data. We will show how to choose an effective visualization to present a compelling
story.
Week 6: Data Visualization of special data types. We will show how to visualize special types of data, e.g, graphs.