Data Visualization

You are here

Home » Studies » Bachelor's degrees » Bachelor Degree in Bioinformatics » Curriculum » Syllabus » Data Visualization
Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
UAB
The Data Visualization course introduces concepts of visual design and data metaphors essential for the design and use of specific bioinformatics tools. The course is divided into two main parts. The first part focuses on basic tools for data visualization with a special emphasis on bioinformatics: common libraries and visualizations, interactive visualizations, etc. The main package used in this part is ggplot2, which is based on the Grammar of Graphics and will be very relevant for the entire course. The second part deals with visualization for exploring complex data, dimensionality reduction, and principal component analyses (PCA, t-SNE, and UMAP). The lessons are highly practical and dynamic, providing an interactive learning experience of the subject.

Teachers

Person in charge

  • Marta Coronado Zamora ( )

Others

  • Jose Francisco Sanchez Herrero ( )

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Learning Outcomes

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K7 - Analyze the sources of scientific information, valid and reliable, to justify the state of the art of a bioinformatics problem and to be able to address its resolution.

Skills

  • S4 - Develop specific tools that enable solving problems on the interpretation of biological and biomedical data, including complex visualizations.
  • S5 - Disseminate information, ideas, problems and solutions from bioinformatics and computational biology to a general audience.
  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.
  • S9 - Exploit biological and biomedical information to transform it into knowledge; in particular, extract and analyze information from databases to solve new biological and biomedical problems.

Competences

  • C2 - Identify the complexity of the economic and social phenomena typical of the welfare society and relate welfare to globalization, sustainability and climate change in order to use technique, technology, economy and sustainability in a balanced and compatible way.
  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C4 - Work as a member of an interdisciplinary team, either as an additional member or performing managerial tasks, in order to contribute to the development of projects (including business or research) with pragmatism and a sense of responsibility and ethical principles, assuming commitments taking into account the available resources.

Objectives

  1. Visualize, manipulate and extract biological data
    Related competences: K1, K7, S4, S7, S9,
  2. Know existing techniques and computational tools in a particular field
    Related competences: K7, S4, S7, S9,
  3. Evaluate what is the most suitable technical and/or computational tool in every situation
    Related competences: C2, C3, C4, K2, K7, S4, S5, S7, S8, S9,

Contents

  1. - Basic tools: Grammar of Graphics (ggplot2)
    Theoretical sessions on perception, visual illusions, the Grammar of Graphics by applying ggplot2, as well as specialized libraries and advanced visualizations.
  2. - Interactive visualizations using htmlwidgets and Shiny
    Learn how to create interactive visualizations with htmlwidgets packages and Shiny applications.
  3. - Principal component analysis (PCA)
    Explore techniques for visualizing complex data and dimensionality reduction (PCA).
  4. - Non-linear projections: t-SNE and UMAP
    Application of t-SNE and UMAP methods for data reduction.

Activities

Activity Evaluation act


Practical lessons

Fill RMarkdown document / application-based
Objectives: 1 2 3
Contents:
Theory
0h
Problems
22h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Home assignments

Conceptual / synthesis-based / application-based
Objectives: 1 2 3
Contents:
Theory
0h
Problems
8h
Laboratory
0h
Guided learning
0h
Autonomous learning
45h

Mid-term exam

Conceptual / synthesis-based / application-based
Objectives: 1 2 3
Week: 9
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
20h

Final exam

Conceptual / synthesis-based / application-based
Objectives: 1 2 3
Week: 18
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
25h

Theoretical lectures

Conceptual / synthesis-based / application-based
Objectives: 1 2 3
Contents:
Theory
25h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

During theoretical sessions, the professor will present concepts dynamically, using examples and solving practical casses in class. During practical sessions, students will independently work on hands-on exercises, with supervision and assistance from the professor as needed. Both theory and practical lessons require a laptop.

Evaluation methodology

The evaluation of the subject will be structured as follows:

1. Active participation in class (10%): Weekly assessment of the participation in the theoretical and practical sessions, including discussions, activities, and brief quizzes.

2. Assignments (40%): Group activities will be evaluated through four assignments per main unit.

3. Midterm exam (20%): short exam to evaluate the concepts acquired during the first block of the subject.

4. Final exam (30%): practical exam covering all concepts.

The weighted grade of the midterm exam and the final exam requires a minimum score of 3.5 out of 10 to consider the other parts of the evaluation. A final grade of at least 5 out of 10 is required to pass the course. Plagiarism or cheating will result in course failure and potential disciplinary actions.

A student is considered to have taken the subject if he/she takes the final exam. If the student has taken the subject but has failed, then the student may take the re-evaluation exam and in this case the grade of the subject will be 40% home assignments, 10% participation and 50% recovery exam.

Bibliography

Basic:

Web links

Previous capacities

Basic knowledge in R and familiarity with RStudio are prerequisites.