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

Others

Weekly hours

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

Competences

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: K2, K7, S4, S5, S7, S8, S9, C2, C3, C4,

    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
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final exam

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

    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%): theory-practical exam to evaluate the concepts acquired during the first block of the subject.

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

    Both midterm and final exams are done using a computer.

    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.