# Teaching

## HS19: Data Visualization and Analysis (BINF4245)

### Organisation

Lecturer: | Dr. Claudio Mura |

Assistants: | Haiyan Yang |

Time: | Monday 16:15-18 |

Room: | BIN 2.A.01 |

Language: | English |

Exercises: | Exercises are integrated into the lecture hours or handled via OLAT. |

OLAT: | OLAT course link for BINF4245 |

Course Catalogue: | VVZ course link for BINF4245 |

### Overview

Integrated interactive data analysis and visual data exploration techniques, or visual analytics methods, are very important for the effective analysis of complex, large scale and multidimensional data. In this course we will cover many basic concepts from data analysis, signal processing, color science as well as 2D/3D raster and vector graphics which are closely related and complementary to interactive data visualization methods. The goal is to learn the most important data (pre-)processing, analysis and display techniques which are used in visual analytics methods. Hence the students will be exposed to data processing and analysis techniques such as clustering, principal component analysis or dimensionality reduction, which are often required in the data pre-processing stage of the data visualization pipeline.

This module complements the *Data Visualization Concepts* module (BINF4234) by including more in-depth signal processing, data analysis and display techniques. The lecture is targeted to students with an assessment level BSc in computer science or similar basic knowledge of computer science, programming, data structures, algorithms and math. It is recommended for the BSc students in the 3rd or higher semester.

#### Tentative list of topics to be covered (book [x] chapter)

Sampling and quantization [3] Ch.6.1 |

Interpolation and Data Fitting [1] Ch.8 |

Image processing [2] Ch.3 |

Segmentation and clustering |

Dimensionality reduction [1] Ch.6 |

2D/3D vector graphics [1] Ch.13 |

Rendering [1] Ch.16 |

Vectorfield visualization [4] Ch.6, [1] Ch.15 |

Color science [3] Ch.4 |

### Literature

Main cours textbook:

[1] Mathematical Principles for Scientific Computing and Visualization by Farin and Hansford, AK Peters, 2008

Selected book chapters from:

[2] Digital Image Processing by Gonzales and Woods, Prentice Hall., 2008

[3] Fundamentals of Multimedia by Li and Drew, Pearson Prentice Hall, 2004.

[4] Data Visualization: Principles and Practice by A. Telea, AK Peters, 2014.

As a standing homework assignment you are expected to review the corresponding book chapters matching the lectures.

### Completion Requirements

#### Reading

As a standing homework assignment you are expected to read the corresponding book chapters before the lectures and to review the material thoroughly after the lectures covering them.

#### Exercises

To complete the lecture, students must also submit any exercises given in class or distributed on OLAT. A minimum score must be attained in order to be admitted to the final exam (details in the first lecture). Programming projects must be completed and submitted exactly in the appropriate form as indicated in the exercise requirements via OLAT to the assistant leading the exercises.

#### Exam

The lecture will be completed with a written exam at the end of the semester. The exam is scheduled according to the standard UZH/OEC/IFI regulations. See also the course catalogue link at the top of the page.

**Additional Material**

- Python: https://www.python.org/
- Anaconda (open data science platform with Python IDE): https://www.continuum.io
- HoloViews (Python interactive visualization library): http://holoviews.org/index.html
- Bokeh (Python interactive visualization library): http://bokeh.pydata.org/en/latest/
- VisPy (Python interactive visualization library): http://vispy.org/index.html
- PIL (Python Imaging Library): http://www.pythonware.com/products/pil/
- Seaborn (Python visualization library): http://stanford.edu/~mwaskom/software/seaborn/
- Matplotlib (Python 2D plotting library): http://matplotlib.org/index.html
- Pygal (Python charting library): http://www.pygal.org/en/stable/
- Pandas (Python data analysis library): http://pandas.pydata.org/