LSI-M-2: Data Visualization (SS21)
Topic outline
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LSI-12-1 Data Visualization
Prof. Dr. Javier Valdes (javier.valdes@th-deg.de)
Prof. Dr. Phillipp Torkler (phillip.torkler@th-deg.de)
Niveau: Master Degree: Life Science Informatics Time of attendance: 60 - 150 Stunden ECTS: 5 ECTS / 4 SWS
NEW DIAL-IN
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Review of statistics & introduction to Dataviz,
We will start reviewing some of the fundamental concepts that we will use throughout the course.
To do this, we will review some of the basic concepts of statistics and visualization technics to prepare us for the following lessons.
Everything you need to study this lesson is on the ilearn platform, but if you have any questions or want to comment, use the forum or send me an email directly to javier.valdes@th-deg.de.
I will update the course before the beginning of the session and will provide you with the Slides before we start.
See you on Friday
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In this second block, you will see how to work in R. This includes understanding its logical structure. This will allow you to be more confident when working with complex data and to differentiate between the different types of data structures in R
Once we have seen this introduction to R from a data structure perspective, we will explore how to quickly generate graphs in R! This is of great importance when doing descriptive analysis and generating hypotheses!-
Recordings and invitations to Zoom
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Articles for next week Assignment:
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Need some practice? Check this exercises and their solutions
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Resources
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data
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Generating graphs in R is not difficult thanks to the functions that come installed as standard in R. Are you ready for the next level?
Generating graphics that are attractive is a whole different thing, for that, you have to keep in mind what kind of audience, the time available, how receptive they will be to your message and even how their mind works! In this third block, we are going to cover all this and learn how to generate high-quality graphics using ggplot2.
See you on Friday!
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Slides
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Assignment:
Based on what you learned in class and Peng's chapter "Exploratory Data Analysis with R" generate an exploratory analysis of the data included in the mtcars dataset. Use a Notebook document that includes the plots and some explanations about the analysis and the results.
The deadline for submission is Thursday, April 23rd at 23:59 CET.
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Aditional support material
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The first part of the course is coming to an end and it's time for us to put everything we have learned to the test. Are you ready?
In this section, you can find all the necessary information on how to develop this final work of the course. You can also send me your work using the link below.
How fast the time has passed! I hope you enjoyed the course.
For me, it has been a pleasure to share what I know about data visualization!
Best of luck!
Javier-
DEADLINE: 08.July
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You can rate the course Data Visualization Part 1 (Prof. Javier Valdes) following this link:
https://tinyurl.com/ye5nu7aj
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Here you find all necessary information for the final project of this course. The zip file contains 5 files:
1) 10_DataVis_Part2_Project_description.html is a description of the tasks for your final project. In a nutshell you are asked to write code to re-create the shown example figures and you need to answer a text exercise.
2) 10_DataVis_Part2_Project_template.ipynb is a notebook that you can use as a template to start your project.
3) 10_project_data_annotation.csv is a data file with dummy genomic annotation that is needed for the visualization exercises.
4) 10_project_data_signals.csv is a data file with dummy signal data that is needed for the visualization exercises.
5) Wong_NatMet_2012_pencil_and_paper.pdf is the paper for your text exercise.
Use Jupyterlab/Jupyternotebook to work on your project. Export your final notebook to an html and upload your exported html on iLearn.
Deadline: July 19th 11:55 pm CET
Good luck!
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Final Project Part 2 Assignment