Data Analysis and Visualization in R

Module IN2339

Credit: 6 ECTS.

Moodle: https://www.moodle.tum.de/course/view.php?id=68693

Lecture Script: https://gagneurlab.github.io/dataviz/

If you have any questions, please contact us via Email to teaching-gagneurlab@in.tum.de

When and where?

This lecture is given in the winter term.

Virtual Lectures every Tuesday from 14:00 - 16:00 via Zoom. Additionally, recordings will be available online.

Exercises:

The exercises consist of two sessions per week.

- Tutorials will only held in presence: 1h30 sessions, in which exercises are solved and interactively supported by tutors. (Multiple sessions are held at different times throughout the week, where each student attends one.)
- Exercise courses will held in presence and are streamed online: In this plenary session on Friday 8:00 - 10:00, (Rudolf-Diesel-Hörsaal, MW 2001, Boltzmannstr. 15) solutions to the homework are presented and discussed.

Please make sure that you register twice in TUMonline, for the lecture and also for the separate exercise.

Teaching material will be shared via the platform Moodle. To access Moodle, a registration to the course via TUM online is needed.

Description

This module for students in bioinformatics, master students of Data Engineering and Analytics, and master students of Biomedical Computing teaches methodologies and good practice of data science using R. The lecture is structured into three main parts, covering the major steps of data analysis:

1. Get the data: how to fetch, and manipulate real-world datasets. How to structure them ("tidy data") to most conveniently work with them.

2. Look at the data: basic and advanced visualization techniques (grammar of graphics, unsupervised learning) will allow students to navigate and identify interesting signal in large and complex datasets and formulate hypotheses.

3. Conclude: concepts of statistical testing will allow concluding about the raised hypotheses. Also methods from supervised learning will allow to model data and build accurate predictors. Each week, the lecture is accompanied with exercises. During the exercises, combinations of the concepts seen during the lecture will allow performing more involved data analysis tasks. Students generate report that embed code and analysis. One more advanced case study complements the course. Many examples will stem from applications in genomics, but no pre-requisite in this domain is necessary.

Required background and Computer setup

Experience with programming of any language. The theoretical aspects of data analysis are kept low in this module. However, basics in probabilities are required. See our companion module "Statistical modelling and machine learning" to complement it.

Chapters 13-15 ("Introduction to Statistics with R", "Probability" and "Random variables") of the Book "Introduction to Data Science" https://rafalab.github.io/dsbook/ make a good refresher. Make sure all concepts are familiar to you. Check your knowledge by trying the exercises.

Bring a laptop with RStudio installed, a free programming interface for the R language.

Who can attend

The module is an elective module for many study programs. Among others, it is in the catalogue of:

BSc and MSc Bioinformatics
MSc Informatics
MSc Information Systems
MSc Data Engineering and Analytics
Medicine students
BSc and MSc 'Management and technology'

Topics

R programming basics, report generation with R markdown Importing, cleaning and organizing data (tidy data) Plotting and Grammar of graphics Unsupervised learning (hierarchical clustering, k-means, PCA) Drawing robust interpretations (empirical testing by sampling, classical statistical tests) Supervised learning (regression, classification, cross-validation)

Evaluation

The final exam is a 2 hours written exam. The mark will be the one of the final exam plus bonus points for the home works and case studies. The exact exam date has not yet been set. We will announce this as soon as we know.

Teaching team

This lecture is given by a team of scientists with long experience in high-dimensional data analysis in the field of genomics: Prof. Julien Gagneur and members of his lab.