Software Carpentry 1.2020
31 August - 1 September 2020Software Carpentry 1.2020 https://www.denbi.de/training/821-software-carpentry-1-2020 https://tess.elixir-europe.org/events/software-carpentry-1-2020 Educators: Rabea Müller, Konrad Förstner, Till Sauerwein (ZB MED/Associated Partner) Date August 31 - September 01 2020 Location: Online Contents: Data, Shell, Git and GitHub, Python: Day 1 Before Pre-workshop survey 09:00 Data Intro 10:30 Morning break 12:00 Lunch break 13:00 Shell Lessons 14:15 Afternoon break 15:30 Wrap-up 16:00 END Day 2 09:00 Python Intro 10:30 Morning break 12:00 Lunch break 13:00 Git Intro 14:15 Afternoon break 15:30 Wrap-up 15:45 Post-workshop Survey 16:00 END Learning goals: Get basic knowledge of the programming language python, the unix-shell, automating tasks, the version control software git and the cloud service GitHub, Prerequisites: Attendees must use their own laptop with the following software already installed: Gitbash (Windows) / Shell (Linux) / Terminal (MAC OS) , Anaconda3 and Git. Every attendee also needs a GitHub account. Keywords: programming, automation, version control, reproducible science Tools: Shell, Git and GitHub, 2020-08-31 09:00:00 UTC 2020-09-01 17:00:00 UTC de.NBI Köln, Köln, Germany Köln Köln Köln Germany    workshops_and_courses  
Computational genomics course for hands-on data analysis 2020
23 - 25 September 2020Computational genomics course for hands-on data analysis 2020 https://www.denbi.de/training/806-computational-genomics-course-for-hands-on-data-analysis-2020 https://tess.elixir-europe.org/events/computational-genomics-course-for-hands-on-data-analysis-2020 Educators: Altuna Alkalin, Verdan Franke, Bora Uyar (RBC/deNBI-epi Scientists from Berlin) Date: 23-25 September 2020 Location: Online Contents: The general aim of the course is to equip participants with practical and technical knowledge to analyze single cell RNA-seq data. With this aim in mind, we will go through unsupervised machine learning methods to analyze high-dimensional data sets, and move on to statistical methods developed to analyze bulk RNA-seq. Lastly, we will introduce analysis techniques used for single cell RNA-seq. There will be theoretical lectures followed by practical sessions where students directly apply what they have learned. The programming will be mainly done in R. Day 1: Intro to machine learning & data visualization for genomics Day 2: Bulk RNA-seq analysis Day 3: Single cell RNA-seq analysis Learning goals: The course will be beneficial for first year computational biology PhD students, and experimental biologists and medical scientists who want to begin data analysis or are seeking a better understanding of computational genomics and analysis of popular sequencing methods.r Prerequisites: Some statistics and R programming experience will be good to keep up with the course. Practicals will be done in R. Keywords: Computational genomics, RNA-seq, Machine learing, Tools: R/Bioconductor 2020-09-23 09:00:00 UTC 2020-09-25 17:00:00 UTC de.NBI Berlin, Berlin, Germany Berlin Berlin Germany    workshops_and_courses  
Computing Skills for Reproducible Research: Software Carpentry Course 2020
19 - 23 October 2020Computing Skills for Reproducible Research: Software Carpentry Course 2020 https://www.denbi.de/training/789-software-carpentry-course-2020 https://tess.elixir-europe.org/events/computing-skills-for-reproducible-research-software-carpentry-course-2020 Educators: Renato Alves (HD-HuB) Date: 19-10-2020 - 23-10-2020 09:00-18:00 Location: Online Contents: Computation is an integral part of today's research as data has grown too large or too complex to be analysed by hand. An ever-growing fraction of science is performed computationally and many wet-lab biologists spend part of their time on the computer. Many scientists struggle with this aspect of research as they have not been properly trained in the necessary set of skills. The result is that too much time is spent using inefficient tools when progress could be faster. This course provides training in several key tools, with a focus on good development practices that encourage efficient and reproducible research computing. Topics covered include: Introduction to Python scripting Introduction to the Unix shell and usage of cluster resources Version control with Git and Github Analysis pipeline management Scientific Python & working with biological data Literate programming with Jupyter notebooks Learning goals: This course aims to teach software writing skills and best practices to researchers in biology who wish to analyse data, and to introduce a toolset that can help them in their work. The goal is to enable them to be more productive and to make their science better and more reproducible. Prerequisites: This is a course for researchers in the life sciences who are using computers for their analyses, even if not full time. The target student will be familiar with some command line/programmatic computer usage, will want to become more confident using these tools efficiently and reproducibly. A target student will have written a for loop in some language before, but will not know what git is (or at least not be very comfortable using git). Keywords: Programming; Command Line; Version Control; Bioinformatics; Data Analysis; Cluster Computing Tools: Python; Bash; Unix/Linux; Git; GitHub; SnakeMake; Biopython; Pandas; Numpy; SciPy; Matplotlib 2020-10-19 09:00:00 UTC 2020-10-23 17:00:00 UTC de.NBI Heidelberg, Heidelberg, Germany Heidelberg Heidelberg Karlsruhe Germany    workshops_and_courses  
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