Reiter
About this training
Introduction to `R`: Data analysis and Visualisation
Data science is one of the most exciting fields around, and the R statistical language is one of the best ways to learn and practice it. In this course, you will learn how to do data science using R from the ground-up. You will start by learning the basics of the R language and the RStudio programming environment. You will then cover topics ranging from loading data from external files, to cleaning and organising data, to conducting basic statistical analyses and creating striking visualisations.
Learning objectives
At the end of the course, you will become comfortable with the R environment and be able to read, understand and write R commands, allowing you to implement and interpret a workflow for your own data analysis:
Data science is one of the most exciting fields around, and the R statistical language is one of the best ways to learn and practice it. In this course, you will learn how to do data science using R from the ground-up. You will start by learning the basics of the R language and the RStudio programming environment. You will then cover topics ranging from loading data from external files, to cleaning and organising data, to conducting basic statistical analyses and creating striking visualisations.
Learning objectives
At the end of the course, you will become comfortable with the R environment and be able to read, understand and write R commands, allowing you to implement and interpret a workflow for your own data analysis:
- interact with the R environment
- import and summarize data
- explore data with graphs
- test statistical hypothesis
- GCB, UNIBE staff, students and externals
- Participants must bring own laptops
- No prior knowledge in R is assumed
- Basic statistical knowledge is required for simple statistic metrics
- Installed R and R-Studio (www.r-project.org, www.rstudio.com/products/rstudio/download) or email course responsible for virtual access
- The training alternates between short theoretical introductions and interactive programming sessions.
- A certificate with 2 ECTS credit points for participants who have attended the whole training and successfully presented their project.
- PD Dr. Michael Schulte-Mecklenbeck, Dr. Dirk Wulff, PD Dr. Sigve Haug (responsible)
Practical information (time, location ...)
Time : 2019-02-13-15 and 2019-03-01 (presentation day) 9:30 - 17:00
Location : Main Building, Room 105, Hochschulstrasse 4, University of Bern
Training language: English
Participants : Max 25
Registration : Mandatory, till December 15th only for GCB students
Coaches : PD Dr. Michael Schulte-Mecklenbeck, Dr. Dirk Wulff, PD Dr. Sigve Haug (responsible)
Fee: Free of charge for GCB students
Certificate: 2 ECTS certificate for attendance and completed practical work
Location : Main Building, Room 105, Hochschulstrasse 4, University of Bern
Training language: English
Participants : Max 25
Registration : Mandatory, till December 15th only for GCB students
Coaches : PD Dr. Michael Schulte-Mecklenbeck, Dr. Dirk Wulff, PD Dr. Sigve Haug (responsible)
Fee: Free of charge for GCB students
Certificate: 2 ECTS certificate for attendance and completed practical work
Schedule and content
Start Bootcamp: https://dwulff.github.io/Intro2R_Unibe/
For this bootcamp it will be adapted slightly to the new days below and the participants.
2019-02-13 Wednesday
9:30 – 11:00 Introduction to R
11:00 – 11:15 Break
11:15 – 12:00 Interactive session
12:00 – 01:00 Lunch
13:00 – 13:30 Data & functions – Learn about R’s data formats
13:30 – 14:45 Supervised exercises
14:45 – 15:00 Break
15:00 – 15:30 Data I/O – Learn how to read and write
15:30 – 17:00 Supervised exercises
2019-02-14 Thursday
9:30 – 10:30 Data Wrangling – Learn how transform organize data
10:30 – 10:45 Break
10:45 – 12:00 Supervised exercises
12:00 – 01:00 Lunch
13:00 – 13:30 Data Analysis – Learn how to aggregate data and compute simple statistics
13:30 – 14:45 Supervised exercises
14:45 – 15:00 Break
15:00 – 15:30 Statistics – Learn how to run statistical tests
15:30 – 17:00 Supervised exercises
2019-02-15 Friday
9:30 – 10:30 Plotting – Learn how do publication-ready figures
10:30 – 10:45 Break
10:45 – 12:00 Supervised exercises
12:00 – 01:00 Buffet
13:00 – 13:30 Plotting – Learn how customize figures
13:30 – 14:45 Supervised exercises
14:45 – 15:00 Break
15:00 – 15:30 Reports – Learn how to create dynamic reports using Markdown
15:30 – 17:00 Supervised exercises
For this bootcamp it will be adapted slightly to the new days below and the participants.
2019-02-13 Wednesday
9:30 – 11:00 Introduction to R
11:00 – 11:15 Break
11:15 – 12:00 Interactive session
12:00 – 01:00 Lunch
13:00 – 13:30 Data & functions – Learn about R’s data formats
13:30 – 14:45 Supervised exercises
14:45 – 15:00 Break
15:00 – 15:30 Data I/O – Learn how to read and write
15:30 – 17:00 Supervised exercises
2019-02-14 Thursday
9:30 – 10:30 Data Wrangling – Learn how transform organize data
10:30 – 10:45 Break
10:45 – 12:00 Supervised exercises
12:00 – 01:00 Lunch
13:00 – 13:30 Data Analysis – Learn how to aggregate data and compute simple statistics
13:30 – 14:45 Supervised exercises
14:45 – 15:00 Break
15:00 – 15:30 Statistics – Learn how to run statistical tests
15:30 – 17:00 Supervised exercises
2019-02-15 Friday
9:30 – 10:30 Plotting – Learn how do publication-ready figures
10:30 – 10:45 Break
10:45 – 12:00 Supervised exercises
12:00 – 01:00 Buffet
13:00 – 13:30 Plotting – Learn how customize figures
13:30 – 14:45 Supervised exercises
14:45 – 15:00 Break
15:00 – 15:30 Reports – Learn how to create dynamic reports using Markdown
15:30 – 17:00 Supervised exercises
About ScITS
The Science IT Support is there to boost your research by supporting you solving computing challenges.
Your code doesn't compile, you need more computing power, more storage, a data mangament plan and
so on - drop an email, call or pass by our office.
Our webpage is scits.unibe.ch.
Your code doesn't compile, you need more computing power, more storage, a data mangament plan and
so on - drop an email, call or pass by our office.
Our webpage is scits.unibe.ch.
