Getting Started with R Software for Data Analysis-M2

2024GSR

Engels

Description

R is a flexible environment for statistical computing and graphics, which is becoming increasingly popular as a tool to get insight in often complex data. While in some ways similar to other programming languages (such as C, Java and Perl), R is particularly suited for data analysis because ready-made functions are available for a wide variety of statistical (classical statistical tests, linear and nonlinear modeling, timeseries analysis, classification, clustering, ...) and graphical techniques.

The base R program can be extended with user-submitted packages, which means new techniques are often implemented in R before being available in other software. This is one of the reasons why R is becoming the de facto standard in certain fields such as bioinformatics (Bioconductor) and financial services.

This course introduces the use of the R environment for the implementation of data management, data exploration, basic statistical analysis and automation of procedures.

It starts with a description of the R GUI, the use of the command line and an overview of basic data structures. The application of standard procedures to import data or to export results to external files will be illustrated.

Creation of new variables, subsetting, merging and stacking of data sets will be covered in the data management section. Exploration of the data by histograms, box plots, scatter plots, summary numbers, correlation coefficients and cross-tabulations will be performed.

Simple statistical procedures that will be covered are:

comparisons of observed group means (t-test, ANOVA and their non-parametric versions) and proportions
test for independence in 2-way cross tables and linear regression (focusing on the R-implementation of the statistical methods that are the subject of other modules of the statistics series)

Finally, installing new packages and automation of analysis procedures will also be discussed.

Practical sessions and specific exercises will be provided to allow participants to practice their R skills in interaction with the teacher.

Target audience

This course targets professionals and investigators from diverse areas with little to no R-programming experience who wish to start using R for their data manipulation, data exploration or statistical analysis.

Course prerequisites

The course is open to all interested persons.

Knowledge of basic statistical concepts and experience with other programming languages are considered advantages, but not required for learning the R language.

Exam / Certificate

There is no exam connected to this module. If you attend all four classes you will receive a certificate of attendance via e-mail at the end of the course.

Microcredential

This module is part of the microcredential 'Data Analysis in R: Basics and Beyond' that consists of three modules:

Getting Started with R Software for Data Analysis
Leverage your R Skills: Data Wrangling,Plotting & Reporting

If you are planning on registering for all two modules, consider enrolling for the microcredential instead. Read more...

Type of course

This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a session on campus.

Schedule

October 21, 24, 28 & 31, 2024, from 5.30 pm to 9 pm.

Venue

Faculty of Science, Campus Sterre, Krijgslaan 281, 9000 Ghent, Building S1, 3th floor, 3.1.

Course material

Access to slides and data files.

Fees

The participation fee is 770 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations.

Industry, private sector, profession*: € 770
Non profit, government, higher education staff: € 615
(Doctoral) students, unemployed: € 385

*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.

Registration

To register, add the course below to your shopping cart and proceed to checkout.

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UGent PhD students

Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.

By registering for a course or event organized by the Science Academy, you agree to the cancellation procedure that you can find on our website.