Training and Events
Introduction to R
Dr. Will Laurence
18/02/2019 - 22/02/2019
Goldsmiths, University of London
View in Google Maps (SE14 6NW)
Learn how to process and analyse data using many of R's powerful functions, install packages for additional functionality and produce high quality graphics for use in publications.
This course offers an intensive, hands-on introduction to the R statistical computing environment, focusing on practical aspects of data analysis. The programme is designed to give you as much practical experience as possible.
The course will cover the following key aspects of using R:
You will experience a range of teaching and learning methods, including lectures, active participation in tutorials, practical sessions, debates and discussions. You will also receive academic guidance and feedback on your progress throughout.
By the end of this course, you will be able to read in a variety of structured and unstructured datasets. You will be able to ‘clean’ data, which contain errors or are badly entered, as well as re-structuring data to make it more useful to you. By the end of the course you will have applied both linear and non-linear models on a number of different datasets to help identify and quantify important relationships between variables. You will have created publication-quality visualisations that help express these relationships visually. In your final day task you will build a predictive model based on real data concerning either: the factors that predict survival on the titanic AND/OR the factors that predict childhood bullying. This task will involve real world datasets that will require data cleaning, visualisation as well as data modelling and will demonstrate your new ability to handle and gain insight from large and unfamiliar datasets.
Those interested in large-scale data analysis and in further programming training should consider Introduction to Python in Week 2. This combination will offer a competetive edge to anyone interested in analysing, managing and working with different types of data.
Entry (no or almost no prior knowledge)
Website and registration
Data Collection, Quantitative Data Handling and Data Analysis, ICT and Software, Research Skills, Communication and Dissemination
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