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Introduction to R - Data Analysis and Programming

Duration:

5 days

Day and time:

Mon - Fri, 10am-5pm

Next course: Starting date
Monday, 12 Feb 2018
Future courses: Upcoming dates

Course overview

Related subjects

Computing, Data Analysis, Programming

Department Psychology
Tutor

Will Lawrence

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.

I really liked that the tutor adjusted to the class needs, e.g. we learnt to do advanced plotting. Knowledge on building statistical models in R from scratch was particularly useful.

The course was vey useful. We have learnt new methods: data analysis (using cluster, the basic commands that run the cluster), syntax, analytical algorithms...We have studied in detail statistical analysis using shared scripts.

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:

  • Data analysis, reading in data, data exploration and filtering
  • Vectors, arithmetic, recycling
  • Graphics and advanced graphics
  • Analysis workflow
  • Making your own functions
  • Linear modelling
  • Object-orientated programming
  • Principal component analysis

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. 

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Future courses

Upcoming dates:
Monday, 12 Feb 2018 - Friday, 16 Feb 2018
Tuesday, 8 May 2018 - Saturday, 12 May 2018
Monday, 13 Aug 2018 - Friday, 17 Aug 2018
Monday, 5 Nov 2018 - Friday, 9 Nov 2018

How to Apply

Please click on the date of the course you'd like to attend below. You'll be taken to Eventbrite, which is our booking system:

All enquiries can be sent to Teemu Toivainen at air@gold.ac.uk (020 7078 5468)

Cancellations

Cancellations up to 14 days before the course start date will incur a 20% cancellation fee. For later cancellations, or non attendance, the full course fee will be charged.

Discounts

  • 10% if you are taking two courses in consecutive weeks
  • 20% UK students
  • 25% UK Law & Society Association (UKLSA) Members
  • If five people register from the same institution for the same intake, the fifth place is free
  • Goldsmiths students, staff and alumni - email us for current discounts

Fees

£750

Course requirements

  • A laptop - Windows, Mac or Linux with R installed (help with installation is available. All software required is freely available to download and install)

Optional software:

  • Rstudio
  • Tinn-R
  • Sublime Text 2

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