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R programming course, predictive analytics & building an R package. Learn R...
Mon, 21 Nov 2016, 09:00 – Wed, 23 Nov 2016, 17:00 GMT
October 2016 (Senate House, London)
We are very excited to invite you to this intensive week on data science.
There is a variety of tickets available for multiple days and individual days. Discounts are applied for multi buy, student, academic and charity staff. For four or more attendees from the same company it is often cheaper to have a bespoke package for your company please contact us directly at firstname.lastname@example.org
- Comprehensive set of printed notes;
- Each course comes with its own R package that contains exercises and solutions;
- Attendance certificate from Jumping Rivers;
- Networking opportunites;
- Small class sizes with a deadicated presentor able to answer your questions as we go;
- Central Leeds location.
- Lunch provided
November 21st & 22nd: Predictive analytics
This is a two day intensive course on using the R programming language for predictive analytics. This course will be a mixture of lectures and computer practicals.
It will be assumed that participants are familiar with R. For example inputting data, basic visualisation, basic data structures and use of functions. Attending the introduction to R course will provide a sufficient background.
The course will be structured as follows:
- Introduction to analytics: a general introduction into analytics and some of the techniques that are in common use.
- Simple regression problems: simple and multiple linear regression and model diagnostics.
- Classification: KNN, clustering, logistic regression, Linear Discriminant analysis and associated diagnostics.
- Model selection: various model selection procedures, subset selection, shrinkage.
- Advanced regression techniques: polynomial regression, splines, local regression, GAMs, trees and random forests.
November 23rd Building an R package
This is a one day intensive course on building an package. This course will be a mixture of lectures and computer practicals. The main focus will be getting a working R package ready for distribution. It is assumed that all applicants have a basic knowledge of R.
Participants can bring their own code or they can use the provided example code to write a fully functional R package.
- Why create an R package.
- What is in an R package.
- Writing documentation with roxygen.
- Creating packages with rstudio.
- Distributing your package.
Participants will need to bring their own laptop.