R programming and predictive analytics. Learning R with confidence
£230.27 – £1,258.14
R programming and predictive analytics. Learning R with confidence

R programming and predictive analytics. Learning R with confidence

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Senate House

London, United Kingdom

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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 jumpingrivers@gmail.com

Course overview

  • 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 London location.
  • Lunch provided

Oct 3rd: Introduction to R

This is a one day intensive course on R. This course will be a mixture of lectures and computer practicals. The main focus will be to introduce fundamental R concepts.

No prior programming knowledge of any kind is assumed. This course is suitable for a wide range of applicants e.g., biologists, statisticians, engineers, students.

The course will be structured as follows:

  • Introduction to R: A brief overview of the background and features of the R statistical programming system.
  • Entering Data: A description of how to import and export data from R.
  • Data types: A summary of R's data types.
  • R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results.
  • R Graphics: Creating, editing and storing graphics in R.
  • Manipulating data in R: Describing how data can be manipulated in R using logical operators

Oct 4th: Programming with R

This is a one day intensive course on R. The course will be a mixture of lectures and computer practicals. The main focus of the course is R programming techniques, such as functions, for loops and conditional expressions.
The course follows on from the Introduction to R course. It is assumed that all students have attended this course (or have equivalent skills). This course is suitable to a wide range of applicants e.g., biologists, statisticians, engineers, students.

The course will be structured as follows:

  • Vector operations: details of R's vectors operations.
  • Conditionals: using "if" and "else" statements in R
  • Functions: what is function is, how are they used, and how can we construct our own functions.
  • Looping in R: an introduction to the concept of looping in R. In particular "for" and "while" loops.
  • The apply functions: apply, tapply and other members of the apply family.

Oct 5, 6th: 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.


Oct 7th: R for Big data

This course is a practical introduction to dealing with large data sets in R. We'll cover hardware, programming with Rcpp, out-of-memory datasets and SparkR.

It is expected that participants have previous R experience, in particular, they are familar with the topics in the programming with R course.

The course will be structured as follows:

  • Hardware: a brief overview of CPU, memory sizes and RAM. The benefit of switching to the cloud.
  • Rcpp: leveraging C++ for slow operations
  • The remainder of the course will consider three classes of data sets:
    • Large in-memory data sets: the dplyr package
    • Out of memory: ff and the big memory suite of packages
    • Distributed data sets: Spark

Participants are encourage to bring their own datasets and associated problems to the event.

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Senate House

London, United Kingdom

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