Data Analystics using R and R Studio

ABOUT THE COURSE
This is a hands-on course to train engineers and managers about the methods and tools of data
analytics. The course will cover a broad range of practical methods for analysis and modelling from
data. The great advances of information acquisition and storage technology has resulted in a new age of data deluge which offers some unprecedented challenges and opportunities. On the one hand, it can be overwhelming to deal with and make sense of the vast amount of data, on the other hand if the huge datasets can be properly utilised, it can offer extremely useful intelligence on the processes from which the data is recorded. A professional who can take advantage of vast amounts of data would be able to deliver great value to his/her organisation through optimisation of processes, through enhanced situational awareness, and through discovery of new insights. In this course we introduce the scientific basis of analysing large datasets for fun and profit.

There would be hands-on tutorial sessions on R, a statistical modelling software. Some exposure to
programming would be helpful but it is not a prerequisite. The course will introduce R and RStudio
and cover a wide range of data mining and machine learning topics like classification trees and cluster analysis. These techniques are commonly used by researchers to represent knowledge and aid decisionmaking. The course is intended for practising engineers, researchers, consultants, and managers with a numerate educational background.

PROGRAMME

Day 1
08.30 – 09.00 Delegate Registration
09.00 – 10.30 Lecture 1: Introduction to R and RStudio. This lecture will
introduce the programming language R and one of its most popular
graphical user interfaces, RStudio..
10.30 – 10.45 Break
10.45 – 12.15 Lecture 2: R basics (logical variables,vectors, character strings,
factors, matrices, dataframes, lists). This lecture will show you the
most common data types in R and show how you can work with
each one.
12.15 -13.30 Lunch
13.30 – 15.00 Lecture 3: Data Management in R. This lecture will focus on
showing you how you can access data sets, subset them, merge
them tohether and how you can import and export data in R.
15.00 – 15.30 Break
15.30 – 17.00 Lecture 4: R graphics. This lecture will show you various data
visualisation techniques using base R graphics and the ggplot2
package.

Day 2
09.00 – 10.30 Lecture 5: Classification trees in R (Part I). This lecture will focus
on one of the most commony used techniques used . We will apply
these techniques in R and analyse its output.
10.30 – 10.45 Break
10.45 – 12.15 Lecture 6: Classification trees in R (Part II). During this lecture
participants will work on a real-world data set and they will need
to apply the previously-learned techniques in order to answer
specific questions.
12.15 -13.30 Lunch
13.30 – 15.00 Lecture 7: Hierarchical-based clustering techniques in R (Part I).
This lecture will introduce the idea behind partitioning cluster
analysis and the k-means algorithm. We will apply these
techniques in R and analyse its output.
15.00 – 15.30 Break
15.30 – 17.00 Lecture 8: Hierarchical-based clustering techniques in R (Part II).
During this lecture participants will work on a real-world data set
and they will need to apply the previously-learned techniques in
order to answer specific questions.

CVs of Lecturers
Dr Charalampos (Charis) Chanialidis Charalampos (Charis) Chanialidis is a Lecturer at the
University of Glasgow in the School of Mathematics and Statistics. He is also the programme director of the parttime online distance learning programmes (PgCert/PgDip/MSc) in Data Analytics within the School.

These are the largest online programmes in the University comprised of 155 students from 41 di fferent countries. These programmes are targeted at those that are already in employment since there is a strong interest in quali cations which are viewed as improving subsequent employmnet prospects, and there is an enormous demand for data analytics expertise. Before that, he was a Post Doctoral Research Associate at the University of Glasgow in the School of Mathematics and
Statistics and the Urban Big Data Centre, working with Marian Scott and Adrian Bowman.
He also was a Post Doctoral Research Fellow at the University College Dublin in the School of Mathematical Sciences, working with Nial Friel and a PhD student of Statistics at the University of Glasgow in the School of Mathematics and Statistics. His supervisors were Ludger Evers and Tereza Neocleous and the title of his thesis is Bayesian mixture models for count data. He obtained his
MSc degree in Statistics and Operational Research at the University of Athens. His research interests include:  Bayesian Inference, Computational Statistics, Markov Chain Monte Carlo Methods, Machine Learning.

WHO SHOULD ATTEND
Engineers, scientists, and managers can benefit from this course. Personnel from assets and transportation management companies, risk analysts, oil and gas companies, classification societies and engineering firms can all benefit from this broadly applicable course.

BIM Training Inverness, Glasgow, Edinburgh, Aberdeen, Dundee, Perth and onsite courses throughout the UK.