Fundamentals of R
The course is part of this learning path
This module will introduce you to the R programming language and the RStudio Integrated Development Environment. You’ll also look at some useful tools available in RStudio
The objectives of this module are to provide you with an understanding of:
- How to download and install the R programming language
- How to download and install the RStudio IDE
- The different panes in RStudio
- How plots are formed in RStudio
- How to add comments in RStudio
- Useful keyboard shortcuts in RStudio
Aimed at all who wish to learn the R programming language.
No prior knowledge of R is assumed
Delegates should already be familiar with basic programming concepts such as variables, scope and functions
Experience of another scripting language such as Python or Perl would be an advantage
Having an understanding of mathematical concepts will be beneficial
We welcome all feedback and suggestions - please contact us at email@example.com to let us know what you think.
- Hello. My name is Canal and I'll be taking you through the fundamentals of R. My background includes having worked as a data scientist at a credit management company. I investigated their loan portfolio, which was focused on Italy. This required me to understand the Italian legal process and utilizing the programming language R, forecasting cash flows, timings. Prior to this I've worked in various data-focused roles within finance. For example, updating the models of industries with new discount curves or, another example, migrating trades into a cloud-based platform. I've also worked at the government as a statistician where I developed a database which monitors and models the emerging levels of council tax data. My formal education includes a Masters in statistics and an Undergraduate in mathematics. On this learning path, you'll be learning about the programming language R. We will start by installing both R and R studio which is an IDE to help you use R. We will spend some time learning about data structures. And most importantly, vectorization. We conclude the course with understanding how to input and output from R.
Kunal has worked with data for most of his career, ranging from diffusion markov chain processes to migrating reporting platforms.
Kunal has helped clients with early stage engagement and formed multi week training programme curriculum.
Kunal has a passion for statistics and data; he has delivered training relating to Hypothesis Testing, Exploring Data, Machine Learning Algorithms, and the Theory of Visualisation.
Data Scientist at a credit management company; applied statistical analysis to distressed portfolios.
Business Data Analyst at an investment bank; project to overhaul the legacy reporting and analytics platform.
Statistician within the Government Statistical Service; quantitative analysis and publishing statistical findings of emerging levels of council tax data.
Structured Credit Product Control at an investment bank; developing, maintaining, and deploying a PnL platform for the CVA Hedging trading desk.