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Course Summary

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Fundamentals of R
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Course Summary
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The course is part of this learning path

Fundamentals of R
course-steps
11
certification
3
lab-steps
1
description
1
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Course Summary
Overview
DifficultyBeginner
Duration1m
Students6
Ratings
5/5
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Description

Course Description 

The last section offers a brief summary, and provides you with some next steps in your journey in R.  

Learning Objectives 

The objectives of this module are to provide you with and understanding of what you should look in to next in your journey with R.   

Intended Audience 

Aimed at all who wish to learn the R programming language. 

Pre-requisites 

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 

Understanding mathematical concepts will be beneficial 

Feedback 

We welcome all feedback and suggestions - please contact us at qa.elearningadmin@qa.com to let us know what you think. 

Transcript

- I hope you enjoyed learning about the fundamentals of Arc. Going forwards, I hope you continue using all the data structures. You now know the difference between an array and a list, and that a data frame will factorize character columns. Remember that creating plots can use a simple function, with many parameters, in order to create layers. To continue your development, QA offers courses in big data, data science, machine learning, and statistics for data analysis. Thank you.

About the Author

Students238
Labs1
Courses11
Learning paths1

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.