Feedback

4.8 out of 5 average

"I enjoyed customising the training based on our needs and implementing data similar to that which we deal with. The business intelligence section of the course was very useful and the trainer ran the course at a good pace."  ES, Assistant Executive, R for Data Analytics,  May 2021
 

Feedback

4.8 out of 5 average
 

"I enjoyed the in depth look into R with Shiny, and the course has given clarity over ways to improve reports, including the use of interactive charts through plotly and highcharter." HS, Executive, R with Shiny, May 2021. 

Watch client feedback from Data Analytics and Python training courses: 

What you will learn with our R training course?
 

Clean, normalise and gather data from Excel, SPSS, .csv and other sources

• Learn the basics of tidyverse
• Perform exploratory data analysis
• Combine ggplot2 and tidyverse to calculate meaningful summaries
• Design effective data visualisations and produce them from your data
• Gain an introduction to Business Intelligence and its tools
• Gain an introduction to Web Scraping: programmatically extracting links and images from websites
• Learn about Web Scraping with HTML and CSS
• Automate scraping with functions and parallel programming
• Create, obtain and design maps and geographic data
• Visualise geographic markets interactively with the leaflet package

R FAQs:
 

Which R Training Course is right for you?

Our R for data analysts training course is designed for teams looking to replace or augment Excel, SPSS or SAS workflows with R.

It will help you gain a clear understanding of the principles of data analysis and wrangling, and how to use the versatile R programming language for data analysis.   We will cover many different data formats and topics, so you will have the skills necessary to start working in R with most types of data.

What is the R programming language used for?

R is a programming language for statistical computing and graphics that you can use to clean, analyze, and graph your data. It is widely used by researchers from diverse disciplines to estimate and display results and by teachers of statistics and research methods.
 

Why learn R ?

R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured.

This has made R, the standard language for performing statistical operations. R allows various features that set it apart from other Data Science languages. R plays a very important role in Data Science, you will be benefited with following operations in R.

You can run your code without any compiler – R is an interpreted language. Hence we can run code without any compiler. R interprets the code and makes the development of code easier.Many calculations done with vectors – R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.

Statistical Language – R used in biology, genetics as well as in statistics. R is a turning complete language where any type of task can perform.

Python or R for Data Analysis: Which Should I Learn?

If you're passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you're interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.
 

R Vs Python: What's the Difference?

R is mainly used for statistical analysis while Python provides a more general approach to data science. The primary objective of R is Data analysis and Statistics whereas the primary objective of Python is Deployment and Production.
 

is R programming in demand? 

R programmers are most in-demand, especially in emerging startups. Some of the positions that are available for the R programmers are as follows: Data Scientist. Business Analyst.