Observing a Mango Public R Training Course

January 12, 2016
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(This article was first published on Mango Solutions, and kindly contributed to R-bloggers)

R Courses

 

 

by Paulin Shek

As a new Mango Consultant, I had my first experience of a Mango training course a few weeks ago. I observed Aimée Gott @aimeegott_R teach a 2-day public training course – “Introduction to R for Analytics”.  Feedback is recorded from all Mango training courses and Aimée is one of the highest scorers so she is an ideal person for me to learn from. Due to the fact that I’d never considered training as a career before joining Mango, I found the experience to be fascinating on multiple levels, and it gave me much food for thought for the train journey home.

The first thing that I noticed was the pace of the course. Aimée spent a long time on the introduction – the chapter that I’d skipped when I first read through the training materials! But as with all things that Aimée does, this was purposeful. The introduction covers, amongst many other things, websites and forums related to the R community, especially websites like stackoverflow or other places to get help. The aim of the course is to equip people with the necessary programming skills as quickly as possible, and a very important part of that is to be able get help and continue learning after the course ends.

However, it didn’t end there; I made many other observations about the pace of the course throughout the two days. The pace of the second day increased in comparison to the first. On the first day, a lot of time was spent ensuring  the data types and vector manipulation were fully understood but the second day we got stuck in to learning about statistical functions in R, followed by the package ggplot2. Again, there was rationale behind this: “By ensuring people have a strong understanding of the basics, they will be more confident looking back over the material covered to clarify their understanding”.

Director of Mango, Rich Pugh, summarises this really well with the following analogy: “Our R training courses lead attendees through a corridor with many exciting-looking doors heading off that corridor. There’s a temptation to look in each door but we prefer to get people to walk along that corridor to the end on day one, so that when we look back into each exciting door on day two, and beyond, they understand how to use the contents.”

Though the particular class that I was in was quite small, there was still a mix of backgrounds in the room. Aimée was able to adjust the pace appropriately and she also made sure there were advanced exercises set aside for the fast learners.

I have a background in Computer Science and my university days are not too far from memory yet, so I also made many comparisons between the two styles of teaching. The more striking was the difference in the content of the course. Though the difference itself is quite subtle, this dramatically changed the objective of the course. At Mango, we teach what we know will be useful to a specific audience, with an aim to get course attendees started and programming as quickly as possible. University courses tend to be more general, with more focus on theory and lectures rather than practical application and interactive classes.

It was also a really enjoyable day for me. I met some really interesting people and it’s always nice to hear what other people are doing (or planning to do) with R. From this, we were able to better identify the needs of our class and adapt the course material accordingly, i.e. spend more time on the sections relevant to the most people.  In this case, several members of the class were planning to use R through Tableau’s R integration. Since we know that Tableau has some great graphing features, Aimée spent less time on ggplot2, focussing on “what” can be done, rather than “how”. Then, she spent more time on the dplyr package and statistics.

The predominant take home from this experience was the adaptability and consumer-focus of Mango training courses. The course content has a real business focus, with the aim of applying the programming knowledge as soon as possible outside of the classroom.  We also really take interest in the individual needs of our attendees and try to tailor the course around them.

The next public “Introduction to R for Analytics” course starts on the 26th Jan and I highly recommend that you book your place now!

Email [email protected] to book your place.

Find out more about our R training courses, click here.

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