You can split the long list of items that anyone dealing with RA should know into two categories: must-knows and nice-to-knows. It is not always clear which is which. Obviously you need to have a foundation in telecoms; don’t ask which part – all of it! End-to-end assurance of all revenue streams, right? You will learn that while a basic reconciliation is straightforward, obtaining the information you need is not; you might have to dig through documents, sanitize data and spend hours in sessions to get a grasp of an unknown data flow. It would be nice to be able to use something as simple and handy as statistics to get as much out of the data you fool around… er, work with every day. Stats would be an added tool in your skillset; allowing you to look at data in multiple ways and draw conclusions but most probably will only come in use after a given point of putting down the basics. In addition, basic stats and data analysis are key not only in RA but in any data intensive job.
As such I decided that my next course would be “Foundation of Data Analysis” on EdX. So far the course has been great; straightforward material that is easy to absorb and, in addition, I’m learning another tool: R using R-studio. The course consists of readings (in pdf), then lessons (video clips) and finally practical exercises on R. Each week a new topic is introduced or builds on the previous week’s lesson. R is a really powerful tool with intuitive commands and features. It seems like it will be a useful addition to the RA analyst’s toolkit, because it allows you to quickly extract information about a dataset (obviously the size of the data matters – although it appears that it can be used for large sets). The course ends in February and in the meantime I am also re-taking the Intro to Linux course which has been updated. I did not manage to get far with this one the first time around and I will delve into the reasons why below.
You know how you planned to take “lesson 4” at 5AM next morning or that night session at 9 to 11PM? This sounds good in theory, but there are so many things that will go unexpectedly, or even as expected, to derail your schedule: monthly reporting at work, child with the flu, the need to open a laptop and get something out the way for work instead of taking a lesson. All you need to do is keep at it and add motivators. So what I did for Data Analysis was register for the “verified” version of the course, which is 50 dollars for a verified (i.e. ID verified) certificate. This adds more pressure, makes it more “formal”.
I have to admit that I registered for 4 courses at once when starting out; in addition to the first Linux course I took Cultural Anthropology, English grammar and Science of Happiness. Needless to say, I completed none. It’s better to take a small bite and manage it, than to dive into the deep end when there are daily priorities battling for your attention and energy. Another issue is that each course is different in the way its structured. Some require reading, some only to watch the video lesson, others require practice on a keyboard (you can watch a lesson on a tablet but when you need to sit at a keyboard you do need to set time apart).
So for now I am taking it a step at a time; Data Analysis, then Linux. I am resisting taking some other courses which are “interesting” in favour of those that I could actually add to my resume. The courses that are simply interesting will have to wait.
Note: Udacity has an excellent nanodegree for Data Analysts, but this requires a huge investment in time (and money 200 USD/month). It looks perfect if you want to go down that path in a more serious manner and have the time to do so.