The ‘New Year, New You’ trope has been done to death but when Andy set us the task of writing three reflective blogs about our time at the Data School I found myself thinking mostly in terms of how I could make things easier for myself going forward. So in the spirit of self improvement here’s a list of some of the things that I personally found the most challenging about my time at the Data School, and how I’m going to try and face them.
DS has easily been one of the most challenging things I have ever undertaken, both academically and psychologically. You learn equally as much about yourself as you do about using Tableau and Alteryx, maybe even more (in some cases, a lot more!). It’s a fast-paced, energetic, all consuming rollercoaster ride of four months but I’d like to think that when I come out the other side I won’t just be a good analyst but also a more well-rounded individual.
Things go fast, as in blink-and-you-miss-it fast. There will never be enough time to do all the things you want to do, all the dashboards you want to make, all the datasets you want to investigate, all the blogs you want to write. Andy and co. will inspire your brain to light up with a hundred ideas a day and you will be frustrated by the fact that you know in your heart there will not be enough time to follow all of them through. It’s a terrific lesson in acceptance, and in forcing you to pick and choose what is genuinely the most important to you.
This happens regularly, especially at the beginning. It’s a side effect of learning so much in such a short space of time. On the surface it may appear that other people’s brains are not melting (see the Fishbowl Effect below) – ignore the pretence, theirs will be too but maybe not at the same rate as yours, or over the same things. Even though most DSers come from such a wide range of backgrounds and experiences it can still come as a surprise to learn that the things you take completely for granted will be the most difficult for other people, and vice versa.
The Fishbowl Effect
This follows on from brain melt. The Data School bowl is filled to the brim with clever fish. Just by virtue of that you are forced to up your game, big time. It’s beyond easy to forget that outside the bowl is a whole world of people who are not lucky enough to be getting this kind of training. At some point Impostor Syndrome will try and come for you. Hard. For me, being in an intense learning environment where a large proportion of your immediate peers are at least a decade younger than you and fresh out of uni was incredibly intimidating. If you can honestly, genuinely get through the four month training period without at least once (if not once a week!) thinking “Oh god, why am I here? Everyone else gets it and I don’t” then the drinks are very much on me. Trust in the process. Some things click immediately, some later, some with practice, some with hard graft (and some without) and some even just seem to subconsciously find a place in your brain and bring you a delightful little unexpected surprise. It’s good to remind yourself frequently that everyone was picked to do this job for a reason, that each person brings a different set of skills and that yours are just as valuable as theirs. Also that, as with everything worthwhile, the learning never actually ends.
My big challenge. My career until this point has been that of a librarian and a researcher. I am pretty much hard-wired to ask ‘why?’ and try to find explanations. I see interesting things and want to know why. Why is that anomaly in the data? What’s the context for this? Why? Why? Why? All the time. I’m not saying that this is in any way a bad thing but there are many potential rabbit holes for a naturally inquisitive mind to fall down when looking at datasets and making visualisations. My dear DS4 colleagues have on occasions enjoyed my attempts to discover why there is an apparently vast availability of tofu at the farmers markets of Arizona compared to the rest of the U.S. (still wondering about that one), why Brussel Sprouts were more expensive in 2010 (very cold temperatures at harvest time) and how many hours of flight delays occurred as a result of confiscated weapons at various airports. Rabbit holes appear in front of you on a daily and sometimes hourly basis and not falling into all of them is a skill. One I hope to become more proficient at.
Learning To Ask For Help
Another hard-wired trait. I’m still working on this one. There’s some debate over who said the quote on the left but it’s one that I used to live by, and now one which I have had to try my hardest to abandon. There’s no getting away from it, DS forces you to suck it up and at times admit to yourself that you need someone to help you to understand things. This means opening your mouth and saying loudly and clearly that you don’t understand and that you need help. This is difficult and at times excruciating, especially when you feel that you should be fully capable of doing things by yourself. Ultimately though, after the first time it gets progressively easier and ends up being one of the most worthwhile parts of the whole process.
So, that’s some of the challenges covered….next up, how to be successful at the Data School.