Three Things for Non-Data Scientists Who Love Data
Channel your inner data-loving self, even if you don’t consider yourself a coder
Every day when I open R and look at my code, I think about either how poorly it is written or try to figure out how I wrote something so complex. If you have coded before, you have had this feeling x100 over, or maybe more. It’s called imposter syndrome and it isn’t something that should ever hinder your growth as a data scientist.
But for me, the feeling is somewhat warranted. Alas, I am not a data scientist. I don’t spend my whole day in R or Python. I can’t build AI algorithms or ML models. I barely understand half the code I write (the R functions lapply and map still mess with my head constantly).
Even though I see myself as a data science imposter, I cannot stop loving data science! Give me a problem, some data, a Google browser and a few hours on a Saturday morning and I can get lost in it all, especially if it has to do with politics or elections.
So overall, the biggest takeaway here is I am not a data scientist, but I love data science. Over my many-a-year love affair with the domain, I have learned a few pointers that might be helpful for the accountant who sometimes codes in SQL; or the student who chanced upon R in class; or maybe just a normal person who decided to move beyond Excel and take up Python to get ahead in their job/ career. Well, this article is for you.
My three key pieces of advice for the non-data scientist who loves data:
1) Follow your passion & sprinkle some data analysis on top
Politics, sports, movies, art, finance, cars, books. Whatever your passion is, there is data for. Take that open data, clean it up a bit and make some cool graphs, charts or visualizations. More than that, there are packages and modules for each of these subjects too.
Data analysis is about digging deeper into certain subjects and coming to new conclusions or outcomes. Linking those new conclusions/ outcomes with your hobbies or passions is literally an amazing feeling. Whether it be creating an app that shows election results, a model that optimizes your fantasy football team or visualizing imbd movie rankings, data science can give you a whole new outlook on your passion!
2) Find the reason data analysis is relevant to your life & career
It seems like AI and ML are everywhere. Realistically, those terms have become as buzz-wordy as ‘innovation’ or ‘synergies’, and the most you might encounter AI is talking to a chatbot. But data analysis is everywhere, and you don’t need to be a hardcore coder to do it.
Being able to confidently read, clean and analyze a dataset will put you above 90% of people in the working world and help you out in almost any job. Remember when doing a vlookup() or a pivot table used to make you the Excel guru of the office? Well, today it’s just as easy to build a simple app or code an automatic chart for your team (saving you 2 hours a week). Whatever you do in your career, data will be (or can be) involved to help deliver better results. The key is to figure out how it might help your career and take some steps to get better in that single area. I will never code AI or ML, but as a management consultant and political geek, creating cool visualisations to communicate information is super important. This I achieve through cool visualizations like the one below, which helps confirm my above-average communication skills.
3) Use the data science community, don’t just code alone
This is a piece of advice I wish I followed when I first started coding, and one I wish I would follow more nowadays. As a data science ‘imposter’ I am always nervous to reach out to the broader R or Python community. However, every time I have done so I have learned a lot more about data science and how to improve my skills.
The thing to know is that you are not in it alone. Literally hundreds of thousands of people are now trying to learn to code even though their job doesn’t necessarily require it. And most of the best data scientists started out in that same position. So reach out, ask questions, attend seminars/ networking sessions, and be a part of whatever data community you feel you want to gain something from. Trust me when I say that these people are more helpful than anybody you will ever meet!
To Sum Up…
I love data. I love coding. I love solving problems. But most of all, I love not being a data scientist. So despite my imposter syndrome, I have learned so much from doing all my political visualizations, random Twitter brand sentiment analyses, or election maps, and found that sweet spot between passion and career development. You can too and you should never let the audacious idea of needing to be a data scientist stop you from evolving your life and career with data. Trust me on this and if you have any doubts send me a direct message on LinkedIn or Twitter and I will dispel your hesitation!
I am a Simulation & Strategy Consultant at Monitor Deloitte, I use stats and analytics to inform Digital Twin models that re-invent the way companies approach strategic decisions. In my free time, I’m obsessed with politics and policy, blogging about it all the time at Policy In Numbers. You can find me there or at my LinkedIn and Twitter accounts (feel free to connect or give me a follow).