This post is inspired by experiences as a learner and conversations with current data practitioners
2020 has been the year of adapters. With that, a lot of people have pivoted into tech careers; some of which are data related. There are a LOT of bootcamps online, and some of you may have even completed a college degree aimed towards analytics, data science, and other forms of information science. I myself went to school for this career pivot, and here are some things I have learned, and continue to learn throughout this journey.
You can never stop learning. This is not to say, become a professional student by taking all courses available online. You will never finish, and want to start applying this knowledge. It is about keeping up in a focused way. Everyday, there are new innovations in data fields. If you want to stay competitive in the job market, especially as a minority of any kind, you have to keep learning. A lot of times, there is a false promise that a degree or bootcamp makes you marketable and prepares you for your job. If anything, what we should take away from those structured instructional courses are they are only where we learn how to learn. This was made apparent after analysing my gaps between what I was interested in doing and what it took to actually do it. I made a list of the common skills needed for my desired role and checked my knowledge to see what needed a boost. Speaking of what you want to do…
Data is everywhere! That makes information sciences a broad field with many avenues through which to enter. If you selected a bootcamp for data science or machine learning, that is just scratching the surface. The degrees are the same even when they come by different names (data science, data analytics, applied economics, business intelligence ). So if you are so lucky to identify the stage at which you enjoy working with data the most, focus on that lane. Like I mentioned earlier, innovations (even ones we don’t know how to manage yet) happen everyday. If you really enjoy working in that segment of data, make sure you are aware of where you want to expend energy. This KD Nuggets article* does a great job at explaining each role involved in data science and the common skills used. They have images with the TL;DR version of each role, so don’t give up as soon as you see how much you have to scroll just yet !
*The article ending that endorses DataCamp is not endorsed by me. Google it. You’ll know why, and then decide for yourself
Note that this is just the technology vantage point of data. Depending on the domain and/or industry you work in, the experience of data science may be different. Some social scientists I have had the chance to interact with mentioned that they don’t really code a lot and have used R and not Python. Academia prioritizes research so the tools may not always be the same. If you hear STATA, don’t duck! This brings me to my next point. The tools!
This might be what Trixie Mattel, the skinny legend, calls controversial yet brave. To a certain extent, do not marry any type of technical tool. You can get into a committed relationship with a bunch of them, but do not say no to opportunities because of tools unless it is a complete pivot from what you want to do.
Case in point: I love Java! Credit to two wonderful women who introduced me to it, and have helped me keep up - Dr. Han-fen Hu of UNLV and Ms. Angie Jones, a Java Champion and master inventor. However guess what? I like data mining, cleaning, wrangling, all of that smoke - and machine learning, especially natural language processing. Guess what you can’t use for all of that to keep up in the industry.. Yes, beloved Java. So pivot I did. Java has now become the long time friend I will always keep up with, but it looks like there is a lifetime commitment ceremony coming up for me and Python. Given the commonality of its use in data science and machine learning, there are a lot of free resources to learn from.
All of this to say, if you enjoy doing something in programming or data, don’t be afraid to leap into those tools. A data analyst may just enjoy making lovely visualizations and R, PowerBI, and Tableau with a little SQL and Python may be where destiny lies. Go for it!
Do not limit your job search by clinging to what you know (remember, learning is good). Expand your knowledge where your interests are, and the worst that can happen is you can apply for more jobs! I do realize that this is a somewhat priviledged take to have, given that not everyone can afford to wait too long to earn money on a career pivot. So here is where we remember not to reinvent the wheel and learn from other practitioners with a shared experience and/or background.
If you take nothing else from this long soliloquy or are scrolling to find some sensible takeaway, THIS IS IT! Network dear data lover, network!!
They do not teach you this anywhere! Some of the best interaction, code help, community, and job leads have come from Twitter. There is a Tech Twitter and Black Tech Twitter community that is strong. In fact, all the best job leads I had came from there (sorry, LinkedIn). I just had a code environment setup issue today and within two or three interactions, I got ideas and solved my problem!
More questions today 🙈— Sia Seko | Maya Moore Started It 🇹🇿 (@siawayforward) September 13, 2020
Having a "module not found/ import not resolved" error on Python with a venv, and have activated and installed the module in that venv (the versions + pip are compatible with my system and user versions on my machine).
Any ideas, Pythonistas?🙏🏽
This is only one example. Twitter is great because it combines the casual nature of human interaction with a community that is multi-faceted. You won’t only learn about your field, but others that exist and see that you are not alone in your journey. You get to celebrate small wins, big wins, and cheer others on when things are not going so well. This is the place where I learned about Diversify Tech, the best resource for underrepresented folx in tech. Another great community you can join or interact in for a network is DEV. You get to learn from users - anything from career tips to anything with a code snippet on it.
I should say, LinkedIn has worked for others. It just didn’t work for me as much as Twitter, and I have 11 months worth of job searching to be able to compare with others.
In closing, I hope you take away some thoughts from this; primarily that you should always have a gap analysis list to see where you are at compared to where you want to go, be open to different things, work in a data segment you love (if you can), and NETWORK!
Happy data journey