How I landed a Data Analytics job without a relevant degree and during COVID-19.
Making the transition from a non-technical background into the Data Analytics field (and how you can too!).
For those that have followed my journey the past seven to eight months, you know that my goal was to transition from a non-technical Marketing background — and former Graphic Designer — to the Data Analytics world. Well, after roughly 8 months, I did just that. To give back to the community that has welcomed me as a career coach throughout the process, here is a rather straightforward post of my whole journey from beginning to end; I hope this helps motivate those that are on the fence with making the career transition and/or think that they cannot achieve this given the current state of the job market with COVID-19 affecting businesses/positions.
To start, here’s a simple timeline of my professional history, starting from the very second I received my Bachelors degree, all the way to my present state of accepting and starting my first analytics job at The Home Depot as an HR Analytics Analyst. Let’s dive in.
How would you simplify the steps you took into an easy-to-read format?
As a career coach that hates complexity in explanations, I am all about simplifying these processes into steps in order to exclude the fluff, include the essentials, and to ultimately help readers easily digest the information.
Step 1 — Join an online bootcamp (recommended).
Enroll in a flexible online coding bootcamp that will leave you with a solid portfolio to showcase your newfound analytics skills in the necessary tools (Excel, SQL, Tableau, Python). I personally enrolled in Thinkful, and if you’re curious as to my experience with them, check out my series on my journey with them here. Bootcamps like that offer very flexible schedules for you to balance taking the course, and having an existing full-time position. They also have very cost-effective payment methods that don’t break your wallet, compared to universities and traditional education tuitions. The alternative is to take some independent courses from personal instructors on sites like Udemy in regards to these topics. The only caveat with that is that you need the discipline to create your own portfolio without the guidance of a bootcamp and its mentors. However, if there’s a will, there’s a way. I am not here to say that a bootcamp is absolutely necessary for a successful transition.
Step 2 — Utilize other resources online.
Consider other online resources to supplement your learning throughout your data journey. I believe in learning these topics through multiple perspectives. This includes Youtube videos on various coding topics, Udemy courses to supplement your bootcamp’s program, etc. It’s important to not limit yourself to one source for what you’re learning. It’s through these various resources that you will gain more understanding in these topics and to ultimately be more well-rounded when applying it to the real world work environment.
Step 3 — Start applying to jobs as soon as you can.
Apply to positions as soon as you gain the proficiency in some of the more common “entry-level” skills, such as Excel and SQL. The program I went through specifically teaches Excel, SQL, Tableau and Python. However, what I found was that most junior level analyst positions mainly only require Excel and SQL. Therefore, I wasted no time in applying to various positions as soon as I gained proficiency in those two concepts, and after having some projects under my belt.
Step 4 — Keep applying and learn from each interview.
Continue to apply to positions everyday. Every. Day. But more importantly, strive to learn something from each interview. I firmly believe that no matter how bad an interview plays out, there is always something to learn from the experience. I personally started a collection of interview questions from each interview I’ve gone to. That way I can come up with better ways to answer certain questions that I might have not been able to answer eloquently or with enough information in the failed interview, and to better prep for the next time it might come up again.
Final thoughts.
Take all this from someone who also had doubts when he first started this seemingly long journey. Although I enjoy Marketing, I truly found my passion in being in a position that is data-driven. And although I lacked the coding and statistical background when starting out, with hard work and continuous determination, I was able to make this transition. It’s through understanding who I was beforehand that I can truly say that if I can do this, anyone can; no matter how irrelevant you think your educational or work background is.
As always, thank you all for taking the time out of your day to read my blog. If you enjoyed this post, and you enjoy anything regarding analytics, career advice, and motivation, consider following my account and checking out some of my other articles. Also, feel free to connect with me on LinkedIn if you would like to ask me any additional questions in regards to any of these topics and/or need a mentor. I am always looking to help in any way I can. Thank you again and have a great day!
