My Journey from Marketing to Data Science

When I started my first analytics role, my only formal education was a bachelor of arts degree in Communication. I had never taken a statistics course, I didn’t know Python or R or even SQL. I had never run a hypothesis test. Six years later, I’m a Data Scientist with a multinational corporation, and hold a master of science degree in Data Science.

How?

Like many wide-eyed 18-year-olds, I kind of stumbled about when picking a college major. I was good at math and had done a little bit of programming, so my first attempt was Computer Science. But my first programming class was a major turn-off. I found it boring and impossibly hard to follow, and despite passing the class, I didn’t remember any of the material or concepts I’d learned. I didn’t think that was the right career path for me. Eventually, I landed on Communication, because I envisioned myself working in a glamorous career in public relations or journalism.

After graduation, I learned very quickly that the majority of jobs in the field of communication are not glamorous. My first job was promoting accounting conferences. My second job was public relations & marketing communication for a non-profit healthcare system, so while not glamorous, it was at least rewarding. I made my way into a digital marketing role with that organization and started dipping my toe in data analysis. Then I left for a digital marketing role in commercial real estate — less rewarding, better pay, and more opportunities for career growth.

And that’s where I made my way into a marketing analytics role, despite, as I opened with, having zero formal training in anything related to analytics. But I knew a lot about marketing, I had shown some interest in data analysis, and I had proven that I could teach myself a thing or two — I was comfortable digging into web and social media analytics and had cobbled together enough insights via Excel to impress my bosses to the point that they moved me into the analytics role during a team reorganization.

Domain Knowledge & Business Acumen

When trying to break into a new career path, most folks focus on the hard skills — for data analysis and analytics, they focus on SQL, Python, R, PowerBI, Tableau, statistics, maybe even machine learning. And those skills are great ones to have in your toolbox! But they are merely tools. And most businesses don’t necessarily care what tools you have — they care how you apply whatever’s in your toolbox to solve their business problems.

I had proven that I could use Excel to analyze the data available to me (web analytics, social media analytics) and derive business insights and recommendations. That’s all that mattered to my bosses — not how I got there, but that I had something insightful to share.

Technical Skills

However, once I was in a role focused solely on data analysis — and working under someone much more experienced in that field than I was — I quickly realized I could be so much more impactful if I had better tools in my toolbox. You can do a lot in Excel (especially if your datasets aren’t huge), but you can do so much more — and work more efficiently — in almost any other tool.

I was able to learn more tools on the job — specially Power BI (thanks to formal training from Microsoft covered by my employer), A/B Testing (via Adobe Target), and I got more in-depth in Adobe Analytics, which I had already been using for a few years.

However, once my boss started teaching me R, I realized how vast my skill gaps were. I knew nothing of statistics, and I was not comfortable writing code. I needed more formal training than what she could provide while balancing our actual jobs.

Graduate School

I started looking into formal training — bootcamps, certificates, and graduate programs. The bootcamps and certificates were cheaper and shorter, but would they be enough? Also, would the investment be worth it? My company offered tuition reimbursement, but only for accredited programs. Which basically meant a formal college degree.

Ultimately, I decided a master's degree was the way to go for my situation. For one thing, I liked the thought of having that credential on my CV. And due to limitations based on accreditation status, I could either get a few hundred dollars to put toward a bootcamp, or $5,000 annually to put toward a college degree. And let’s face it, a master’s degree from a recognized institution would carry a lot more weight than a bootcamp from a pretty much no-name organization. I figured if I was going to put in the work, aim high, and don’t leave myself wishing later on that I had aimed higher.

The Payoff

When I transitioned from marketing to analytics, I knew I was moving into a line of work that paid more. However, because I was inexperienced in analytics, my former employer felt that my current pay (from my marketing role) was appropriate for my level of experience relative to analytics pay ranges. This was further motivation to upskill so I could land a more advanced role.

When I enrolled in my master's program, I had been in my marketing analytics role for just shy of two years. I was doing school part-time, and after about a year, I had gotten through enough of the basics — specifically statistics, databases/querying/SQL, regression, and programming in both Python and R — that I felt like I was in a good spot to land a better job.

After aggressive job searching for about six months, I received three offers, and accepted a product analytics role with a large US-based tech company. This also came with a roughly 35% pay raise. Minus tuition reimbursement, my degree would “pay for itself” after about one year in my new role — and two years before my expected graduation date.

More Growth

The other benefit to the new job was that as I continued to learn more advanced things in my graduate program, I had more opportunities to apply them than I would have in my previous role. This allowed me to not only keep my skills fresh but also made the topics in my class more approachable — I had a good frame of reference for what we were learning and how to apply it in the real world. Eventually, my role evolved into a Data Science role, focused on experimentation and defining metrics and KPIs (key performance indicators).

Takeaway

There is no one path to a job in analytics or data science. I have met hundreds of folks working in this field, and they have a variety of backgrounds.

I’ve met other folks like me who came from a non-STEM background like marketing, communication, and business, or social science fields like psychology/sociology.

I’ve met folks who came from finance/accounting, or physics and engineering, and yes, lots of folks from math/statistics and computer science.

I’ve met folks in this field from all kinds of academic backgrounds, from folks with STEM PhDs to folks who never finished a college degree but were able to learn the skills on their own and work their way up starting from customer service roles in tech.

If you’re interested in a career in data analytics — you can get there, no matter where you are starting from. Check out my roadmap.

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