Data Science For Dummies. Lillian Pierson
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Career Alternatives That Involve Data Science
Not to cause alarm, but it’s fully possible for you to develop deep and sophisticated data science skills and then come away with a gut feeling that you know you’re meant to do something more.
Earlier in my data career, I was no stranger to this feeling. I’d just gone and pumped up my data science skills. It was the “sexiest” career path — according to Harvard Business Review in 2012 — and offered so many opportunities. The money was good and the demand was there. What’s not to love about opportunities with big tech giants, start-ups, and multiple six-figure salaries, right?
But very quickly, I realized that, although I had the data skills and education I needed to land some sweet opportunities (including interview offers from Facebook!), I soon realized that coding away and working only on data implementation simply weren’t what I was meant to do for the rest of my life.
Something about getting lost in the details felt disempowering to me. My personality craved more energy, more creativity — plus, I needed to see the big-picture impact that my data work was making.
In short, I hadn’t yet discovered my inner data superhero. I coined this term to describe that juicy combination of a person’s data skills, coupled with their personality, passions, goals, and priorities. When all these aspects are in sync, you’ll find that you’re absolutely on fire in your data career. These days, I’m a data entrepreneur. I get to spend my days doing work that I absolutely adore and that’s truly aligned with my mission and vision for my data career and life-at-large. I want the same thing for you, dear reader.
https://businessgrowth.ai/), you can find free access to a fun, 45-second quiz about data career paths. It helps you uncover your own inner data superhero type. Take the quiz to receive personalized data career recommendations that directly align with your unique combination of data skills, personality, and passions.
For now, let’s take a look at the three main data superhero archetypes that I’ve seen evolving and developing over the past decade.
The data implementer
Some data science professionals were simply born to be implementers. If that’s you, then your secret superpower is building data and artificial intelligence (AI) solutions. You have a meticulous attention to detail that naturally helps you in coding up innovative solutions that deliver reliable and accurate results — almost every time. When you’re facing a technical challenge, you can be more than a little stubborn. You’re able to accomplish the task, no matter how complex.
Without implementers, none of today’s groundbreaking technologies would even exist. Their unparalleled discipline and inquisitiveness keep them in the problem-solving game all the way until project completion. They usually start off a project with a simple request and some messy data, but through sheer perseverance and brainpower, they're able to turn them into clear and accurate predictive data insights — or a data system, if they prefer to implement data engineering rather than data science tasks. If you’re a data implementer, math and coding are your bread-and-butter, so to speak.
Part 2 of this book are dedicated to showing you the basics of data science and the skills you need to take on to get started in a career in data science implementation. You may also be interested in how your work in this area is applied to improve a business’s profitability. You can read all about this topic in Part 3.
The data leader
Other data science professionals naturally gravitate more toward business, strategy, and product. They take their data science expertise and apply it to lead profit-forming data science projects and products. If you’re a natural data leader, then you’re gifted at leading teams and project stakeholders through the process of building successful data solutions. You’re a meticulous planner and organizer, which empowers you to show up at the right place and the right time, and hopefully keep your team members moving forward without delay.
Data leaders love data science just as much as data implementers and data entrepreneurs — you can read about them in the later section “The data entrepreneur.” The difference between most data implementers and data leaders is that leaders generally love data science for the incredible outcomes that it makes possible. They have a deep passion for using their data science expertise and leadership skills to create tangible results. Data leaders love to collaborate with smart people across the company to get the job done right. With teamwork, and some input from the data implementation team, they form brilliant plans for accomplishing any task, no matter how complex. They harness manpower, data science savvy, and serious business acumen to produce some of the most innovative technologies on the planet.
Chapters 7 through 9 and Chapters 15 through 17 in this book are dedicated to showing you the basics of the data science leadership-and-strategy skills you need in order to nail down a job as a data science leader.
That said, to lead data science projects, you should know what’s involved in implementing them — you’ll lead a team of data implementers, after all. See Part 2 — it covers all the basics on data science implementation. You also need to know prominent data science use cases, which you can explore over in Part 3.
The data entrepreneur
The third data superhero archetype that has evolved over the past decade is the data entrepreneur. If you’re a data entrepreneur, your secret superpower is building up businesses by delivering exceptional data science services and products.
You have the same type of focus and drive as the data implementer, but you apply it toward bringing your business vision to reality. But, like the data leader, your love for data science is inspired mostly by the incredible outcomes that it makes possible. A data entrepreneur has many overlapping traits and a greater affinity for either the data implementer or the data leader, but with one important difference:
Data entrepreneurs crave the creative freedom that comes with being a founder.
Data entrepreneurs are more risk-tolerant than their data implementer or data leader counterparts. This risk tolerance and desire for freedom allows them to do what they do — which is to create a vision for a business and then use their data science expertise to guide the business to turn that vision into reality.
For more information on how to transform data science expertise into a profitable product or business, jump over to Part 3.
Using my own data science career to illustrate what this framework looks like in action, (as mentioned earlier in this chapter) I started off as a data science implementer, and quickly turned into a data entrepreneur. Within my data business, however, my focus has been on data science training services, data strategy services, and mentoring data entrepreneurs to build world-class businesses. I’ve helped educate more than a million data professionals on data science and helped grow existing data science communities to more than 650,000 data professionals — and counting. Stepping back, you could say that although I call myself a data entrepreneur, the work I do has a higher