I’ve never liked the title Data Scientist. It violates the golden rule of the art of labeling. Be descriptive. Sure, there is some allure to the mystery it invokes when you talk about your job to friends and family.
But the “ooohs and ahhhs” are hard to follow-up with a coherent explanation of what exactly what it is you do. I think calling ourselves data scientists is a net-loss to the profession, and its not just for this rather petty reason. But before I dive into the meatier problems, let me put my solution front and center.
Let me try…
In Part 1 of this piece I argued that businesses need to ask two key-questions to become more effectively data-driven.
1. What data do we need to address this problem?
2. Is this data worth collecting?
If you haven’t checked out this post, I recommend you do before reading on.
In Part 1 I presented a scenario for my hypothetical business that curates an email list for data science related content. As a quick recap, my email list follows a freemium model where anyone can sign-up and get one article per-week delivered to them for no charge. …
Every business is constantly facing new challenges, and decision makers are tasked with addressing these challenges quickly. To face these obstacles, operators and leaders are increasingly turning towards data to determine and execute their business strategies. However, business leaders are getting frustrated when the existing data that they’ve spent time and resources to collect, clean and centralize isn’t a panacea. We’re seeing key decision makers reach a chasm between the outcomes they want, and the data they have.
The response to seeing this gap that I’ve observed throughout my career is a collective throwing of hands in the air and…
It goes without saying that the ability to code up SQL is necessary to landing and succeeding in any data science role. There isn’t any evidence that this is changing anytime soon, SQL is here to stay, so mastering this skill is an unequivocally worthwhile investment. Whether you’re looking for your first role in data science, brushing up on your SQL skills for your next round in the job market, or looking to level up your career I’ve got four principles that will help you succeed in all your SQL-related endeavors.
Let’s dive right in.
What do I mean by…
Over the last few weeks, I’ve been mulling over a decision of whether to take a new data science position or leave the field altogether. In trying to choose the best option, I didn’t find myself thinking about “where I wanted to be in 10 years” but rather was looking back 10 years, to the time I was introduced to Greg Mankiw’s 10 principles of economics. Specifically, his fourth was top of mind.
In mulling over this tenet and how it related to my decision of taking a new job in data science, I was left with the following question:
I grew up hearing stories of my audacious Dad solo-trekking through Yosemite as a twenty-something. Having an itch to be a more bold twenty-something, I decided I would follow in my father’s footsteps and embark on a solo-backpacking trip to the famed El Capitan in Yosemite Valley.
1. Educate Yourself
The first move I made is to understand what essential gear and skills I would need to make sure I came back to San Francisco unscathed. To do this, I turned to my trusty friend REI ,who provide a comprehensive set of posts on backpacking for beginners.
Tip: The single…