Back when I was in college, I had a lunch meeting with the chief financial officer and founding partner of a hedge fund. I was in my junior or senior year back then and never had any prior relationship with this person. I had merely emailed him out of the blue.
My roommate at the time had told me that she was amazed that I could, without hesitation, email someone so successful and ask to meet them for lunch – and that the person would actually say yes!
I’ve continued this habit of reaching out and meeting with successful people ever since then. And by successful, I mean those who are well-respected or well-recognized in their field. That includes people in senior management, well-known public speakers, and authors of widely read books.
Reaching out to successful people like this makes some people anxious though.
That’s how my roommate had felt when I had met with the hedge fund partner. That would’ve made her self-conscious, she said. She knew it was a smart thing to do, but she had that nagging voice in her head that told her she’d somehow offend or annoy the person.
That voice would ask her, “why would someone so successful want to talk to me?”
It’s that question that keeps people from networking with those who would make all the difference in their career.
People often make machine learning out to be something from a science fiction novel. It sounds like teaching a computer to become sentient or something even more outlandish.
In reality, machine learning is usually just statistics, but it's a very specific use of statistics. In fact, my graduate course on machine learning was called “statistical learning.” (I must say that “machine learning” is certainly better branding though.)
But how exactly is machine learning different from other statistics?
There’s two key differences:
We care more about predicting an outcome and less about explaining how that outcome happens*
We write computer programs to improve those predictions
Everyone wants to be a data scientist, nowadays. It’s the “sexiest job of the 21st century,” according to Harvard Business Review. It's intellectually rewarding, pays well, and has consistently strong job growth.
The hard part, though, is landing your first data scientist job. It seems every company has that "two years experience" requirement, which makes it a challenge for newcomers to break into the field.
It took me five years after graduating with my undergrad degree to land my first data scientist job. I know others who transitioned into the role sooner, but I had to network and grow my skill set through graduate school before I was ever seriously considered for the job.
Like most analytics jobs, data scientist roles require a solid foundation in technology, with the addition of statistical expertise. It simply doesn’t matter how good of a strategist or communicator you are – you can’t succeed in a data scientist role without those skills.
Ironically, long-term success requires the opposite focus. After you mastered the technical and statistical expertise, you have to actively work to expand your soft skills. That will ensure your talents translate into a positive impact for your stakeholders.
There's a long list of things to learn, but it's helpful to simplify what you should focus on to achieve this goal. Down below are six specific skill groups that will help you either land a data scientist job or improve in your existing data scientist role.
Do you know what p-values really means? It turns out it's closely related to the concept of the "null hypothesis." Both of these concepts are frequently misunderstood by analysts, data scientists, and researchers alike.
In my latest Towards Data Sciencearticle, I interviewed Dr. Robert Montgomery, an assistant professor in my data science grad program. He helps explain what the null hypothesis really means, what the p-value tells us about its probability, and when we can claim causality in statistics.
Many people believe that the best way for data science to have more impact on an organization is for the data scientists to learn better storytelling skills. Are they right?
In my Towards Data Science article, I interview data scientist Jasmine Samuel and Brent Dykes, author of Effective Data Storytelling, about how to use data storytelling to make a bigger impact with data science.
I recently read a book called the Good Jobs Strategy. It was written by an MIT business school professor named Dr. Zeynep Ton. In the book, she explains why retailers who invest more in their employees outperform their competitors.
These companies, she says, are following the “good jobs” strategy.
I highly recommend reading the book, if your organization is investing more in data. Much of her advice easily translates to the major challenges that face organizations trying to build enterprise wide data solutions.
Industry insiders have always claimed that the Great Recession was a good thing for marketing analytics. They believed that marketers would invest more in data to prove their value to their clients at a time when most companies are cutting their marketing budgets.
I think many of us assumed the COVID-19 downturn would do the same thing for marketing analytics. However, that may not be the case this time around.
I’ve noticed that many peers at different companies have lost their jobs in the past few weeks because of the COVID-19 downturn. It might be that the crisis forced marketers to evaluate whether the costly analytics practices were really worth the money and work involved. Or it might be that we (the data professionals) never delivered as much value as we thought.
In reality, both sides probably share the blame. Neither the analysts nor the marketers have ever really approached marketing analytics the right way.
For the past ten years, the marketing industry invested heavily in building data warehouses, implementing advanced tracking, and hiring data professionals to analyze and report this data.
But along the way, marketing analytics began turning into snake oil.
The benefits were widely overstated for the amount of money invested. The solutions built were flimsy on quality. And the goals were often improbable (if not impossible).
I don’t think the analysts or the marketers intentionally did something dishonest. I think they simply did what marketing people always do – sell the benefits of a product.
The main problem was that marketers may not have been the right people to use this particular product.
For starters, it pays well. The pay is often higher than other more technically challenging jobs in the data science profession. According to Glassdoor, the average salary for a Tableau developer is $81,514, which is higher than database developers, statisticians, and data analysts. (See footnote for more salary details.)
The work-life balance is also good. You can often work remotely and jobs in this field rarely require much overtime. If you become really good and market yourself effectively, you can work on a contract-by-contract basis, thus improving both flexibility and income.
Tableau development is also in high demand. After working for a single year as a Tableau developer, you will get calls from recruiters frequently. Sometimes multiple calls within a single week.
And best of all – it’s the most creative job within business intelligence. There’s no other job in this field that allows you to exercise more aesthetic flare and storytelling abilities than Tableau development.