Why are data skills in high demand? Because cities are struggling to fill thousands of data science jobs. Here's why New York's demand for data professionals is on the rise.
In 2009, Hal Varian, Google’s Chief Economist, told the McKinsey Quarterly that “the sexy job in the next ten years will be statisticians.” As crazy as it might seem to use the words “sexy” and “statisticians” in the same sentence, he was right.
We are, nearly ten years later, inundated by data. In fact, every day, 2.5 quintillion bytes of data are created. If this avalanche of numbers has accomplished anything, it’s in making the benefits of data analysis abundantly clear. According to researchers from MIT, “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.”
It’s no surprise, then, that data science is one of the fastest growing, most in-demand jobs in technology. In fact, since 2012, Data Scientist roles have increased by 650 percent, and this rise shows no sign of stopping. In the next two years alone, the number of available data jobs in the US will increase by 364,000 to 2.7 million.
This is one of the reasons why BrainStation is now accepting applications for a new Data Science Full-Time program. The question is…
What is Data Science?
Data science makes use of predictive causal analytics, prescriptive analytics, and machine learning to help us make predictions, and more importantly, decisions.
If you only understood about 75 percent of that sentence, let’s put it in simpler terms: data science uses math and technology to find hidden patterns (and ways to be more productive and profitable) in raw data. To find those patterns, a Data Scientist spends a lot of time collecting, cleaning, modeling, and examining data, from numerous angles (some of which have not been looked at before).
This exhaustive approach is one of the reasons why, as biostatistics professor Jeff Leek once wrote, “the key word in data science is not data; it is science.”
To find hidden patterns, a Data Scientist used a combination of techniques and concepts, including the aforementioned:
- Predictive causal analytics, which can predict the possibilities of an event in the future.
- Prescriptive analytics, which can suggest a range of actions and their associated outcomes.
- Machine learning, which is a set of algorithms that can be used on data to discover patterns and make predictions.
This isn’t to say that a Data Scientist spends all his or her time with numbers and algorithms. They can, in fact, be heavily involved in the decision-making process across departments — and to do so they will have to communicate their findings to other human beings. This is how the authors of Doing Data Science described it:
“She’ll find patterns, build models, and algorithms — some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.”
How do you Become a Data Scientist?
A Data Scientist requires a number of technical skills to excel in their field, but if we were to boil it down, they must be able to:
- Collect and store data, particularly with the use of databases, spreadsheets, and querying languages, such SQL.
- Analyze and model data sets with Python, R, Hadoop, and Spark, among other analytical tools.
- Visualize and present findings, using tools like Tableau, PowerBI, Plotly, Bokeh, and Matplotlib, among others.
There are, of course, quite a bit of non-technical skills required as well. Communication skills are fundamental — if you can’t communicate your findings to someone that does not possess your understanding of data, you’re not going to succeed. Similarly, a Data Scientist should have a strong business acumen and domain specific knowledge. What are the challenges facing the industry? What about the company? Being able to answer these types of questions will help guide the data analysis process.
What does a Data Scientist do?
A Data Scientist’s specific tasks can vary depending on the industry they’re in, and the company they work for. Generally speaking, though, he or she can expect some or all of the following daily tasks and responsibilities:
- Researching your industry and company to identify pain points, opportunities for growth, and improvements in efficiency and productivity, among other things.
- Defining relevant data sets, and then extracting data from various sources.
- Cleaning data to remove anything unusable and checking that the rest is accurate and uniform.
- Creating and applying algorithms to implement automation tools.
- Modeling and analyzing data to identity patterns and trends.
- Creating visualizations or dashboards for other members of the organization to consult.
- Presenting findings to other members of the organization.
Here’s what Jeremy, the Lead Educator for BrainStation’s Data Science Full-Time program had to say: “Data science work ranges from digging deep into the math of specific techniques, developing and implemented models to describe the data, working to automate processes and explaining results to clients. You might be coding an algorithm in the morning, and in a board meeting in the afternoon.”
To find out more about tasks and responsibilities, check out a conversation we had with Colin Fraser, a Data Scientist at CHIMP (Charitable Impact), and a Lead Instructor for BrainStation’s part-time Data Science course.
You might also want to check out our video about Mohammed, a BrainStation graduate who now works at Shopify as a Researcher.
What are the Roles of a Data Scientist?
Data Scientists can work across many different industries, and can often play an interdisciplinary role in a company. Because of this, common job titles for Data Scientists tend to be quite varied.
Here, however, are some of the most common data science job titles:
- Data Scientist
- Data Analyst
- Data Architect
- Data Engineer
- Database Administrator
- Business Analyst
- Data and Analytics Manager
There are many other variations out there, and these will continue to evolve as data analysis becomes more prevalent.
The good news is that almost all of these positions are in great demand. As we mentioned above, job listings for data-related roles are projected to skyrocket over the next two years, to the point where demand may outstrip supply. American consulting firm Mckinsey & Company claims that demand may be 50 to 60 percent greater than the projected supply of data professionals. This is backed up by the fact that employers are already struggling to fill data roles: on average, data science/analytics jobs remain open for 45 days — five days longer than the market average.
All that to say, if you have data science skills and experience, you are already in a great position when it comes to career development and progression.
What is the Average Salary of a Data Scientist?
Obviously, salary ranges can vary greatly depending on the region you’re in and your level of experience. That said, Data Scientist salaries tend to be on the high end.
In 2015, Glassdoor reported that the average salary for a Data Scientist is $118,709. Payscale, meanwhile, claims that the median pay for Data Scientists in the United States is in the area of $94K per year.
Sound good? Click here to start an application to BrainStation’s Data Science Full-Time program.