People love to make pronouncements about a year. 2016 is the Year of … what? Data? That certainly doesn’t seem possible, given that we’ve been talking about data—both big and otherwise—for some time now. And yet, there was Glassdoor, rating Data Scientist as the #1 Best Job in America for 2016. They weren’t the only ones either. CareerCast.com put Data Scientist at #1 as well. These two studies have been so extensively cited that it’s essentially become a given that Data Scientist is one of THE hot jobs of today, and of the future as well. So how do you prepare for a data science career? Let’s begin by looking at the skills you’ll need.
According to the team at Crowdflower, based on an analysis of literally thousands of LinkedIn job postings, there are five in-demand skills you need: SQL, Hadoop, Python, Java, and R. For perspective on this study, we can read Seamus Breslin’s article on KDNuggets, in which he riffs on the Crowdflower findings, and provides some excellent additional insight. To Hadoop, Python, and R, for example, he adds Data Visualization, Statistics, and … Creativity! Which is excellent, and as you’ll see later in this post, also very important.
The team at Smart Data Collective did an extensive LinkedIn study as well, and their findings largely mirror the Crowdflower results, though they took the interesting step of also looking at variations by level of experience. As an example:
We found that Senior and Chief Data Scientists are less likely to report technical skills. Instead, they list skills like business intelligence, leadership, strategy, and management. These are all skills that one has to develop if they’re going to move into a role responsible for turning data-driven insights into strategic action.
For the full results of their study you can read The State of Data Science at rjmetrics.com. You’ll find a nice high-level summary there, which notes the following as top data science skills: Data Analysis, R, Python, Data Mining, and Machine Learning.
Universal Skills, Unique Skills
For an especially deep look at data science skills, consider reading “Top 10 Skills in Data Science” by Bob Hayes on the Business 2 Community site. Hayes’ research is both rigorous and impressive, and most intriguingly perhaps, he breaks down shifts in skills needs by role. For example, among data professionals who self-identify as having a business manager role, 86% have project management skills. Whereas for data professionals self-identifying as holding developer roles, that number drops to 46%. In his summary, the author notes that some skills appear to be universal regardless of role—included on this list are skills like: managing structured data, math, data mining and visualizations tools, and product design and development. He also notes skills that are largely unique to specific roles—successful developers, for example, have programming skills, whereas researchers possess machine learning skills. The one item that tops virtually every list for every role? Communication. A key reminder that data must be communicated effectively, in order to be effectively acted upon.
Softer Skills, and The Spirit of Data Science
It is in fact abundantly clear that comparatively “softer” skills like communication are critical for success in data science. Linda Burtch, an executive recruiter who specializes in the quantitative business sciences, and who heads up Burtch Works Executive Recruiting, notes some of these skills in a widely-shared article entitled The Must-Have Skills You Need to Become a Data Scientist. She highlights three “non-technical” skills in particular: intellectual curiosity, business acumen, and communication skills. She also references a guest post on her site from Frank Lo, who is the Director of Data Science at Wayfair, and who cites intellectual curiosity as the “#1 Intangible” skill data scientists should possess. Why does he believe this to be the case?
“The spirit of data science is discovery.”
—Frank Lo, Director of Data Science, Wayfair
If you’re reading this post, then perhaps you’re either already a Udacity student, or are potentially considering becoming one. Either way, I’m going to guess you might especially appreciate something else Frank Lo says:
Don’t filter candidates based on degree.
There is a notion out there that the best data scientists are the ones with Ph.D’s. From my experience interviewing and evaluating candidates, it is my opinion that academic degree is the last consideration that matters.
Based on everything we’ve looked at above, and spurred on by Frank Lo’s wonderful quote, I think we can make a clear case in favor of creating your own path to a successful career in data science, through mastering key technical skills, nurturing the development of non-technical skills, and taking advantage of rapidly increasing demand for qualified talent.
The Data Analyst Nanodegree program
At Udacity, we built our Data Analyst Nanodegree program to help you do exactly that—launch a successful career in data science. We began by ensuring we had the best experts and curriculum partners possible, including Facebook. As the Executive Summary portion of The State of Data Science notes, it is virtually impossible to overrate the impact Facebook has had in the field of data science, and their contributions are critical to the quality of this program. When you add expert instructors like Diane Tang and Carrie Grimes from Google (Distinguished Engineer and Google Fellow respectively), Ryan Orban (CEO and Co-founder of Zipfian Academy), Katie Malone (data scientist at Civis Analytics, recently profiled on our blog), and our own Sebastian Thrun, you have a pretty impressive roster of talent! Plus, our project-based approach means not only do you learn the skills you need to succeed, you also build a portfolio that demonstrates your mastery of these skills. And finally, with the support of our Careers Team, you’ll get ample opportunities to prepare for a successful job search.
To give you a sense of how this program is impacting student lives, here is a recent review of the Data Analyst Nanodegree program from our site:
Udacity provides high quality lectures with many real industry cases plus tons of hands-on projects. After trials of several MOOC specializations in data science, I finally chose Udacity just because it is truly the best. I’ve immersed myself in this learning-by-doing program for the last few months. Not only I’ve gained practical knowledge and developed related hard skills through the platform, but also I’m deeply touched by the spirit of democratizing education and the value of students first. I highly recommend everyone who has time commitment to join the nanodegree and take advantage of the superb project review, coach appointments and forum/slack interactions. You will gain far more than you paid for and see yourself grow day by day.
Are you ready to launch YOUR career in data science? After all, it is The Year of Data!
The Udacity Data Analyst Nanodegree program