Teaching at the forefront of technology—as we at Udacity are fortunate to do—is both exciting and challenging. To fulfill our promise to our students, it’s critical we maintain the up-to-the-minute relevance of our content at all times. Given how fast technology changes, this process is pretty much ongoing, every day. Sometimes the changes are minor; an upgrade here, a new version there. Other times, the opportunity presents itself to engage in a significant program overhaul.
A Complete Path to a Data Career
The recent launch of our Data Foundations Nanodegree program presented just such an opportunity. The addition of this new program has made it possible to now offer a complete path to a data career. But, changes to other programs in our data ecosystem—which includes the Business Analyst and Data Analyst Nanodegree programs—were necessary to fully optimize this path for our students.
We’re very excited today to share details with you about new changes we’ve made to the Data Analyst Nanodegree program.
When choosing a career, there are safe paths to pursue, and there are risky ones.
A career in data offers the best of both worlds. On the one hand, it is a secure choice—demand for data talent continues to increase, and shows no sign of abating. With data skills in your toolkit, you’re going to be in demand in virtually any industry. On the other hand, it’s a brave new world out there. We’re producing massive amounts of data, and that data is making amazing new things possible. But, the methods and strategies we’re having to continually invent to harness all that data means the future is continually being redefined in real time. Those on the data front lines are at the forefront of technological progress.
The good news is, that no matter which route you take—the secure one, the risky one, or something in between—there are ample career opportunities out there for anyone interested in data.
But, how do you actually get started?
That’s where this guide comes in. At Udacity, we’re extremely fortunate to collaborate with some of the most forward-thinking companies in the world, and we work with some of the most innovative thinkers and creators in the data space. Our hiring partners represent some of the best career opportunities in the field, and are a critical source of information about what companies are seeing in their data hires.
We’ve marshaled all these resources together to create this guide for you, and all of our expert contributors offer unique perspectives and experiences. If you’re interested in pursuing a career in data, this is your complete guide. To access the guide, just enter your email address below:
The exponential rise of machine learning is as much a result of technological advancement as it is the active community growing around it. This includes researchers working on core algorithms, as well as practitioners who are pushing the boundaries of how machine learning can be applied. It also includes an increasing number of machine learning enthusiasts with atypical backgrounds who are joining the conversation, bringing in diverse experiences and points of view.
Discovering and Attracting Machine Learning Talent
The increasingly symbiotic relationship between companies that need machine learning expertise, and data science competition platforms like Kaggle, has greatly impacted how rapid advancement is being achieved. This relationship has also changed the hiring landscape. Companies today face ever-increasing pressure to innovate in order to remain competitive, and they are pursuing comparatively unconventional means for discovering and attracting new talent in order to maintain their edge. The need for machine learning talent is so great, that companies are looking far further afield than once they might have.
The pursuit of educational inclusion brings us into close contact with an absolute wealth of wonderful organizations—motivated communities united around the noble goal of ensuring that any and all aspiring learners have the unfettered opportunity to pursue self-empowerment through education.
Women Who Code
Women Who Code is just such an organization, and we were recently able to work with their Silicon Valley chapter on an event that we hosted here at Udacity HQ.
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.
“Machine Learning is everywhere.” This is a phrase we see often these days, and it’s pretty close to a genuine truism. Netflix, Amazon, Siri, Pandora, the list goes on. But it’s not just entertainment and media. It’s also everything from the post office to healthcare to traffic to security. Really close analysis suggests that, for a great many of us, virtually every moment of our lives is touched at some point by Machine Learning.
Is this a good thing?
Didi Chuxing may be one of the most electrifying companies in the world right now. The Chinese ride-sharing service just received a $1 billion dollar investment from Apple, and is poised to grow significantly in the coming year. Which is why we are thrilled to partner with Didi on the global Di-Tech Algorithm Competition, which boasts a $100K prize for the winner, and perhaps more exciting, the opportunity to join the Didi Research Lab and work on innovations that will affect hundreds of millions of riders.