Educating Our Way Out of the Data Scientist Shortage

It’s no secret that employers are looking for data scientists. They have become the stars of the modern workforce – the most valuable employees.

Companies of all sizes have awoke to the fact that data science, by mining new insights from even decades of accumulated data sets, has the potential to drive efficiencies and increase productivity in ways never previously imagined. Simply put, it has the potential to transform businesses. From Zillow’s home price predictions to Amazon’s recommendation engines, applications of data science have become increasingly accurate, prevalent, and impactful on our everyday lives.

But while “data scientist” has been ranked the “No. 1 Job in America” for three years running now, according to careers website Glassdoor, there’s still a shortage of talent to fill the huge need of employers across every industry. In fact, according to a recent LinkedIn study, businesses across the nation need 151,717 more data scientists right now.

The need is nothing short of stunning.

This is why companies understand that they must increasingly invest in the education of their employees in order to compete in an ever-changing world. At the same time, employees need to recognize that traditional higher education just isn’t designed or equipped  to keep up with the breathtaking pace of technological developments and digital transformation that we see in business every single day. People may intuitively know that learning is a lifelong process. But the modern employees also needs to accept that that continually adding to their skill set is the best way they stay competitive in the job market.

Here’s the reality: Jobs are available. But organizations expect potential employees (and current ones) to have the skills to those critical jobs.

The advantage of this digital transformation is that it’s also changing how we think about education. And it truly can be the answer to solving the data scientist shortage within your company.

This ongoing process of learning can take place digitally and independently of location. E-learning can happen anywhere, anytime: at the workplace, at home, on the train, or in the coffee shop. The subject matter can even be adapted to the precise, tailored requirements of a company. This way, it has maximum added value for employees and employers. For example, last year the automobile company Audi launched its employee “data-camp” training focused on big data and artificial intelligence.

Even companies that specialize in data analysis have recognized their own crying need to create alternatives to the traditional training pathways. After all, they are on the front lines of the digital transformation, and their workers need to have cutting-edge skills.

For example, our customer Alteryx, which develops self-service data analysis software, offers a nanodegree that enables regular employees to become data specialists and to expand their own career opportunities. In this way, companies meet the need for data specialists, while employees sharpen their skill sets, receive additional qualifications and ultimately improve their career opportunities.

It becomes a win-win. Organizations benefit the improved effort of employees. The workers themselves expand their horizons.

Employees who have a background in computer science or mathematics – and interact with numbers, data and programming daily – are ideal candidates in terms of becoming data experts in the company. Udacity’s online course, with concrete sample projects and application examples, is usually enough to give employees the added education they need to take that next step within their own company.

But employees outside of traditional IT departments have opportunities to pursue what is known in the industry as  “Citizen Data Scientists.”The term describes employees who evaluate data but do not program the algorithms themselves. Instead, they use self-service tools. These tools enable the analysis and visualization of large amounts of data with preconfigured workflows. The advantage here is that employees usually know more about the context of the data and can bring that understanding directly into their own departments.

Data isn’t the future. It’s now. And it’s critical to every company in every industry.

Companies are looking everywhere for data scientists. They can be academically trained, educating through  internal further education programs, or this relatively new world of Citizen Data Scientists, It’s clear that businesses need all of them because we live in  a world where data is collected everywhere. It’s clear that companies need to invest in employee training to keep pace with digital transformation.

Faced with this dire shortage of talent, business leaders who want to make the most of data science can’t rely on half-measures and casual hiring processes. What they need is a strategic roadmap toward building data science skills internally and effectively upskilling their talented employees.

Stay tuned for new releases from Udacity Enterprise.

The Future of Work is All About Your Skills

The future of work won’t be about college degrees. It’ll be about skills.

That’s the new global reality shaping the job market. Highest performing organizations are reinvesting in their talent to fuel profits and business growth. By investing in training and development efforts, companies can enable their well-rounded employees to perfect their set of skills to succeed in their jobs.

The reality is that employers are looking for more than knowledge — they want skills,  top-tier tech companies such as Google, Apple, and IBM have gone public “offering well-paying jobs to those with nontraditional education.” For these and many other companies, a solid, skills-centered non-formal education is all that separates ambitious students from top-paying jobs. A formal education is no longer the best path to launching a successful career.

The skills gap is widening and companies are struggling to find the right talent. A recent Gartner research supports this premise by highlighting that companies need to shift from external hiring strategies towards their current workforces and apply risk mitigation strategies for critical talent shortages.  According to Gartner, most organizations are undergoing a digital transformation that directly impacts how they do business, yet 70 percent of employees have not mastered the skills they need for their jobs today, and 80 percent of employees do not have the skills needed for their current and future roles.

Re-skilling was also hot topic in this year’s Davos event. According to a World Economic Forum report released just ahead of the event, a total of 1.4 million US workers might lose their jobs over the next decade as a result of new technological changes and inadequate skills compete effectively. However, the report found, it will be possible to transition 95 percent of at-risk workers into positions that have similar skills and higher wages through re-skilling. The report further indicates that the rapid evolution of machines and algorithms in the workplace could create 133 million new roles in place of 75 million that will be displaced between now and 2022.

Too often, college degrees have been thought of as lifelong stamps of professional competency, perpetuating the notion that work — and the knowledge it requires — is static.  The shift to a skills-based economy enables individuals to compete for employment based on what they can do for a company. At the same time it gives companies a tremendous opportunity to more efficiently integrate continual learning into work routines and implement reskilling and upskilling initiatives.

Here at Udacity we are working with global companies to help them:

  • Launch an upskilling initiative across their company (communicate the mission, how employees can get involved, what is expected of them, duration and how success is measured)
  • Develop flexible learning journeys to help employees reach the next level and prepare for tomorrow
  • Encourage our stakeholder(s) to invest in frequent, regular communications about the employee experience
  • Work collectively to employ incentives, learning models, leadership communications, and other motivational campaigns to drive completion rate of upskilling programs.

Udacity for Enterprise provides tailored, end-to-end learning paths for your company and entire workforce. We’ll help companies choose the right learning path for their workforce and help their high-performing employees continue to gain the right skills to excel and innovate.

Let’s get started.

Bridging the AI Skills Gap Webinar Recap

Last week, we held our Bridging the AI Skills Gap webinar featuring Varun Ganapathi, head of our AI and Data Engineering and Mat Leonard, Product lead for our School of Artificial Intelligence.

The conversation centered on five key areas:

  1. AI vs Machine learning vs Deep learning
  2. How companies are using these technologies today?
  3. Skills gap and talent shortage
  4. Common use cases and outcomes
  5. How to overcome the skills gap

AI, machine learning, and deep learning are easily confused and overlap with each other. The panel did a good job of breaking down the definitions:

AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

“AI is any technology that enables a system to demonstrate human-like intelligence,” explained Varun Ganapathi. “Machine Learning (ML) is one type of AI that uses mathematical models trained on data to make decisions. As more data becomes available, ML models can make better decisions.”

Watch Webinar Recording

Today, different AI technologies are finding a place in various industries. For instance, Banking and Financial Services companies are using chatbots or virtual assistants to help customers with routine tasks like scheduling payments, automate most frequently asked questions. Predictive Analytics’ to reduce the risk of loan defaulters. Machine learning to identify patterns of transactions that might indicate fraudulent activity.

The expanding applications for AI continues to create a shortage of qualified workers in the field. AI is moving fast and enterprises need talent today. However, not just any talent. What once was a shortage of coding and software engineering expertise has now evolved into an overall shortage of skills in machine learning, robotics and algorithmic engineering.

Product Lead

“If you’re considering working in AI as a data scientist or machine learning engineer you need to find a good starting point, and it starts with knowing Python, C++, and learning mainstream deep learning libraries like TensorFlow or PyTorch,“ said Mat Leonard, Product Lead at Udacity’s School of Artificial Intelligence.

AI and machine learning are driving innovation and transformation. They are embedded in how we sift through large volumes of data and content and how we interact, connect, and buy today. They are the engines underlying many of our products and services.

Hear more from Varun and Mat about steps your organization can take to embrace AI and close the skills gap. Listen now.

How To Get Started In AI

Learning AI

Artificial intelligence (AI) enters our lives in many different ways. AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning. Deep learning is a branch of machine learning utilizing giant neural networks and massive data sets. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

Artificial intelligence is growing exponentially in all the major sectors, including health, social media analysis, self-driving cars, language processing and others. The AlphaGo victory is just one of the signs of amazing things to happen. The understanding of artificial intelligence opens lots of opportunities.

If you’re considering working in AI as a data scientist or machine learning engineer but need to find a good starting point, here are a few things to consider in your learning journey:

  1. Get your mathematics strong. You should have some appreciation of the mathematical underpinnings, especially linear algebra and calculus. Specifically, you’ll need to be comfortable with matrix multiplication and partial derivatives.
  2. To get a development role on an AI team, be sure to have at least one to two years of software development and machine learning experience under your belt. This can include building your own projects or working at a company driving key projects such as image or text classification. A great place to look for machine learning projects is arXiv where researchers often publish their papers. You can sharpen your skills by implementing models and systems from papers that capture your interest.
  3. Learn Python. This handy programming language is the tool of choice for most machine learning engineers and data scientists.  Python’s syntax is relatively easy to pick up and it has a vibrant and helpful community. The language also has excellent documentation and tons of training resources. With tools such as Jupyter notebooks and libraries like Numpy and Pandas, Python has become the first choice for developing machine learning and deep learning applications.   Outside of machine learning, Python is useful for developing websites, videos games, and more. Udacity can get you coding in Python and building your first neural network in just three months!
  4. Learn mainstream deep learning libraries like TensorFlow or PyTorch. Most deep learning systems are built in either TensorFlow or PyTorch, Python frameworks that provide APIs for defining and training deep learning models. You’ll want to be experienced with at least one of these frameworks as most AI teams are using them for research or product development. You should also consider joining the machine learning community.  

The biggest area of AI research today seeks to enable computers to make inferences from complex data. Techniques to do this are termed machine learning (ML).  AI and ML are large and rapidly-developing fields. While it’s impossible to capture their full potential in a this blog post, we’d like to invite you to Mat’s workshop on Natural Language Processing at the Global AI Conference on January 24th from 2pm – 6pm PST in Santa Clara, California.

Turkcell Embraces Digital Transformation

Turkcell Graduates
Turkcell Graduates

Digital transformation has further raised the need for change of the telco business model. Traditional telcos are almost indistinguishable—same services, different day—resulting in stagnant growth. Customers are constantly shopping around for what’s next, thanks to competition from born-digital market entrants and a growing demand for new services and immersive experiences. In an age of unprecedented disruption where brands cater to customers, telcos must adapt quickly or risk losing even long-time loyalists.

Enter Turkcell. Turkcell is a mobile phone service provider based in Turkey that also operates around nearby countries, with a total of 50 million subscribers, making it the third largest in Europe. In addition, they are listed on the New York Stock Exchange.

The company has invested in building its own digital apps and services, reaching 110 downloads, 3 million of which are from outside of Turkey. The carrier’s current portfolio covers a communications platform dubbed BiP, music platform fizy, TV platform TV+, local search engine Yaani, secure login service Fast Login and digital payments company Paycell. The company has expanded its digital portfolio an embraced the needs of its consumers.

Turkcell needed to move rapidly in a market being transformed by digitalization and needed to make sure its employees were reskilled to handle the changes it was instituting on the technology side.

Turkcell Graduates
Turkcell Digital Masters Program

The company invested in the future of its workforce and created the Turkcell Digital Masters Program. Employed by Turkcell Academy and in partnership with Udacity, Turkcell Digital Masters trained employees in data analysis, machine learning, artificial intelligence, data entry, programming and business analysis. During the 9-month period, 1,088 Turkcell employees prepared a total of 4,878 projects, dedicating 10 hours a week to the program.

Just this past Friday, November 30, 2018 Turkcell held their graduation ceremony where they announced 751 new Udacity graduates from programs spanning from Data Foundations to Artificial Intelligence.

Udacity and Turkcell have been working together since 2017. The collaboration and passion has resulted in:

  • 1,500 applications to the Udacity Nanodegree program
  • 1,088 enrolled employees
  • 751 Udacity graduates (500 attended the in-person ceremony)
  • 4,878 total projects completed
  • 19 news articles reached a distribution of 2.3M people

We wanted to congratulate all the new graduates! Udacity is proud to be working with Turkcell to help them transform their workforce.

How to Be a Champion of Change – Forbes CMO Summit Takeaways

 

CMO Summit
Forbes CMO Summit 2018

I had the privilege of attending the Forbes CMO Summit 2018 a few weeks back. It was a veritable who’s who in the world of marketing, including (in no particular order) CMO’s from Hallmark, PepsiCo, Cadillac, Visa, Microsoft, Wendy’s, Ebay, Salesforce and more.

The overall theme was Champions of Change: CMOs at the Center of Business, Tech and Cultural Innovation. During my career in marketing, which if you must know spans several decades, the role of marketing has changed dramatically. Tools and concepts have changed, the audience is more sophisticated, with lofty expectations, and our organizations are now at the center of it all, owning everything from top of the funnel to revenue to up-sell, renewals and churn. But at the heart of it, we still need to connect one on one with people. Or as Forbes summed up the conference, “data is king, but the heart rules”.

Here are my top 5 takeaways that can be applied to every role in every organization:

1.) Be authentic – It’s easy to get caught up in the crazy that is our daily lives, both at work and home. It’s also very easy to fall into the spell of doing just enough, cutting corners, and in some cases, even being lazy (or as someone once put it, “having the minimum amount of flare”). But to truly be successful, productive (and happy), you really need to be present, be yourself, and be authentic – even if that scares you. Lindsey Foy, CMO of Hallmark, articulated this in her talk at the Summit, which is similar to her Ted talk here. It’s well worth the 17 minutes to watch.

2.) People first, not customers first – This is true for B2B, as well as the obvious B2C. In order to truly engage with buyers, you need to establish rapport and trust and then build on that relationship. Treat people as citizens of your company.

3.) Ask the question “how can I/we add value to that person’s life?”. – By doing this you are focusing on the needs of your customers. I can’t tell you how many sales calls I’ve been on where the person tells me what their solution/features/benefits are without asking me about my needs. But this is also true of marketing collateral I see (and sometimes create). In other words, too focused on what it is versus what value it will bring, not just to the company, but to the end users or consumers of the product or solution.  

4.) Live the brand. Become the story. – When I was at Oracle, we had a saying, “eat your own dog food.” Sounds trite, but for me it was meaningful. We used our own software to do our job. We had specific insight to its features, how to use it, what worked, and what didn’t work. We actually ended up creating a case study on it. Can’t get any more “live the brand, become the story” than that. But by doing this, it helped us develop  the right content and programs to attract them. And let’s face it, if you’re not working for a company you believe in, you’re probably not going to be happy.

5.) Create the right environment for the above to happen seamlessly – Above all else, as leaders, we need to create the kind of environment for 1-4 to exist and thrive.

It’s not often we take the time out of our busy schedule to participate in events like this. Turning the laptop off, putting away the phone, cancelling all meetings and truly being present. For me, this was a great reminder of why I went into marketing in the first place. These takeaways and concepts aren’t new. They are not groundbreaking. They are just reminders of the ways we might have strayed and how to get back on the right path (whatever that may be for you).

So to summarize – be authentic, value customers, and live your brand. Super easy, right?!? Now go forth and be a champion of change.

Written by: Christina Del Villar, Global Head of Marketing, Enterprise at Udacity

Christina is passionate about seeing companies transform, grow  and scale, leveraging technology. With over 20 years of executive-level growth marketing experience at Fortune 100 companies and over 10 startups, she has a successful history of building teams that execute innovative go-to-market roadmaps and strategy. Christina loves working with companies that are going through a growth phase and she has the experience and industry perspective needed to take growing businesses to the next level. Her role at Webgility put Christina in a unique position to impact the e-tail industry with powerful e-commerce solutions. Her most recent role at Udacity, involves shifting the company focus from a B2C model to a B2B model. Christina also enjoys traveling, participating in endurance events, and working with various nonprofits, including Team Ronald McDonald House and Best Buddies.

AI Frontiers Conference 2018

AI Frontiers Conference
Day 1 of the AI Frontiers Conference

The annual AI Frontiers Conference is a three-day conference designed to deliver the latest breakthroughs, trends and prediction in AI to practitioners, academics, businesses and startups. The conference recently took place at the San Jose Convention Center from Nov. 9 to 11, 2018, bringing together experts from AI giants such as Google, Facebook, Microsoft, and AI rising stars like OpenAI, Uber, and Udacity. We had an opportunity to present one of the AI workshops on natural language processing, as well as attend the conference, and wanted to share our top three takeaways from the conference.

1.) AI is prevalent across industries

AI no longer refers to theoretical research at academic institutions or R&D labs; instead, it is a foundational technology that is disrupting society and driving innovations in key industries. From the way we get to work, to how doctors identify and treat diseases, AI is poised to forge a future of endless new possibilities. Some key industries taking advantage of AI include healthcare and finance. For example, in healthcare AI is used to predict diseases, identify high-risk patient groups, automate diagnostic tests and to increase speed and accuracy of treatment. It can also be used to improve drug formulations, predictive care, and DNA analysis that can positively impact quality of healthcare and affect human lives. Another key industry is Finance. Banks are already using AI to streamline their formerly manual processes for tracking data, saving time and maximizing cost benefits. The new horizon? Leveraging AI beyond internal processes to inform consumer interaction. As the finance world grows and develops with this technology, the next step is machine learning that changes and adapts to improve fraud detection and provides smarter customer service by conversing with users every day. By using AI to inform both consumer-facing and internal processes, the potential return on investment can be huge.

2.) AI redefines what it means to be human

Dr. Kai-Fu Lee has been at the center of the AI revolution for more than 30 years. For his Ph.D. thesis at Carnegie Mellon University, Dr. Lee developed the world’s first speaker-independent, continuous speech recognition system. Today, he is Chairman and Chief Executive Officer of Sinovation Ventures, as well as President of its Artificial Intelligence Institute. He spoke at the conference and his message was that AI is giving our society a wake-up call. Currently, so much of our time is spent on busywork and repetitive work, which will largely be automated in the age of AI. Yet, key attributes such as creativity and empathy cannot be substituted by machines or data. Organizations in the world of AI will require people to excel at connecting with others and gaining people’s trust. Many transactions are already occurring online, but high-end corporate sales will require the ability to build long-term customer relationships. “My advice to employees would be to become lifelong learners, always looking for the next skill and never believing that the next 10 years will be like the previous decade,” he stressed. There’s a widening skills gap between traditional and machine-augmented work, but it also creates a real need for new training, new types of experts, and, ultimately, a shift from the workforce we know to a workforce open to new possibilities, as far as new skills, productivity, and contributions by humans made hand-in-hand with machines.

3.) AI goes mainstream faster than imagined

Some of the biggest brands on the planet are placing huge bets on artificial intelligence, betting on everything from face-scanning smartphones and consumer gadgets to computerized health care and self-driving cars. It’s also worth noting that AI has quickly gone mainstream in popular consumer devices such as Apple’s Siri, Amazon’s Alexa, and Google’s Home Assistant. And this trend is happening faster than many could have imagined just a few years ago. As companies embrace the transformative potential of AI, they have been snapping up all the available talent from the relatively small pool of scientists and technicians trained in artificial intelligence, and its sub-disciplines, machine learning and deep learning. As the scarcity of people with the requisite knowledge and abilities has deepened, those companies have been cultivating efforts to up-skill AI skills across their workforce. Making a success of AI in an organization ultimately rests upon diversity: diversity of thinking, of personnel, and of skillsets. On-boarding team members from across the organization, maintaining a critical and inclusive hiring policy, and implementing a cohesive workforce transformation initiative to up-skill and re-skill personnel are vital to bridge the skills gap.

The conference provided a front-row seat of the frontiers of AI and machine learning and highlighted some extraordinary breakthroughs in various industries. As AI technologies become a reality, companies and their workforce must keep up –

And they must do so quickly.

Find out how Udacity Enterprise is helping companies transform their workforce to remain competitive. 

Preparing for AI jobs: Why Nanodegrees are the future of education

Originally published by IBM on August 23, 2018 at https://www.ibm.com/blogs/watson/2018/08/preparing-for-ai-jobs-why-nanodegrees-are-the-future-of-education/

In 2017, IBM predicted that by 2020, demand for these skills would grow by 28% (364,000 jobs) to over 2.7 million job listings. We’ve revised that prediction, as we see growth in this space closer to 45%. A Harvard Business Review article proclaimed there is a growing war being waged for people with skills for the “sexiest job” of the 21st century, the data scientist.

More businesses than ever before are looking to fill a suite of new roles in an AI-driven world:

  • Data Scientists
  • Chatbot developers
  • Computer vision engineers
  • Machine learning engineer
  • AI researchers
  • AI architects

There are other pressing questions, too. How can existing IT professionals build specific skills for AI platforms, while they stay at their existing jobs? How can AI neophytes build the necessary skills and understanding to enter this lucrative profession, without putting their career on hold while they retrain? Nanodegrees seem to be the perfect solution.

To see the five reasons why an increasing number of people are opting for these specialized online programs to help them transition to AI careers, please go to the original article found here.

To learn more about Udacity’s Enterprise solutions, click here.

Kai-Fu Lee joins Sebastian Thrun for Udacity Talks

The author of “AI Superpowers: China, Silicon Valley, and the New World Order” talks about AI, innovation, and his belief in the human soul.

Udacity Talks with Kai-Fu Lee and Sebastian Thrun was quite an international affair! People joined from all over the world, including Canada, Greece, Laos, Malaysia, Mexico, Nigeria, South Africa, Switzerland, and more. Here in the US, we had attendees from Arizona, California, Michigan, New Orleans, North Dakota, South Carolina, Texas, and Washington, D.C., just to name a few!

As is to be expected when Sebastian hosts, conversation topics were wide-ranging, running the gamut from China’s rapid ascendance to the status of AI superpower and the end of banking as we know it, to the inevitability of flying cars and the chasm between “narrow AI” and “general AI.”

Highlights included Kai-Fu Lee’s eloquence on the subject of the human soul, and his teasing admonishment to “the CEO of Kitty Hawk” for still using text messaging!

For those viewers not familiar with the latest developments in China, it was remarkable to hear of the innovations already happening, especially when it comes to infrastructure spending. Lee described one such effort, in which a new city “the size of Chicago” is being built; a city that will essentially have two “layers” — a traffic-free one featuring parks, pedestrian walkways, and pet areas, and a lower level where autonomous vehicles drive.

Details like these make the entrepreneurial spirit in China seem almost magical, but as Lee pointed out, there is a downside. He described the Chinese entrepreneurial space as being like a “gladiator ring” in which only one competitor survives!

The conversation took an unexpected turn to the spiritual when Sebastian asked about the “AI endgame” and whether or not Kai-Fu Lee believed in “general AI,” which is considered the equivalent of human intelligence, and is often understood to be the point at which machines have a “consciousness.”

Sebastian Thrun: Do you believe in general AI?

Kai-Fu Lee: I do not. I choose to believe that we have a soul, and that it cannot be replicated.

Sebastian reintroduced variations of this question throughout the remainder of the talk, with Lee at one point stating that, “I’m not saying it’s impossible to build a machine with a soul, I’m saying we shouldn’t.”

It was powerful stuff, but there was levity as well. When asked to give advice to American companies interested in going to China, Lee had this to say:

“My advice to most American companies that want to go to China is, don’t.”

Lee also offered advice to younger generations coming into the workforce, who will increasingly be contending with an AI-powered world:

“Do what you’re good at, and what you love. And be aware of what AI is going to replace. Think of AI as either a job destroyer, or a job enhancer, and go into those areas where AI will enhance.”

Kai-Fu Lee is the former head of Google in China, and the co-founder of Sinovation Ventures. He is also the author of “AI Superpowers: China, Silicon Valley, and the New World Order,” which Sebastian has described as:

“A unique book by one of the leading pioneers of the field of AI. Kai-Fu Lee is a top notch researcher, business executive and investor. He tells the tale of AI — in China and the US — better than anyone else. A great read!”

You can watch the full episode of this Udacity Talk here.

Answering “Yes” to Hard Questions About the SKills Gap, and The Future of Work

A recent article from the University of California’s Chief Innovation Officer, about the impact of disruptive technologies on jobs and skills, poses critical questions about how we connect learning to jobs—today, and in the future.

Future of Work

Everyone from politicians to policy makers, utopianists to university professors, innovators to investors, is talking about the future of work, the fourth industrial revolution, and the automation age. It’s hard to avoid these topics, and if you’re between the ages of, say, 16 and 80, you probably shouldn’t avoid them.

Our work lives are changing, and depending on how we manage the transition, this could either be a new golden age, or a serious shock to the system.

At Udacity, we’re engaged in helping lifelong learners across the globe empower themselves through learning, in order to build rewarding lives and careers. As such, we’re acutely aware of the looming changes—the theories around how it’s going to happen, and what it’s all going to mean.

We engage every day with innovators, educators, students, employees and thought leaders, to better understand what education needs to do, be, and represent as we move forward. We work with recruiters, hiring managers, entrepreneurs, and executives, to better forecast what skills will be needed, where the demand will be, and what career advancement will look like in the days, years, and decades to come. We collaborate with individuals, startups, and global corporations, to better understand how and where the work of the future will happen. In short, we spend a vast amount of time learning from anyone and everyone about what the future holds, and how we can best prepare our students to succeed.

We listen, we talk, we watch, we ask, and we read.

One article that recently impressed us for its ambitious scope, rich degree of insight, and clear-eyed understanding of where the world is heading, is a post by Christine Gulbranson, the Chief Innovation Officer for the University of California System. The article is entitled The Future of Work: The Impact of Disruptive Technologies on Jobs and Skills. Here is a sample of the wisdom Gulbranson shares in this provocative and timely piece:

“It’s not difficult to make some basic calculations about what skill sets will be needed in the future: automate predictable manual labor jobs and the skills demanded for such jobs decreases. More automated factories will increase the demand for hard skills in mechanical engineering, software architecture, coding, algorithms, data structures, data analysis/data science, and machine architecture/design. Increasing gene editing and robotic surgery will increase the demand for software engineers and mechanical engineers who also have medical skills. Move to IoT cities and policy makers and lawyers will need to understand coding, software architecture, economics, and more, on top of what they’re expected to know today.

Clearly with a rise of connected devices and infrastructure, machines, AI, spatial computing, blockchain, and autonomous vehicles, there comes an increase in demand for STEAM skills. However, sitting on top of hard skills is a deep and strong layer for cognitive, analytical, and soft skills. Employers won’t be looking for a degree that signifies what a candidate knows: they will be looking for someone who can learn, combine and analyze, problem-solve, create, and adjust.”

It’s that last sentence that especially resonated with us, because this echoes exactly what we hear directly from employers every single day. The pace of modern business and the rapid advance of technology have significantly altered the hiring landscape in such a way that characteristics such as agility, growth mindset, adaptability, creativity, and grit have emerged as the most important factors in predicting a successful hire.

That’s not to say that acquired skills don’t matter—they do!—but the ability to learn new skills and apply them has become just as important as the skills you already possess.

This is also not to say that educational pedigree doesn’t have a place any longer—it does—but what constitutes credible pedigree is changing rapidly. As we’ve learned in the years since first launching our Nanodegree programs, a Nanodegree credential fulfills a dual role. In addition to affirming your skills acquisition, earning a Nanodegree credential stands as evidence that you are a self-motivated problem-solver who possesses grit and determination.

Gulbranson’s article concludes on a sobering note of caution:

“Finally, as we already know today, if education can’t keep up with changing industry, then the skills gap will hinder technological advancement and adoption.”

She goes on to ask some powerful questions, such as:

  • Are students learning how to learn, handle high complexity, and be flexible?
  • Are they learning how to make the invisible visible, and how to make good decisions using data and analysis?
  • Are there solutions that don’t cost an arm and a leg and last four years when the industry needs a software engineer who is also a psychologist to create a product that detects the mood of drivers and auto-shuts off the car appropriately?

We’re proud to be part of a new generation of learning providers offering opportunities that represent a “yes” answer to all the above, and we’re grateful to innovators like Christine Gulbranson who are out there asking the hard questions, and providing the right answers.

Through your commitment to lifelong learning at your organization, you are helping build rewarding careers for employees, while creating an environment for innovation.

~

Visit udacity.com/enterprise to discover how we can help your organization successfully navigate workforce transformation!