Technology Leader Sue Barsamian Shares Her Leadership Playbook

Transform your Workforce

Great leadership is the foundation of any organization, big or small. Without capable leaders guiding the way, growth is impossible. Effective business leadership requires a skillful captain to navigate the ship, and not simply a passive presence standing near the helm. Quality leadership is active, not static. One person who knows what it takes to be a successful leader is technology veteran Sue Barsamian. 

We sat down with Sue during our recent Udacity Thought Leader Series Webinar to discuss the qualities leaders need to create and manage high-performing, productive teams. Sue’s extensive background in general management, marketing, sales, and engineering is a testament to her reputation as a foremost expert in team development. Currently, Sue serves on the boards of Symantec, Box, Gainsight, and Xactly. Previously, she was the Executive Vice President, and Chief Sales and Marketing Officer at Hewlett Packard Enterprise Software, where she orchestrated the successful spinning-off of the division from HPE and merger with Micro Focus International, to form the world’s seventh-largest software company.



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Announcing our newest client: Global consulting leader Accenture

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Since we launched Udacity’s Enterprise business in 2017, a growing number of companies have enrolled their employees in Udacity’s Nanodegree programs, indicating that leading organizations are increasingly investing in Udacity’s Nanodegree programs to upskill their own employees while helping to close the talent gap in the labor market. We’re proud to have partnered with Fortune 500 companies to help them transform their workforces. Today, we’re thrilled to announce our latest partnership with the worldwide consulting leader Accenture. 

Learning with Udacity supports Accenture’s mission to help solve their clients’ toughest challenges. Through this pilot program, selected Accenture employees will have the opportunity to enroll in one of five Udacity Nanodegree programs in pioneering technology subjects such as Self-Driving Cars, Deep Learning, Deep Reinforcement Learning, Machine Learning, and Computer Vision.

When I met with Udacity graduates at Accenture last month, we spoke about the changing nature of industries worldwide. Transportation, for instance, is undergoing multiple parallel changes with the advent of electric and self-driving cars, as well as new ridesharing models. Lifelong learning is a crucial tool that enables students to catch up to the latest in these fields. By becoming expert learners in subjects such as self-driving cars and machine learning, students will be able to move in tandem with these innovations, remaining on the pulse, and transition into new capabilities as experts in their fields.

Andrew Smith, Managing Director and Head of Industry X.0 at Accenture, completed Udacity’s Deep Learning Nanodegree program earlier this year. “Continuous learning is important in every job. But in consulting it is vital to stay ahead, especially as the speed of technological advancement accelerates every day,” he said. “Udacity’s programs teach the exact tech skills we need at Accenture. They offer a great learning experience that can be flexibly integrated into your schedule regardless of location and course hours.”

By launching this pilot program with Accenture, Udacity is taking another step towards our mission to power careers through tech education. A warm welcome to all the new learners at Accenture. We look forward to watching your continued progress and seeing what you will build. Thank you for learning with Udacity!

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How companies need to think about the cloud and digital transformation

Enterprises are at a critical juncture: adopt and adapt technology to meet the 24×7 demands of customers, employees, and partners—or risk becoming obsolete. One of the primary shifts in responding to such demands is cloud computing.

As part of our Udacity Thought Leader webinar series, our very own Lalit Singh, COO at Udacity, sat down with Rahul Tripathi, the CTO and VP of Customer Success at Nutanix to discuss the challenges facing companies looking to embrace the cloud while remaining relevant in today’s competitive market. The conversation anchored on how the cloud is the foundational enabler of digital transformation and offers the scale and speed needed for businesses to grow.

When it comes to transforming the infrastructure of a business, both modernization and automation play crucial roles. For organizations looking to bridge the digital skills gap and attract talent, it’s important to move from outdated to next-generation systems that facilitate innovation. Hear Rahul stress how technical skills and soft skills are critical for any business looking to stay competitive and transform into digital enterprises.

Watch as they discuss:

  • What a modern enterprise cloud strategy looks like
  • Why digital transformation requires the right blend of business, IT and soft skills
  • How organizations can take steps to bridge their hiring and training strategies

View webinar

SXSW: How Top Companies Create Training Data

Udacity had the unique opportunity to have two of our thought leaders on a panel discussion on training data for machine learning entitled AI-AI-Oh! during SXSW 2019. The discussion triggered an exchange of viewpoints among the expert panelists which ranged from how the data is being used in various industries, how much training data you need to apply machine learning, and practical tips for the audience to consider.

You can listen to the entirety of our panel discussion here.

The discussion started with the framing of machine learning. Machine learning (ML) is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to.

An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have similar musical taste.

Machine learning fuels all sorts of automated tasks and spans across multiple industries, from data security firms hunting down malware to finance professionals looking out for favorable trades. They’re designed to work like virtual personal assistants, and they work quite well.

Machine learning serves a mechanical function the same way a flashlight, a car, or a television does. When something is capable of “machine learning”, it means it’s performing a function with the data given to it, and gets progressively better at that function. It’s like if you had a flashlight that turned on whenever you said “it’s dark”, so it would recognize different phrases containing the word “dark”.

In machine learning projects, we need a training data set. It is the actual data set used to train the model for performing various actions.

ML relies heavily on data; without data, it is impossible for an “AI” to learn. It is the most crucial aspect that makes algorithm training possible. The panelists discuss three different types of training data including:

Client services data – data generated from customers. “At HubSpot, we gather user-generated training data for ML that informs everything from email send time optimization to audience targeting,” stated Hector Urdiales.

User generated data – data created by users on their own without being prompted.  “We train data based on patterns,” said Rob McGrorty.

Simulated data – sensor data that self-driving cars, for example, collect in the real world. “A test vehicle’s cameras might record video of pedestrians crossing the street at night. Software developers can use that video every time they update their self-driving software, to verify that the software still detects the pedestrians correctly,” explains David Silver.

Essentially, training data is the textbook that will teach your AI to do its assigned task, and will be used over and over again to fine-tune its predictions and improve its success rate. Your AI will use training data in several different ways, all with the aim of improving the accuracy of its predictions.

Quite simply, without training data there is no AI. The cleanliness, relevance and quality of your data has a direct impact on whether your AI will achieve its goals.

Be sure to listen to this informative panel discussion and learn more about training data and practical use cases.

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.

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.