Becoming a Data Scientist: Profiling Cisco’s Data Science Certification Program

Today’s subject matter experts and specialists are tomorrow’s data scientists thanks to Cisco’s Enterprise Data Science Office.

Cisco Systems—a US technology company that develops, manufactures, and sells networking devices and management—has taken a forward-thinking and flexible approach to both finding and retaining talent in the face of rapid advances in machine learning and big data hype.

In an interview with Kristen Burton, Director for the Enterprise Data Science Office and Digital Process Transformation, and Justin Norman, Manager of Cisco’s Enterprise Data Science Office, I learned about Cisco’s Data Science Certification Program. Now in its 4th year, the continuous education program is helping Cisco develop big data skills in their employees in support of Cisco’s digital transformation. For many companies, Cisco’s tactics might serve as a helpful blueprint for developing similar learning plans. Plus, for every level of the four-stage program, I include tips and resources for readers forging their own path towards a career in data science.

Cisco’s Data Science Certification Program

The Data Science Certification Program, open to Cisco employees of all backgrounds, is a uniquely customized educational path designed to train existing talent within the company. It was originally conceived about 3.5 years ago when Cisco’s Enterprise Data Services team, under the leadership of Sr. Director Des Murray began building advanced technology capabilities around new platforms. Their new enterprise data technology stack was best in class, but a lack of advanced analytic capabilities was an impediment to onboarding new internal users which meant the platform wasn’t utilized to its full potential.

When statistical knowledge and data science capabilities began to spread around a few individuals within Cisco, it became clear that a formalized path to growing these skillsets was necessary. Recognizing the limitations of analytic abilities limited to small subset of people within the organization reminds me of the proliferation of “tribal knowledge” as the impetus for building Airbnb’s Knowledge Repo. As Kristen and Justin described to me, Pamela Webber, the architect and program lead behind the Data Science Certification Program, outlined an approach to not just « scaling knowledge » across their organization, but also creating it.

The program consists of four levels beginning with building a foundational basis for developing core knowledge and skills.

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Level 0: Pervasive General Knowledge

The first level of the program begins with courses from MOOCs like Udacity and Coursera from around the web to introduce “students” to programming languages like Python and R, statistics and statistical thinking, as well as the basics of machine learning. By carefully selecting curriculum from an array of freely available sources, employees who enroll acquire a broad and solid basis for future learning while allowing the program to adapt to changing trends, tools, and technologies.

The goal of the “zero” level of the program is to provide students with the basic understanding of concepts and taxonomy with some technical skills and will prepare those who are interested in moving to the next level with enough knowledge to pass an entry exam. Mastering the courses in the foundational level demonstrates motivation and potential to apply what they’ve learned to practical problems.

Anyone who’s got their eyes on « the 21st century’s sexiest career » needs to start from a solid foundation. MOOCs are a great way to get comfortable with popular languages, statistics, and ML essentials plus certificates show you’ve got dedication. Here are a few specific courses I can recommend that will help you explore whether data science is right for you:

Level 1: Associate

After getting their feet wet, graduate employees of level zero seeking to continue the program then move on to level one. Level one consists of a 6 month certificate program designed to teach people how to apply the knowledge they acquired in their foundational courses to real, practical problems. But before entering the certificate program, students must have the demonstrated foundational skills by passing a standardized test. Since this is a highly accelerated program, students are expected to have intermediate knowledge in math, programming and statistics upon program entry and Justin explains that implementing a standardized test helps prepare students for successful completion of the certification.

The certificate program consists of professor-led courses delivered live to students through Cisco’s collaboration technology from North Carolina State University and UC Irvine instructors. The courses provide students with strong backgrounds in R and Python and cover diverse topics to continue building broad competencies including:

  • Data mining
  • Using big data platforms and tools
  • Crafting a story through data visualization

As a strong collaborative company, the certificate program likewise fosters interaction and co-working among students on Cisco’s business problems and datasets as part of its model. Following each class, Justin and Kristen tell me that students break-out into TA sessions to work together in smaller cohorts on homework and capstone projects to apply their newly developed skills. And at any given time, multiple segments of the program are running across the US and internationally meaning people from diverse business and cultural backgrounds are coming together to collaborate.

To finish the certificate, students must complete a final project using Cisco data applied to a business problem. The final project is an opportunity to show off to the data science community through a portfolio and demonstrate an applicable skillset to Cisco managers.

Advancing to the next level in data science means spending the time and resources to develop and demonstrate real-world expertise through a portfolio of projects. It’s also the right stage for you to invest in networking with other data scientists. Here are some concrete tips:

Levels 2 and 3: Practitioner and Lead

The final two levels of the Data Science Certification Program are focused on applying skills in real business settings while acquiring deeper expertise in areas of interest and becoming thought leaders in the organization.

Once students have graduated from the certificate program in level one, they continue to build their data science portfolios. In level two, students focus on developing a specialty, usually within their area of expertise at Cisco. For example, they may work on specializations ranging from NLP and text mining to deep learning or data visualization depending on their career advancement goals. This stage of the continuous learning program is focused on getting students into true data science roles within Cisco.

Finally, students who have ascended to level three are officially considered to be data science professionals and are now working on achieving a Master’s degree in business analytics or machine learning. By now, they have a track record of demonstrated contributions and expertise and are expected to assume leadership roles within their community. At this point, they’re impacting their business unit or even the company as a whole from a thought leadership perspective.

If you’ve made it this far, congratulations! You’re an expert now and my only advice is to:

  • Be a thought leader: Whatever your realm of expertise, use your talent to make a difference. Maybe you have the influence to develop a continuous education program like Cisco’s in your own organization.
  • Keep learning: compete (and maybe even win!) in Kaggle competitions. Plus, being an active member of the community will help you keep up with the latest trends and techniques.

Cisco’s Lessons Learned

It’s easy to understand how Cisco’s Data Science Certification program has positive reverberations throughout the company as Kristen and Justin describe the success of the program at a number of levels. One of the most immediately tangible benefits has been readying Cisco’s workforce for digital transformation by expanding data literacy and skillsets in business functions across the company. And of course, employees have also enjoyed the benefits of continued growth in their careers at Cisco.

Even outside of the Data Science Certification program, Cisco has found it important to provide diverse, engaging ways for their employees to build and retain their skillsets through fun events, collaborations, and networking opportunities. For example, Cisco has hosted competitions internally to allow people to apply their skills and continue to learn in an exciting setting while working as teams across business units.

As I learned from talking with Justin and Kristen, Cisco’s program is a model not just for other companies seeking to cultivate internal talent, but also for individuals looking for a clear direction for professional development in data science. The program adds much needed structure and milestones to what can otherwise be an overwhelming deluge of online courses and new technologies.

InnoValeur | Data Science | Smart Data | Machine Learning | AI

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