As part of our online master’s in data science program, we offer in-person Immersions featuring industry experts.
At our most recent immersion in Palo Alto, an industry panel made up of data science experts from Facebook, Uber, SAP, and other companies offered our students some insider advice on how to obtain and succeed in data science careers.
Here are five valuable insights they shared:
- Data Scientists Come from a Variety of Professional Backgrounds
- Data Scientists are Influencing New Products and Finding Solutions
- Your Ability to Work in Teams is Just as Critical as Your Technical Skills
- Strong Quantitative Skills are not Everything— Data Scientists Must be Good Communicators
- Consider the Potential Automation of Data Science Processes
Tip #1: Data Scientists Come from a Variety of Professional Backgrounds
Not all data scientists start their careers in data science.
A Career Pivot from Accounting
Godlewski began working at PwC, LLP as a certified public accountant auditing financial statements. She became interested in data when her audit team had to call in the PwC computer experts because they did not understand queries.
“That was the moment I decided I had to figure out this whole query thing,” Godlewski explained.
She moved around in PwC and started working in their data management group doing computer-assisted auditing techniques and taught herself coding, which led to a job in e-commerce working for Groupon.
Godlewski is a data scientist at Facebook where she focuses on a feature that shares memories with users, “On This Day.”
An Engineer Who Loved Data
Shetty, who has a strong background in engineering, became interested in data when he enrolled at the India Institute of Science, not to earn a master’s degree, but to learn more about high-performance computing.
“That’s when I got the opportunity to work with really good datasets, and I just fell in love with data,” he said. “I pursued my master’s in computer science more for specialization in data science and machine learning.”
To strengthen his professional profile for potential employers, Shetty also did Kaggle data challenges and kernels, where he gained additional experience exploring and cleaning data sets, and building toy models.
His extra practice paid off.
Shetty is currently working as a data scientist at SAP, a company that builds systems to help businesses run more efficiently.
His team is responsible for recycling the data generated by the company to produce insights that help businesses improve their service to clients.
Tip #2: Data Scientists are Influencing New Products and Finding Solutions
Companies across the globe are benefiting from the power of big data or what has been described as the “new oil.”
They are using data and analytics to support innovation, to better manage the product life cycle, and to solve problems for consumers.
Product Life Cycle Management
Industries marketing consumer products and services need insights from data to adapt quickly to changing market conditions.
Shane McCarthy, Senior Software Engineer at Uber, emphasized the importance of data science as it relates to the entire product life cycle— analyzing how the product is doing, deriving insights about how to improve it, and discovering new products.
Data scientists also play a major role in product development:
“Data scientists who do the discovery work are the ones feeding all the information that we [Uber] use to figure what’s our new product going to be and why. They are also very engaged in the implementation. How we should implement it, how we should log in the information that comes from it in order to derive the insights to see what is successful and what is not,” McCarthy explained.
Solving Problems with Better Solutions
Many industries also turn to data science to find solutions for their customers.
Jack Moore, Product Manager for Zaplabs, a real estate software company, uses data science to improve the product his company provides to real estate companies.
“It’s my job to find problems and dictate how we’re going to solve those problems in a way that fits company strategy,” he explained. “A lot of industries turn to data science to find better solutions for their customers. They use complicated data science solutions in ways that solve people’s problems.”
Tip #3: Your Ability to Work in Teams is Just as Critical as Your Technical Skills
Working in teams is an important skill for future data scientists.
Moore shared this overview of the diverse teams he interfaces with as a Product Manager at Zaplabs:
“I sit between business and tech. So, there is a business person, who is asking what is are product doing, and the tech person, who is asking why are we making these product decisions, or the designer, who is asking why are we designing this thing that way. The data product manager bridges those gaps. ”
Given the importance of teamwork for success in data science careers, the group work both within and outside the synchronous sessions in our online master’s in data science program is designed to mirror a typical work environment for data scientists. The program gives students real-world experience working on teams and collaborating with other technical and non-technical professionals.
Tip #4: Strong Quantitative Skills are not Everything— Data Scientists Must be Good Communicators.
An overarching theme among our panel of industry experts was the need for data scientists to have excellent communication skills.
Moore emphasized that data scientists who are skilled communicators excel in the field:
“Data is incredibly complex. It’s hard to explain it to people, but you can be really successful if you do a good job at it.”
Godlewski also emphasized the importance of data scientists being skilled communicators:
“We can’t be successful without data communication. Otherwise it’s who the heck cares? You can come up with all the numbers but nobody understands it unless somebody translates it.”
Understanding the Nuances of Qualitative Interpretation
Ryan Welsh, Founder and CEO of Kyndi, and Godlewski pointed to the importance of data scientists developing strong qualitative interpretation skills along with their quantitative capabilities.
Welsh explained that data scientists need to be skilled using tools and techniques to explore the data that falls outside the realms of statistics:
“So many times, we focus on the quantitative data and a lot of it is qualitative. How do you access that with new techniques? Getting into linguistics graph computational methods using different probability theories beyond the frequent Bayesian approaches.”
Godlewski also echoed the importance of good qualitative learning for aspiring data scientists:
“Qualitative learning is just as important a quantitative learning to data. You can’t have one without the other.”
Tip #5: Consider the Potential Automation of Data Science Processes
Welsh believes that from a startup perspective there is a lot of opportunity for streamlining data science processes.
Consequently, he also thinks that many data science processes will be automated in the future:
“I think where data science is going is the process is getting cleaned up a lot; so, prepare for that and build teams around the whole cleaning up of the data science process.”
To remain at the forefront of the field regardless of its future automation, Godlewski emphasized the importance of data scientists being able to synthesize and go beyond performing a set of tasks in a software package.
“Despite the automation in this field, if you stay on the front cusp of the technology, when it does become easy, cheap, and ubiquitous, you will be in really great position to understand the background behind these things and advocate for them in a larger sense when they become more acceptable.”
That’s one of the things Notre Dame’s online master’s in data science gives students.
“It’s higher level learning where students think about things more deeply. They are not just thinking about topics for a couple of weeks, they’re thinking about them continually and in a synchronized way,” said Roger Woodard, Director of Online Program in Data Science.
In our program, students develop a deep understanding of the data science process that goes beyond munging data and running lines of code, it prepares them with the exceptional analytic and critical thinking skills needed to adapt and problem solve in a fast-changing data science environment.
Bottom Line: The Human Element of Data Science Will Always Be Critical
As data science processes change and new ones emerge, Welsh believes the human element that data scientists bring to the table will always be highly coveted:
“No matter how the processes of data science change or become automated, creativity and resourcefulness will always be in-demand.“
The human element of data science also refers to understanding the human impact of data usage.
Moore explained that solving the right problems is something he strives to do every day in his job.
For this reason, he champions the strong ethical foundation in Notre Dame’s program:
“Notre Dame has this really amazing ethical foundation that makes it easy for students to embrace this idea of going out into the world and solving the right problems.”
The insights shared by these industry experts confirm that future data scientists will need to:
- Be agile thinkers, who can go beyond the techniques
- Understand the processes of data science and apply critical thinking
- Be effective communicators with strong qualitative skills, not just quantitative skills
- Understand the ethical implications of their work