Demand for data science professionals is growing, and educational institutions such as the Nanyang Technology University (NTU) have started offering interdisciplinary offerings that incorporate data science with topics such as economics.
But can good data scientists be churned out by simply putting them through a curriculum or having them sit through some certification?
In a contributed opinion piece to The Business Times, David Hardoon, a senior advisor in data and artificial intelligence for UnionBank argues that the diversification and complexity of data science pose a real challenge to data science education.
Data science is a complex field
“[Data science] is a hybrid of statistics, machine learning, and data mining, and covers programming languages, technology frameworks, development platforms, as well as visualization tools,” writes Hardoon.
“How does one balance the multiplicity of areas, covering them with sufficient depth in order to produce a well-rounded data scientist who can provide impactful value to a future employer?”
Indeed, in an article earlier this year titled “Placing 'Practice' at the Center of Data Science Education”, Eric Kolaczyk, Haviland Wright, and Masanao Yajima described the downsides of typical postsecondary training approaches in core data science fields.
Practice is treated as something that is done only to capstone projects or as a finale, they wrote, and this results in “students, upon exiting academia, needing a nontrivial ramping-up period before they can truly have an impact with their first employers.”
Having helmed or served in advisor roles to a diverse range of organizations in Singapore such as the Corrupt Investigation Practices Bureau (CPIB), the Central Provident Fund (CPF), and the Monetary Authority of Singapore (MAS), Hardoon notes that this feedback is common and goes beyond the degree itself and into the practical applications of the coursework.
“The majority of data scientists, upon completion of their education, still require a hands-on education at their place of employment to refine their learned skills to practical necessities… a freshly minted, well-rounded data scientist is unlikely to hit the ground running at their first employ despite a presumption otherwise,” he wrote.
A call for change
To help data scientists close this gap and produce effective work quickly upon completion of training, organizations might want to consider operationalizing data science by identifying key data science roles within the organization, suggests Hardoon.
One solution is to segregate between key components of data science operations, with the creation of roles. Some examples: Data science developer (Focus on data science methodologies and techniques), data science engineer (Focus on data piping, data quality, and data ingestion), data science solution architect (Deep understanding of platforms and data enterprise architecture), and data science storyteller (Focus on data visualization with strong business acumen).
Alternatively, existing data science programs can be tailored to offer greater depth with a focus on niche areas or professional specialization to better reflect the maturity of the data science field today, Hardoon notes.
Don’t expect this to happen tomorrow, though. While we do see data science moving in the direction suggested by Hardoon, these changes will take time yet to fully materialize.
In the meantime, employers may just have to accept that a newly minted data scientist is unlikely to produce valuable, actionable insights from day one – or even the first few months. To help a new data science hire get started quicker, additional on-the-ground training is essential.
Paul Mah is a senior editor at CDOTrends. He likes (almost) all things tech and enjoys writing about data science, AI, and digital transformation. You can reach him at [email protected].
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