How to Develop Your Data Science Team

Data science is one of the top technical skills all employees need in 2022. But how do you help your team develop these skills and define what specifically they need to learn? We recently asked a panel of data scientists to share their insights on developing a data science team. Here are a few key takeaways. 

Look for in-house talent 

There’s a growing need for analysis skills like machine learning, data science, and data visualization, but not enough candidates on the market to meet these needs. This is why it’s critical to develop this talent in-house. 

“Developing in-house talent takes a two-pronged approach,” says Udemy instructor Mike Cohen. The first prong involves ongoing training and continuous learning. “Data science is continuously evolving,” says Mike. This means that your in-house team should be regularly devoting time to following the latest developments in the field and reinforcing the foundational math and statistical skills that data science techniques are built on. 

The second prong involves educating non-experts on what the company can expect to get from its data. This process of developing data literacy ensures everyone understands what is realistic and where their data might be flawed. “You don’t need to be a data scientist in order to learn how to make data-based decisions,” says Mike.  

Find a balance between breadth and depth of knowledge

“Trying to know everything results in superficial knowledge,” says Mike. And in the world of data science, it’s impossible to know everything. Data scientists should strive for a balance between breadth and depth of knowledge. Mike puts it this way: “Each individual should have their own deep knowledge of a small number of topics.” 

It’s also imperative to develop knowledge of your business’s needs, argues Udemy instructor Diogo Alves de Resende. “If you just work on your data and building your models, but you’re not aware of what the business needs, there’s a disconnect,” says Diogo.   

Provide tools to help your team develop data science skills

Which tools will set your data scientists up for success? According to Diogo, “The best data science tools are those that allow collaboration.” Here are a few of the specific types of tools that data scientists will need to use regularly. 

• Shareable notebook tools: Notebooks are a type of interactive computing that allow data scientists to write and execute code, visualize the results, and share insights. They create an instant feedback loop and promote collaboration. There are numerous notebook tools available, with Jupyter being the most popular option.  

• GitHub: “Data scientists need to use GitHub for much the same reason that software engineers do — for collaboration, ‘safely’ making changes to projects, and being able to track and rollback changes over time,” according to Rebecca Vickery at Towards Data Science. 

• A fast, efficient database that’s easy to query: Data scientists need to design, create, and interact with databases in order to perform their jobs. “Databases make structured storage secure, efficient, and fast,” explains Towards Data Science’s Sara Metwalli. “They provide a framework for how the data should be stored, structured, and retrieved. Having databases saves you the hassle of needing to figure out what to do with your data in every new project.” Common databases include Hive, Presto, and Redshift. 

• Easy-to-use dashboard tools: With a growing number of workers looking to glean insights from data — even when they aren’t officially data scientists — dashboard tools are becoming increasingly popular. Data visualization dashboard tools like Tableau and Looker help make data easier to present and share. 

Support your organization’s data science journey

Whether you’re developing a team of a few data scientists — or a few thousand — it’s important to understand the data science landscape. What are the most critical skills and how can you help your team develop them? Explore these topics in more detail in our on-demand video series. Watch it here.