Technology is advancing rapidly, and it’s worth your while to ensure your tech team’s skills and knowledge remain relevant and ahead of the learning curve. Data science teams are composed of highly skilled and sought-after roles, and the expertise required to fill them is evolving as quickly as technology. These roles will continue to grow in importance and have become critical areas for organizations to expand and build upon.  

But what led to data science’s significance and growth in the workforce? Where does data science go from here? Udemy instructors and data science professionals, Michael X Cohen and Diogo Alves de Resende, discuss these topics, among others, in detail during UB’s Insights on Demand: Diving into Data Science webinar. Here we’re going to take a closer look at the key factors these Udemy instructors believe have influenced the growth of data science. Plus, some emerging data science trends to be on the lookout for in the future.

What’s contributing to the growth of data science?

Data science, in general, is an interdisciplinary field where scientific methods and processes are used to extract useful insights from data sets that can be used for applicable solutions. 

This process is involved in almost everything we do, including the social media timelines we scroll through on our phones and decision-making processes in the workforce. 

In short, data science is everywhere these days, and Cohen believes its growth is centered around workforce needs and the sustained interest of those inside and out of academia.

The relationship between data science and business revolves around organizations’ growing need for data-driven initiatives. This includes, but is not limited to, the need to leverage data science to make decisions that influence organizational strategy and direction. And this need isn’t solely confined to the high-tech or software realms.

In fact, there has been a massive growth in data-science jobs in the education, marketing, and manufacturing sectors. Business leaders want — and need — the best, most up-to-date resources at their disposal to help make decisions about the direction of their organizations. Data science delivers information backed by scientific data to help guide these decisions.

While many data scientists get their start in academia, many also develop their skills in the workplace. Cohen explains that those in academia have immense interest in data science and are often discovering new ways to analyze structured and unstructured data. There is a simultaneous need for data scientists in the workforce to analyze and leverage findings from the growing volume of data companies collect. Due to the dire need for more data science talent, companies are looking for more ways to expedite the growth of their data science departments, including collaborating with universities and students in fields that align with strong data science capabilities, while also developing data scientists from within their organizations.

Another factor contributing to the data-science boom is the growing interest that young adults are showing in this discipline, which has contributed to the “birth of the data-science generation.” Additionally, the internet and social media have played a large role in pushing young adults — specifically millennials and members of Generation Z — to consider careers in data science, whether it’s to work with social media algorithms directly or to find ways to use data science for advancements in other fields.

Future data science trends to consider

We now have a general sense of the factors that experts believe have contributed to the growth of data science. But where do we go from here?

Both Cohen and de Resende believe data science will continue to influence how businesses run their day-to-day operations. A notable advancement, in de Resende’s opinion, relates to time series and forecasting. Time-series analysis is a specific way of analyzing a sequence of data points collected over an interval of time, and forecasting relates to what’s going to happen in the future by analyzing the past and present. De Resende sees value in this capability and suggests companies focus on upskilling teams with these tools in the future.   

Another emerging trend to consider is multivariable analysis, which Cohen refers to as a collection of analysis methods designed for large data sets with lots of variables. An example of this would be how a person’s overall health is determined. In this case, the multivariable analysis would take into consideration a number of variables —  like calorie intake and how much someone exercises or sleeps — to get an accurate assessment of the individual’s overall health.

As an example of how this principle applies to the workplace, companies currently experiencing issues in the supply chain — an area that includes moving parts from multiple departments and locations — could potentially find the multivariable analysis useful.

Multivariable analysis’ stock increases further when considering how an individual or corporation is limited to testing only two variables at a time when they don’t utilize this approach. Without multivariable analysis, an organization’s ability to categorize customers or examine marketplace trends would be highly compromised.

Multivariable analysis can provide insight into how businesses can best determine which variable or combinations of variables have the most potential to affect revenue and growth.

Is your team prepared?

Data science and the need for data scientists in the workforce will continue to grow, so long as companies continue to drive data-driven decisions.

Learn more about new trends and topics in data science — including how to help your technical teams develop their data science and analytics skills — from our on-demand webinar, Insights on Demand: Diving into Data Science.

This video offers meaningful insights on all things data science, including tips and resources from Udemy Business instructors on ways to upskill and retool your data science teams.