Data science has become a buzzword in today’s modern world. Every business keen to survive in the competitive market has heard, if not explored, this booming industry. The study and use of data, though, isn’t something new. In the past, it was commonly used for scientific experiments. You get data from the results of a series of testing because you want to prove or disprove a hypothesis.
Now, with the rise of technology, particularly Artificial Intelligence and Machine Learning, it’s much easier to get and digest large sets of data for more accurate decisions. Its popularity is not slowing down any time soon, either. According to the report of a top job site, Indeed, the demand for data scientists increases every year. It has a 344% upswing since 2013.
However, businesses looking to start their foray into data science should understand there’s more to the field than hiring one data scientist and be done with it. It’s like every other business area. They won’t find a single person that has all the competencies and resources to do all the work. Like in digital marketing, sometimes, you must expand the team or avail of outsourced services like WordPress development or social media management.
If companies decide to build an in-house data science unit, they should consider the following roles:
Considered as the most famous role, data scientists deal with spotting patterns and insights in the data. They play around with the raw data and utilize multiple statistical procedures and tools such as Hadoop, Python, SAS, or R to generate conclusions on what the data is saying. It may look simple, but cleaning and processing data takes a lot of time. That is because not all data sources are organized, aligned, or usable. The data scientist must transform unprocessed data into something that the applications can understand and make reliable connections with.
Data Visualization Developer
Building interactive dashboards are the bread and butter of data visualization developers. They wrangle the data and insights into something easily understood and manipulated by management, especially those unfamiliar with the nitty-gritty of data science. Their work helps decision-makers analyze the lay of the land and come up with solutions based on evidence instead of conjecture. Data visualization is both an art and science with how it blends design elements with statistics.
Data Science Project Manager
Every project still needs a project manager, no matter the industry. Businesses don’t want their technical team members distracted and frustrated because they also must deal with project management and client relations. That is where the data science project manager comes in. They ensure that the project is going smoothly and within the agreed timeline. Additionally, they serve as the shield protecting the team from scope creep and unreasonable requests from the clients. Project managers from other industries can transition into the data science field, but they should be able to understand the technical aspects of the project.
In the information age, knowing how to make sense and implement insights, with the use of data science, is gold. Businesses, however, have to understand that there’s more to the industry than having one data scientist on board.