How the Role of a Data Scientist Has Progressed


Data science is one of the fields that combine several disciplines, from mathematics and statistics to AI and data analysis. Throughout the years, the role of data scientists has slowly evolved from being the silent guys behind the scenes to becoming industry leaders who now share a seat at the table.

Below, we have highlighted some of the progress and changes witnessed in the data science sector since it first gained momentum in the early 2000s, through the recent days where it has gained widespread adoption. Here are some of the noteworthy developments. 

Domain Knowledge as Key Capability 

From 2005 onwards, the analytics team comprised mainly statisticians, mathematicians, and economists. These professionals had a good grasp of analytic concepts but lacked deep knowledge and insights into how most businesses operate. The data analytics team would evaluate data and give recommendations in areas they had limited experience, and this challenge only became obvious with time. 

Throughout 2010 and beyond, the rapid technological advancements and increased market competition forced many companies to rethink their business models. It became apparent across industries that a homogenous data science and analytics team was no longer effective. Hence, the need to have people with domain knowledge in the various fields/sectors related to the business. 

Today, most data science teams consist of engineers, MBAs, sales professionals, business executives, physicists, and even psychology graduates. Such a diverse team ensures everyone brings in their expertise to ensure in-depth business analysis guarantees proper decision-making.

Minimum Education Level and Key Areas of Study 

According to a study report by 365 Data Science, 80% of data scientists hold a minimum of a master’s degree. Those with a Ph.D. represent only 27%, meaning specialization isn’t a key requirement for getting into data science. This study conducted between 2018 and 2020 surveyed more than 1000 data scientists’ LinkedIn profiles and offered valuable insights into the entire industry.

The survey also found that between 2018 and 2019, computer science, economics & social sciences, and statistics & mathematics, were some of the most popular study areas among data scientists. However, 2020 saw data science & analysis as one of the top degrees offered by universities and higher learning institutions. Graduates from other fields such as natural sciences and engineering represented 11% of the data science professionals. 

Additionally, most women in the field (24%) were found to have earned a statistics & mathematics-related degree. However, men mostly earned a degree in data science & analysis (22%), with computer science coming in second (19%).

Common Programming Languages and Years of Experience 

Nowadays, some popular programming languages among data scientists are R, Python, SQL, Java, and C/C++. Over the years, Python has seen widespread adoption, overtaking R, which was once the most used language in various data science projects. Python’s user community, online documentation, ease of learning, and the language’s general capabilities puts it ahead of the rest. Besides having a deep understanding of these two programming languages, data scientists are also expected to have some knowledge of business intelligence software such as Tableau and PowerBI. 

As far as the years of experience is concerned, it’s worth noting that most data scientists stay in the profession for a longer period. This means there is higher competition when looking to enter into critical data science roles. However, this isn’t to say that qualifying for entry-level positions is difficult. In fact, there are several new opportunities for data scientists that are always coming up across different industries.

Join the Data Science Revolution  

Besides the shift in skills preferences and study areas, data science is also becoming a highly-interconnected field. The high-end automation and integration of various tools and software call for proper risk and compliance management. This is especially true when dealing with sensitive data and connecting workflows to and from the cloud.

That said, data science is all about the keen and accurate transformation of data into knowledge and actionable insights. Today’s data science teams are multifaceted, leveraging their unique individual skills and expertise to access, prepare, and analyze data for relevant information. 

Recent trends in the data science sector also point to more tailored education from universities and a key emphasis on certain study areas. If you are interested in data science, the field is full of great opportunities. You just have to get started.