Data Analyst vs Data Scientist

According to a recent BrainStation Digital Skills Survey, 79 percent of data respondents did not begin their career in data, and 65 percent have been working in it for five years or less. This suggests that while the data field is growing fast, it is still in the early stages of its development, with multiple career paths available. In fact, the survey found that while the aforementioned demand is typically associated with “Data Scientists,” a majority of data professionals currently work under different titles, most notably “Data Analyst” and “Business Analyst.”

Data Analysts find meaningful insights from data, effectively finding answers to a given set of questions from data. Data Scientists, on the other hand, aim to predict the future using past patterns and trends. In short, Data Scientists develop, Data Analysts optimize.

Data Scientist is generally a more senior position involving more technical expertise. Data analytics can be considered a more entry-level field; it’s more narrowly focused on business intelligence (BI), which might remove barriers to entry for people who have the skills but not the degree.

Traditionally speaking, the difference has been rooted in professional and educational backgrounds, with Data Scientists typically earning graduate-level, or higher, degrees, while Data Analysts can enter the field with a bachelor’s degree and specialized data analytics training. Increasingly, a distinction is found in the tools different data professionals rely on, too: Data Scientists tend to have more experience working in more programming languages—less Excel, more Python—or with other relevant technology.

Here are some of the other differences between Data Analysts and Data Scientists.

When it came to what technology data professionals were using, Excel emerged as the most widely used tool in the Digital Skills Survey, at close to 81 percent. This was followed by SQL, Python, and Tableau.

Excel’s presence at the top of the list was somewhat surprising, so we dug a bit deeper to see how these responses were broken down by job title. We looked at the five major categories of respondent roles (Data Analyst, Business Analyst, Data Scientist, Researcher, Data Analytics Manager) to see the distribution of tools they used.

We found that Data Scientists relied much less on Excel. In fact, respondents with this job title were the only ones to cite the programming language Python as their most frequently used tool. Data Scientists also reported the usage of a much wider range of secondary tools, including SQL and Tableau. You can learn how to use tools like these as part of BrainStation’s comprehensive Data Science curriculum.

As mentioned above, the Data Scientist job title traditionally implied a more senior level of experience and training, and these survey findings would seem to back this up; additional knowledge and skills would provide more exposure to a programming language like Python, as well as any additional relevant technology.

Most data respondents in the digital skills survey said they spent the bulk of their time wrangling data and cleaning it up. The primary use of data, meanwhile, was devoted mostly to the optimization of existing platforms and products, as well as the development of new ideas, products, and services.

When we broke this down by the major job titles, another difference emerged between Data Analysts and Scientists: The majority of Business Analyst and Data Analyst respondents tend to focus more on optimizing existing platforms and products. Data Scientists, on the other hand, work primarily on developing new ideas, products, and services.

The difference here may again be explained by experience and knowledge levels, as more skilled and experienced Data Scientists would likely be more involved in high-level, strategic planning and development.

Where these two job titles unite, is in their expectations for the future. 77 percent of data respondents say they don’t work with artificial intelligence (AI), which may be surprising, given the attention AI has received. It may be, though, that since data respondents are more familiar with AI, they are more hesitant to use the term than say, Marketers. However, data respondents did feel that Machine Learning and AI would have the most impact on the next five to 10 years, with blockchain, and internet-of-things technology coming in third and fourth. If this turns out to be true, it looks like this fast-evolving field will continue to change.