Data Science vs Data Analytics

Data Science vs Data Analytics


The job of Data Scientists and Data Analysts can be described as interconnected, but distinct differences divide these two roles. From mathematical expertise to interpersonal skills, both fields look to professionals with skills ranging from problem solving and investigate know-how to a technical skillset that is refined and expansive.

Yet, when delved further, the divide between analytics and science comes down to their scope and viewpoint. These viewpoints see Data Analytics adopt a micro view whilst Data Science looks macro in its approach.

Data Analytics

Data analytics is the process of evaluating data. Using analytical and logical reasoning, it is the Data Analyst’s job to derive business intelligence and valuable information. These insights are achieved through the cleaning, organising, transforming and enriching of raw, complex data through methods such as data mining, business intelligence and text analytics. They extract insights from a single source using programing languages such as Python, R and SAS. Comparatively, Data Scientists explore multiple disconnected sources and require tools beyond programming languages. 

Data Analytics could be defined as a more focused, less in-depth version of data science, working best when a concentrated approach is adopted. As the ones responsible for the day-to-day analysis, Data Analysts work to answer defined questions using existing data.

The average annual salary of a Data Analyst in the United Kingdom is £26,366, $69,291 in the United States and in Europe ranges from €24,700 in Italy to €83,000 in Switzerland.

Data Science

Data Science is an umbrella term that takes data analytics one step further. Rather than answering specific questions as data analytics does, data science looks to ask the questions and solve problems that are yet to be identified. Data Scientists work to find actionable insights through data visualisation tools.

The Data Scientist skillset is broad with machine learning and statistical model building a considerable part of a Data Scientist’s scope. When obtaining answers, Data Scientists uses several techniques spanning computer science, deep learning, machine learning, statistics and predictive analysis.

The average annual salary of a Data Scientist in the United Kingdom is £50,725, $120,495 in the United States and in Europe ranges from €37,200 in Italy to €88,400 in Switzerland.

In summary

Both roles share an overlap in tools, output and process, and share the common goal of extracting insights from data, however their separation boils down to their depth of work. A Data Scientist must be able to clean, interpret, and enrich data, just as a Data Analyst does. However, a Data Scientist requires a depth and expertise in these skills that surpasses a Data Analyst, along with technical grasp of machine learning and predictive analytics that is foreign to most in analytics.

Data Scientists extracts insights that look to what questions should be asked, whereas Data Analytics looks to answer defined questions being asked. A Data Scientist questions the ‘what if’ scenarios.

Long-term career prospects

Despite differences, both roles promise professional longevity. The skills of both are in high demand as more businesses invest in extracting value from their data. With this growing demand and limited talent pool, now is the best time to be entering the Data Analytics and Data Science job market.

To browse the latest Data Analytics and Data Science roles, click here.


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