Different organizations may have different ways to define the core job roles and key responsibility areas for their big data roles. While the roles of a Data Scientist and a Data Analyst may sound very alike, there are some core differentiators too.
As a result of the roles being alike, in many organizations, there are overlapping responsibility areas between what a data scientist does and what a data analyst does.
While a data scientist and data analyst may require some common skills to perform their tasks better, some skills may need to be stronger in the former.
Chandra Prabha, currently a Project Manager at DemandMatrix adds, “For both data scientists and data analysts, alongside all the coding and technical mandates its very critical for a person to understand the Business requirements , the why of the ask, spend good amount of time to research and really understand the consumption of it.... And then comes the technical implementation part later. The ability to understand the domain, find pattern & correct reasoning, insights , problem solving acumen becomes imperative. A combination of both is unstoppable and missing any one of these aspects makes you incomplete."
So what skills matter most to whom?
1) Robust business acumen and visualization skills:
While a data scientist needs to have a strong combination of both, a data analyst would benefit from basic visualization skills. Specialized business skills are a more pressing need for a data scientist.
The core reasoning behind this is that a data scientist tends to approach business issues and works on those issues that have a greater while business value. A data analyst usually just approaches the business issues. To elaborate, a data analyst is usually given the task of solving the questions given by the business, a data scientist tries to formulate questions to find answers that will benefit the overall business. That is why one of the key responsibility areas of a data analyst is to run SQL queries that answer the most complex business questions.
2) Proficiency in ML and in building statistical models:
A data scientist needs stronger proficiency in machine learning and statistical modeling than a data analyst, because these find huge applications in predictive modeling, recommendation systems, spatial models, data classification and clustering. A data analyst may not require strong proficiency here. That is why a data scientist should have solid knowledge in statistics, mathematics, data mining, correlation.
On the other hand, a data analyst would benefit from advanced skills in data architecture’s tools and components. A data analyst usually needs only excel in data storing and retrieving.
3) Predictive Analytics:
Deriving advanced predictions based on the information in past data sets is always a primary responsibility area for data scientists. A data analyst in the other hand can derive insights from big data sets and doesn’t usually use predictive modeling to perform his/her role.
4) Insights from single vs multiple sources of data:
A data scientist usually needs to breakdown and understand data from multiple sources to connect the dots. A data analyst will usually do the same task after analyzing one single source of data, like a CRM platform.
While both data analysts and data scientists work with data, the key difference lies in the actual scope of work they do, in what they actually do with the data. In short:
Data analysts study large data sets to identify trends, build charts, create visualizations to present their findings…all of this is with the aim to help businesses make strategic decisions.
Data scientists on the other hand help design, construct and implement, new processes for data modeling and further production using prototypes, algorithms, predictive models and custom analysis.