A Simple Way to explain the distinction between Data Scientist and other Data-driven roles

Original article was published on Artificial Intelligence on Medium

A Simple Way to explain the distinction between Data Scientist and other Data-driven roles

Data scientists have a unique role across both public and private sectors, and that role is different from the roles of others, such as data engineers, statisticians, and business analysts.

Source: https://www.edureka.co/blog/data-analyst-vs-data-engineer-vs-data-scientist/

Data Engineers vs Data Scientists

Data engineers work to make sure data flows smoothly between the source and the destination. The source is where data is collected, and the destination is where data is extracted and processed. In a nutshell, data engineers optimise data flow.

Data scientists work to make sure that value is extracted from data smoothly. Data scientists optimise data processing and work with data engineers as well as business people to define metrics, establish how data is collected, and ensure that data science processes work well with enterprise data systems.

When Data Scientists + Data Engineers

When data scientists and data engineers work together, the data scientists need to write code that’s reusable by the data engineers.

Data Scientists vs Statisticians

The amount of data that data scientists work with is often massive, so they spend a lot of time with tasks like large-scale data collection and data cleaning. Data scientists then try out different methods to create machine learning models, and then they choose the method that results in the best model. Also, data scientists implement algorithms that process data automatically, and this enables data scientists to provide automated predictions and actions.

Statisticians rely on more traditional and smaller-scale methods of data collection, such as surveys, polls, and experiments. Statisticians often work on improving one simple model to fit the data best.

Data Analysts vs Data Scientists

Business analysts focus on database design and ROI assessment, and some business analysts work on financial planning and optimisation, and risk management.

Data scientists can help business analysts. For example, data scientists can help automate the production reports and speed up data extraction. , according to data scientist Vincent Granville, the collaboration between data scientists and business analysts has helped business analysts extract data that’s 100 times larger than what they are used to, and ten times faster.


The below table illustrates the different skill sets required for Data Analyst, Data Engineer and Data Scientist:

Source: https://www.edureka.co/blog/data-analyst-vs-data-engineer-vs-data-scientist/

Real-life Examples

Data scientists can automate through data analysis and algorithms (data model) such as using automation to pilot drone, cars and many more. The model can automate product recommendations on Amazon or friend recommendations on social media such as Facebook or Instagram. Data scientists can also create a data model to automate computational chemistry to simulate new molecules for cancer treatment or weather forecasts. Automation can also help to estimate the values of houses, or matching a movie to a user to enhance the user experience. Such automation can also help return highly-relevant results to Google searches or detect credit card fraud and tax fraud.

As we can see, the power of data science is changing our world, and that’s a great reason for us to continue learning.