The Five Types of Data Scientists you Will Meet in Your Life


Data science is one of the fastest-growing fields in the technology world. There are various people involved in the data analytics field as actuarial scientists, analysts, mathematicians, business analytic professionals, and software programming analysts. People serving in these fields are well-appointed with data scientist skills and are most in-demand in the world of business. To know what are the different types of data scientists you are at the right place.

 

Data Scientist as Statistician

This is data analysis in the traditional sense. The field of statistics has always been about number crunching. A strong statistical base qualifies you to extrapolate your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization, and quantitative research are some of the core skills possessed by statisticians which can be extrapolated to gain expertise in specific data scientist fields.

 

Data Scientist as Data Engineer

This is a software-development-intensive role that thrives on programming skills and the ability to make data tangible to data scientists. Data engineers manage large datasets and do the data cleaning, aggregation, and ETL processes. But they also build data pipelines to get the data to the analysts and scientists within an organization.

 

Data Scientist as Mathematician

Mathematicians have routinely been connected with broad hypothetical research; however, the development of big data and data science has changed that perception. Mathematicians have been increasing more acknowledgment in the corporate world than ever before, infer-able from their profound knowledge of operations research and applied mathematics.

 

Data Scientist as Vertical Expert

The Vertical Expert is a data scientist who has lots of experience in a particular domain. (S)he probably started out as a generalist, but after years of working in a particular industry, has developed the business knowledge necessary to solve problems in a certain context. They can be valuable for their obvious back-knowledge from the get-go, but the drawback can be difficulty thinking outside the box on standard problems or questions.

 

Data Scientist as Research data Scientist

Research data scientist is good at handling large data sets. His/her work may not be directly tied to the organization’s outputs, but it is crucial for activities like report-making, summary presentations, and other analytical purposes. The skills of this type of data scientist are especially useful in large think tanks and financial and research institutions.

 

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