What skills does a typical data scientist have?
Data science has emerged as the most sought-after career post-2017. However, a data scientist has a plethora of roles to play in the data science field. Apart from data analysis, he needs to have a good understanding of business data. Data science is a vast domain and as a data scientist a professional needs to be flexible from data sourcing to data execution.
Different industries or companies may ask for different skills for the post of a data scientist. However, in this blog, we will discuss on the skills which are broadly categorized based on technical and non-technical skills.
Necessary technical skills that a data scientist must acquire
The two most required technical skills in data science are:
- Computer science
Skills as an analyst
A data scientist can play either as an analyst or as a programmer.
To play as an analyst: Most of the analysts have a strong educational background like Master degree or Ph.D. in statistics, Mathematics, Economics or Computer science. They need to work on complex algorithms for data analysis.
The role of a programmer: Python or R is the two most used languages for data science-related programming. Hence, mastering any of the two languages are must as a data scientist.
Skills in computer science
Coding: A data scientist must possess good programming skills. Apart from R and Python, he must be comfortable with languages like Java and C++.
Knowledge of Hadoop: Data science activity most of the cases extend to big data related activities. Hence, although not must knowledge of Hadoop is an added advantage for a data scientist.
Familiarity and flexibility with different data forms: Data scientists need to handle unstructured data in most of the cases. He must have the love for numbers.
SQL coding: A concept and coding capability on SQL are necessary to work with data.
Necessary non-technical skills that a data scientist must acquire
Like every profession, data science also demands some soft skills and business skills.
Curiosity: Data science is a field where as a data scientist a professional needs to handle a large set of data which are indeed nothing interesting. But a data scientist must have that intellectual capability to grab the detail out of those numbers.
Business knowledge: The first step to solve any problem is to understand the problem properly. Hence, a data scientist must have proper business knowledge he is dealing with to understand the goal of solving the problem or the purpose of the analysis.
Communication skill: A data scientist may have to communicate with other business teams or sales teams for clarifications. Hence, he must have good communication skills to articulate the problem statements appropriately.
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