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Skills of a Data Scientist

In this world of advanced and futuristic technology which are mostly data driven, Data Scientists are the most sought after people to find solutions to data problems across various industries, ranging from tech to healthcare to government agencies. Depending on the needs of the industry, the requirements of a data science job can vary from cleaning and visualize data to training an AI chatbot. However, there are a few important skills which a Data Scientist should adhere to while applying for a job in any industry. Here are the 5 must have skills for a data scientist –


Mathematics and Statistics

Mathematics and statistics are the foundation of data science and sound knowledge of both helps a Data Scientist to understand how to deal with a dataset. Knowledge of calculus, linear algebra, descriptive and inferential statistics is required to evaluate the data properly and decipher the relevant parameters to come up with a data-oriented solution.


Programming is a fundamental skill that a Data Scientist should have as it helps to augment statistical methods, analyze and visualize datasets and also to create automation tools to deal with redundant tasks. Knowledge of R and Python programming are important as the former is required mostly for statistical analyses and the latter to work with development of tools based on the performed analyses. Companies working on software development are more interested in Python as it helps to create APIs or deploy code on the server based on the tools created for analyses or automation.

Domain Knowledge

Industry knowledge and product intuition give the ability to understand the complex system which generates all the data. Product knowledge helps a Data Scientist to create hypotheses of different ways a system can behave and produce results. Also, the need for defining metrics of performance of a product and debugging analyses helps a company to keep track of the progress along with various hindrances faced while developing a product.


Effective communication is the key to success and it holds true across all domains or job roles. One of the biggest challenges of being Data Scientist is to explain the analyses to people who are handling the business and a better storytelling method provides the decision makers with a clear and concise way to effectively act on the insights of the analyses. Data visualization is another fundamental skill to learn as a good graph is always better than a bunch of text and numbers.


Being a good Data Scientist also means to use the power of creativity while dealing with data. Creative thinking helps to spot trends, find connections between datasets, cost and time effective ways to perform analyses or produce results, and communicating the results of the analyses in an informative and attractive manner.