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Role of Data Scientists in Business Processes

Data scientists work with business associates to collect and analyze structured and unstructured data. Data scientists collect data from various sources like social media, mobile networks, emails and convert that unstructured data into structured data. A data scientist can efficiently analyze the business and take decisions to improve inventory and reduce production errors. They use various business processes, predictive models, and algorithms to extract and analyze data related to the business needs and convert that data into a form suitable for the business. Data scientists are actually experts in both technology and social sciences, and we can say they are capable enough to fulfill the social needs of the business with advanced technologies. A data scientist must have some of these core skills related to business like analytical thinking, interpersonal skills, business intuition, critical thinking, inquisitiveness. Data scientists can support both developers and leaders to make good decisions to create better products. The demand for data scientists is increasing day by day, but they are shortcoming. So we can say that it will be a good career opportunity in the near future. To become a data scientist is merely difficult because it is a hybrid collection of many fields like mathematics, programming languages, software engineering, knowledge of visualization tools, database management systems, etc. Some people don’t even know where to start from. So, the person who wants to become a data scientist must have some technical background means they must know about any programming language, know to handle database systems. In addition to the technical knowledge, he/she must be proficient in statistics, algebra, and other mathematical tools.

Following are the technical skills required for a person to become a data scientist:

  • Programming Language, Basically Python is the most preferred language, but you can go with Java, C/C++, or Perl also.
  • SQL database management system.
  • Machine learning and Artificial Intelligence.
  • Data Visualization tools like Matplottlib, Tableau, d3.js, etc.
  • Big data in collaboration with the latest technologies like Spark, Hadoop, etc.
  • R programming.
  • Mathematics(probability and statistics)
  • Communication

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