The emergence of data science in many industries has attracted millions of fresh talents to grow their computer programming and machine learning skills and land a data science job in the past few years. As data science projects are mostly done within the framework of enterprise software projects, software engineering skills are mandatory for data scientists to perform. In this article, we will discuss core software engineering skills that are required for aspiring data scientists:

Object-Oriented Programming

Computer programming is probably the most critical part of a data science job. Programming skills are one of the crucial abilities required for data scientists to change turn the raw data into an effective analytics software user experience. That’s why data scientists need to be proficient in more than one programming language.

Within the computer programming skills required for data scientists, object-oriented programming (OOP) has an important place. While programming languages like Python and Java make it so easy s to comply with major OOP principles yet, data scientists need to understand the concepts related to OOP (such as objects, attributes, methods, and inheritance) to work in real-world software projects.

Full-stack development

In software projects, data scientists often need to deliver more than some machine learning modules in the backend. There is increasing demand from the employers that data scientists need to put the machine learning and analytics codes into production. Data scientists typically need to work with programming languages like Python, R, Java, and Scala. They also need to integrate the code with the frontend and deploy the software modules in big data production environments. Therefore, Full Stack Development is one of the most crucial software skills that data scientists need.

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