Original article was published by Pouyan R. Fard on Artificial Intelligence on Medium
Top 5 software engineering skills that data scientists need to master
Technical skills necessary to grow as a data scientist
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:
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.
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.
Databases & Big Data
The ability to work with structured and unstructured database technologies is also a necessity for data scientists within software projects. These database technologies can include SQL databases like PostgreSQL or NoSQL databases like MongoDB. Databases are so widely used in any software system that there is almost no escape from data science projects. There are also advanced big data technologies like Spark and Hive that enable working with Hadoop clusters.
Cloud computing is a significant trend for the big data and AI industry in 2020. Using cloud environments like AWS, Microsoft Azure, or Google Cloud makes it quick and straightforward to deploy AI-powered software modules and integrate them with operational software. Therefore, data scientists need to acquire to develop AI and big data solutions on top of cloud infrastructure.
For data scientists, learning DevOps is essential. DevOps can be used to optimize the deployment of software components related to data pipelines, model training, model testing, model predictions, and model deployment. Ensuring DevOps best practices are the core of functional big data pipelines in the production environment, whether on-premise or cloud.
One of the areas in DevOps widely used software projects is continuous integration / continuous delivery (CI/CD). Without the basic knowledge level required to work with CI/CD pipelines, data scientists cannot effectively collaborate with software teams. In the meantime, becoming proficient in cloud computing makes it easier for data scientists to learn and leverage the already existing DevOps capabilities within the cloud infrastructure and this helps the data scientists to adapt their skills faster to the requirements of working in production environments.
The times are changing, and new technologies are emerging every day. The process of work in software projects is continuously evolving. Therefore, the data scientists must adapt to these new technologies for better performance at enterprise software projects.
About the Author:
Pouyan R. Fard is the Founder & CEO at Fard Consulting & Data Science Circle. Fard Consulting is a Frankfurt-based boutique consulting company serving companies in various industries. Pouyan has years of experience advising companies, from startups to global corporations, on data science, artificial intelligence, and marketing analytics. He has collaborated with Fortune 500 companies in pharma, automotive, aviation, transportation, finance, insurance, human resources, and sales & marketing industries.
Pouyan is also leading the Data Science Circle team to build a career hub between employers and data science talents. DSC’s mission is to nurture the next generation of data scientists through career training and helping the employers to find top talents in big data.
Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in developing state-of-the-art solutions in machine learning and artificial intelligence.