{"id":6341,"date":"2018-10-15T10:09:33","date_gmt":"2018-10-15T14:09:33","guid":{"rendered":"https:\/\/blog.brainstation.io\/?p=6341"},"modified":"2018-10-23T10:23:21","modified_gmt":"2018-10-23T14:23:21","slug":"3-reasons-why-developers-make-great-data-scientists","status":"publish","type":"post","link":"https:\/\/brainstation.io\/blog\/3-reasons-why-developers-make-great-data-scientists","title":{"rendered":"3 Reasons Why Developers Make Great Data Scientists"},"content":{"rendered":"

Data Science has rapidly expanded in the last two years, with Machine Learning Engineers, Big Data Engineers, and Data Scientists ranking amongst the <\/span>top emerging positions on LinkedIn<\/span><\/a>.<\/span><\/p>\n

Data science is \u201cinherently multidisciplinary,\u201d as John Foreman, MailChimp\u2019s Vice President of Product Management, once said, explaining why Data Scientists were \u201ckind of like the new Renaissance folks.\u201d The field draws on elements of computer science, statistics, and mathematics; a unique blend that is not typically covered in university degree programs. <\/span><\/p>\n

This is one of the reasons why <\/span>data science training, courses, and diploma programs<\/span><\/a> are increasing in popularity, helping professionals master the skills they need to meet the demands of this rising field. <\/span><\/p>\n

And when it comes to transitioning into a career in data, one group of professionals may be in better position than most: Developers. <\/span><\/p>\n

Here are three reasons Web Developers make great Data Scientists. <\/span><\/p>\n

They\u2019re Got a Background in Data Curation<\/b><\/h2>\n

Data Science typically covers two broad areas: data analytics and data curation. Analytics deals with analyzing and extracting relevant insights and knowledge from data. This is the activity that people most often associate with data scientists: crunching numbers and producing actionable insights and predictive models. But there is another side to data science: figuring out how to collect, manage, preserve, document, transform, alter, and access the data effectively and efficiently in order for analytics to be possible. In academia, these activities are often referred to as data curation<\/span>.<\/span><\/i><\/p>\n

\"data<\/a><\/strong><\/h3>\n

Data curation involves the capturing, modeling, management, documentation, storage, transformation, and retrieval of data. In the professional world, titles for a specialist in data curation include <\/span>Data Engineer<\/span><\/i>, <\/span>Data Developer<\/span><\/i>, <\/span>Business Intelligence Developer, Big Data Specialist,<\/span><\/i> or sometimes just <\/span>Data Scientist.<\/span><\/i><\/p>\n

These specialists would need to have a strong understanding of the following aspects:<\/span><\/p>\n