Remote Data Science: What It Needs to Make It Work?

Remote Data Science: What It Needs to Make It Work?

What do you need to know about remote data science and its impact?

Data science is undoubtedly an emerging field of study. It helps organizations with processing a large volume of data and deriving insights for business growth. The data team within a company opens new opportunities for productivity and enable a competitive edge over their peers. As remote work in the corporate arena is not new, it has become crucial in today's COVID-19 world. Every company these days are forced to turn to work from home to keep their business running. In this scenario, the role of data science has also shifted to remote work facing a new set of challenges such as access to systems, team collaboration, infrastructure and productivity.

Data science comprises a diverse range of roles including data scientists, data architects, data engineers, machine learning engineers, and more. As these names are different, their roles are somewhat similar as they all are responsible for organizing and assessing voluminous amounts of data with other liabilities.

When looking into the advantages of remote data science, working remotely as a data scientist paves the way for an organization to be more agile. Remote work helps those who are fully remote as well as infusing best practices that benefit distributed teams and merge data practices across an organization.

What does it require to Make Remote Data Science Work?

Data science professionals typically are mid- to senior-level employees. They help businesses with every step of their action to achieve success. When it comes to the remote work environment, they may face substantial emerging challenges, especially data access. To overcome such hurdles, data teams need centralized storage where they can store their work. According to Florian Douetteau, the CEO of Dataiku, a central location inspires good data governance and collaboration practices. And data teams can easily work together on a data science and machine learning project.

Implementing a single access point for remote data science enables easier data access without the need to move them for processing. This is imperative as teams require instant access to format and schema data irrespective of where it is stored including MPP database, cloud databases, NoSQL stores, Hadoop clusters and more.

Leveraging the right end-to-end platform and tools can also help data teams working remotely and take care of everything related to data. Such tools manage data science processes all the way from business understanding to deployment. A good data science and machine learning platform can offer a remote data team the building blocks to their data science project.

Moreover, working on any project requires a robust collaboration across teams within an organization. This robust and proactive collaboration also applies in remote data science teams. This is because data projects are not only about data, they also require strong involvement from diverse teams to make the project succeed.

Over the last few years, the field of data science has been experiencing tremendous disruptions, making the work of data scientists easier and effective. Mastering data science demands a unique set of skills and knowledge including programming languages, Query languages, NoSQL databases, big data processing frameworks, data visualization, machine learning techniques, applied statistics, and excellent business acumen along with strong communication skills.

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