데이터 사이언스 아티클

In this blog post, we discuss five of the best online certifications, each of which specializes in different software.

The immense potential of data science and analytics is well recognized by businesses across all industries. But for these data science initiatives to succeed and scale, the data must first be relevant, accessible, and of high quality. This is where data governance tools come in to serve as vital enablers in the automation of governance operations and data stewardship efforts.

In a recent webinar, we discussed how data governance is a key component of an organization’s data strategy and enables it to harness the full value of data. For a data governance plan to succeed, it is important that the right tools and technology are employed.

An academic study of online education during the pandemic, published by the National Academy of Sciences, shows that users across all socioeconomic groups engaged more with DataCamp in 2020.

We recently hosted a panel with Bloomberg and NatWest Group on the importance of data literacy, and how to go about operationalizing data skill transformation programs.

Government agencies have access to troves of data. In order to fully harness its value, there needs to be a committed and coordinated effort towards data democratization within and across government agencies.

A spotlight on one of our inspiring nonprofit giveaway partners.

Discover whether you should learn a data science and analytics language, such as Python or R; SQL; spreadsheets; or a business intelligence tool.

Discover how data science is being used to revolutionize the trucking industry, from A/B Testing and econometrics to machine learning and self-driving cars.

Life in the military teaches you some very useful skills, traits, and habits. It also teaches you how to get started with data science by doing it.

These 10 simple rules will help researchers and practitioners in data science overcome the frustrations they might feel when working with each other.

Discover how to overcome the steep learning curve of version control for data science, while also taking into account best practices and recommendations.