The terms data analysis and data visualization have become synonymous in everyday language in the wider data community, but the two are quite different. Data analysis is an exploratory process ...

Data analyst and data scientist roles are two of the hottest jobs on the market right now. But what exactly is the difference?

While a master's in data analysis or data science methods is not necessarily required for a successful career, it can help you be competitive in the job market. The more education you have, the more ...

Data Science covers every type of data analysis, which might or might not require computer use, and it is more closely related to the mathematics field of statistics, which is inclusive of the ...

Data science is a method to transform business data into assets that help organizations improve revenue, reduce costs, seize business opportunities, improve customer experience, and more.

Below are some examples of the profound differences between the sometimes inflexible IT department and the often swashbuckling data scientists.

The key difference between data analysis and data science is that the former primarily looks to interpret existing data, while the latter involves creating new ways of doing so.

Many enterprises, vendors, and startups often confuse the role of data scientist and data engineers. While the overlap of these roles is substantial they’re not particularly interchangeable.

Business intelligence and data science often go hand in hand. Both fields focus on deriving business insights from data, yet data scientists are regularly touted as the unicorns of big data analysis.

The main differences between data modeling and data analysis Data modeling and analytics are both integral to data management and data-driven operations.