Let me compare: Analytics and Data Science - each their own unique characteristics
Business analytics and data science are related disciplines since they both focus on analyzing and exploring data in search for business insights. To do so data science often uses predictive analytics tooling to deploy algorithms or search for patterns and correlations, whereas business analytics more uses those algorithms. In a previous article we spoke about using both business analytics as predictive analytics techniques to get insights from data. But what is the difference between these disciplines? Where is the one taking over from the other, and where do they overlap? Various definitions are being used and people still debate on the boundaries of both disciplines, but the definitions most applicable are:
Business Analytics: research and extract valuable information from structured and unstructured sources to explain historical, current and future business performance. Determine the best analytical models and approaches to present and explain insights to business users
Data Science: design, develop en deploy algorithms through statistical programming that support business decision-making tools. Manage large amounts of data and create visualizations to aid in understanding structures and patterns of data
A very good article exactly stipulating the differences in characteristics between data analytics and data science can be found on the DataScience101 page via this link. It provides an infographic from the faculty of online MS in Analytics at American University. Read about the backgrounds and skills of both business analysts and data scientists. Where do they work and what exactly do they do?