Analytics

Data scientists and engineers at the online data science education platform focus on attribution, customer lifetime value and the ideal customer profile.

However, the complexity and volume of data every business accumulates is a common challenge for those that need to make decisions. Data is a continuous and always growing consideration, and it can be particularly challenging for larger enterprises.

Make enterprise data more accurate, and instantly actionable, by adding automated data integration to your stack. Today’s enterprises and medium-sized companies are looking to ensure that critical business decisions are guided by rigorous data analysis. They have scaled up their analytics teams (composed of data engineers, data scientists and data analysts), and their IT departments have tried to meet the needs of those teams.

Timely, accurate and trusted data has never been more important than it is now during this pandemic. Since late summer, many areas across the UK have had more stringent restrictions imposed to reflect the growing number of cases. Similarly, Test and Trace uses information on who we’ve been in contact with to provide guidance for when we should self-isolate, which in turn helps us personally manage the risk to those around us.

Automated data integration can help you jumpstart your machine learning efforts. Learn about the modern data science stack. It’s an oft-cited factoid that data scientists spend only 20% of their time doing actual data science work, while the rest of their time is spent on doing what is often delightfully referred to as “data munging” — that is, obtaining, cleaning, and preparing data for analysis.