This article was posted by Bob E. Hayes on Customer think. Bob, PhD is Chief Research Officer at Appuri. He a scientist, blogger and author on CEM and data science.
Data scientists have a variety of different skills that they bring to bear on Big Data projects. These skills cut across Subject Matter Expertise, Technology, Programming, Math & Modeling and Statistics. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning).
While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning. In today’s post, I wanted to explore how machine learning skill proficiency varied across different types of data professionals. In a joint study with AnalyticsWeek, we surveyed over 1000 data professionals and asked them about their skills, team makeup and other demographic information. Specifically, we asked them to indicate their level of proficiency across 25 different data skills (including machine learning), satisfaction with work outcomes of analytics projects and their job role.
The original article can be found here.