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).
Machine Learning Proficiency Among Data Professionals
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.
I compared the proficiency in machine learning across four data science job roles: Business Management, Developer, Creative and Researcher. The results are located in Figure 1. As you can see, data professionals in the job role of Researcher reported the highest level of proficiency (36% reported, at least, an advanced level of proficiency) in machine learning. Developers had the next highest level of proficiency (30% reported, at least, an advanced level of proficiency). Creatives and Business Management data professionals reported the lowest level of proficiency in machine learning (25% and 22% reported, at least, an advanced level of proficiency, respectively).
Next, I examined the relationship between machine learning proficiency and satisfaction with work outcome for each of the four job roles. The correlation between machine learning proficiency and satisfaction with outcome of work was statistically significant for all four job roles (see Figure 2). Generally speaking, as proficiency in machine learning increased, satisfaction with work outcome increased. For the data professionals who identify as Business Managers, their satisfaction peaked at an intermediate level of proficiency.
Machine learning is a hot area in the world of Big Data. As interest in machine learning continues to grow, businesses will need professionals who possess these skills.
The results of the Evans Data Corp. survey are in line with the current survey results. They found that 36% of the developers employ elements of machine learning in their big data projects, and the current survey showed that 30% of developers possess advanced/expert level of proficiency in machine learning.
Generally speaking, the more proficient data professionals are in machine learning, the more satisfied they are with the outcome of their analytics projects. The relationship between machine learning and satisfaction with work outcomes tends to be a positive and linear one, except for data professionals in Business Management roles. Their satisfaction with outcome of analytics projects peaked at an intermediate level of proficiency. Due to the nature of their job roles, advanced machine learning knowledge may not play a key role in their day-to-day work.
Different data professionals possess differing degrees of machine learning talent. My earlier analysis of our data science survey found that the machine learning skill is really a part of the “Math/Statistics” domain, an area where Researchers excel, not Developers. So, it’s not surprising that machine learning talent is the most prevalent among Researchers. Still, you are able to find machine learning talent in each of the four job roles studied here. It appears that looking at data professionals in Research and Developer roles would be the best starting point for businesses and recruiters who want to find data professionals skilled in machine learning.