10 Data Science Skills You Need to Improve Project Success

Last week, I identified the top skills across different data science professionals. The results of our survey of 620+ data professionals showed that, while data scientists possess many different skills (we looked at 25 skills), some skills are more popular/common to data scientists compared to other skills. For example, the top two skills were communication and managing structured data while the bottom two skills were big and distributed data and cloud management. Just because a skill is popular (unpopular) among data professionals, however, does not necessarily mean it’s important (unimportant) for project success. In this week’s post, I will examine the degree to which data science skills are related to satisfaction with the outcomes of analytics projects. Are some data science skills more closely associated with successful project outcomes?

A common method of ranking data science skills is based on the frequency with which professionals possess the skills. Skills that are held by most data scientists are deemed the “top” data science skills. I did this last week. Another way to rank data science skills is based on their importance to the success of projects. To rank data science skills on their importance, we need to clarify what we mean by “importance.” Determining the importance of a data science skill is an exercise in quantifying the statistical relationship between skills and the quality of the outcome of analytics projects. Here is how we determined the importance of data science skills.

In our ongoing study of data scientists, we ask data professionals to indicate their proficiency in 25 different data science skills in five skill areas (see Figure 1) using the following scale:

Additionally, in the study, we ask data professionals to indicate their their satisfaction with the outcomes of analytics projects on which they work. This rating is on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). I am using this score as a measure of project success; higher satisfaction ratings indicate better outcomes of projects.

Here is how we determined “importance” of data science skills. For each of the 25 data science skills, I correlated proficiency ratings of each skill with the satisfaction rating. Each correlation shows us the degree of relationship between a specific skill and the satisfaction with the outcome of analytics projects. Skills that show a high correlation with satisfaction with outcomes are more important to project success compared to skills with lower correlations.

I ranked the 25 data science skills according to the magnitude of their correlation with satisfaction with project outcome. This list appears in Figure 2. The first 10 skills listed in the figure (from left to right) were the skills most closely associated with good project outcomes. The 10 most important data science skills to project success were:

Many of the important data science skills are highly quantitative in nature; in fact, 8 of the 10 skills include Math and Statistics skills, including Data Mining and Viz Tools, Statistics and statistical modeling, Science/Scientific Method and Algorithms and Simulations.

The correlations between data skills proficiency and satisfaction with project outcome appear in Table 1. On average, we see that proficiency in data skills is more closely linked to satisfaction with work outcomes for Business Managers (average r = .29) and Researchers (average r = .30) compared to Developers (average r = .18) and Creatives (average r = .18). That is, higher levels of proficiency in data science skills leads to much better project outcomes for Business Managers and Researchers compared to Developers and Creatives.

Next, I looked at the importance of data science skills by job role. This depiction also appears in Figure 2 (and Table 1 in detail). For each of the job roles, I graphically indicated the importance of data science skills in driving satisfaction with project outcomes.