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Assess Your Data Science Expertise

What kind of a data scientist are you? Take the free Data Skills Scoring System Survey at http://pxl.me/awrds3

Data_Science_Skills

Figure 1. Data science skills

Companies rely on experts who can make sense of their data. Often referred to as data scientists, these people bring their specific skills to bear in helping extract insight from the data. These skills include such things as Hacking, Math & Statistics and Substantive Expertise (see Figure 1). In an interesting study published by O'Reilly, Harlan D. Harris, Sean Patrick Murphy and Marck Vaisman surveyed several hundred practitioners, asking them about their proficiency in 22 different data skills. They found that data skills fell into five broad areas: Business, ML / Big Data, Math / OR, Programming and Statistics.

Complementary Data Skills Required

There are three major tasks involved in analytics projects. First, you need to ask the right questions, requiring deep knowledge of your domain of interest, whether that be for-profit business, non-profits or healthcare organizations. When you know your domain area well, you are better equipped to know what questions to ask to get the most value from your data. Second, you need access to the data to help you answer those questions. These data might be housed in multiple data sources, requiring a data worker with programming skills to access and intelligently integrate data silos. Finally, you need somebody to make sense of the data to answer the questions proposed earlier. This step requires data workers who are more statistically-minded and can apply the right analytics to the data. Answering these questions could be more exploratory or intentional in nature, requiring different types of statistical and mathematical approaches.

Getting value from data is no simple task, often requiring data experts with complementary skills. After all, I know of nobody who possesses all the data skills to successfully tackle data problems. No wonder why data science has been referred to as a team sport.

Data Skills Scoring System (DS3)

We at AnalyticsWeek have developed the Data Skills Scoring System (DS3), a free web-based self-assessment survey that measures proficiency across five broad data science skills: business, technology, math and modeling, programming and statistics (see press release here). Our hope is that the DS3 can optimize the value of data by improving how data professionals work together. If you are a data professional, the DS3 can help you:

  1. identify your analytics strengths
  2. understand how to improve your analytics skill set
  3. identify team members who complement your skills
  4. capitalize on job postings that match your skill set

While the publicly available DS3 is best suited for individual data professionals, we are customizing the DS3 for enterprises to help them optimize the value of their data science teams. By integrating DS3 scores with other data sources, enterprises will be able to improve how they acquire, retain and manage data professionals.

Find out your data skills score by taking the free Data Skills Scoring System Survey: http://pxl.me/awrds3

We are also conducting research using the DS3 that will advance our understanding of the emerging field of data science. Some questions we would like to answer are:

  • Do certain data skills cluster together?
  • Are some data skills more important than others in determining project success?
  • Are data science teams with comprehensive data skills more satisfied with their work than data science teams where some skills are lacking?
Respondents will receive a free executive summary of our findings.

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