The Structure of Data Science Skills

Data science, undoubtedly, requires numerous and varied skills. Analytics Week, the world’s largest analytics network, identified 25 data skills that make up the field of data science, trough a survey conducted to 490 data professionals from different companies. Their project ended up being an excellent way of providing a look into the field of data science.

These skills fall into five broad areas:

  • Business
  • Technology
  • Programming
  • Math & Modeling
  • Statistics

Many experts over the time, since Big Data became so important, have offered their insight on what it takes to be a successful data scientist. Different types of data professionals, depending on their role, are more proficient in any of the five different areas. Fore example, data experts in Business Management will be, logically, more proficient in business skills and so on.

With this in mind, while pondering over the structure of data science skills, it’s safe to say that it appears that a team approach is the most effective way of tackling data science projects, paired with applying the scientific method.

The scientific method plays a serious role in understanding any data, regardless of their size, speed or variety. Through the collection, analysis and interpretation of data, data scientists are applying the scientific method and extracting empirically based insights that enhance how humans and algorithms work.

Solving problems through a data-driven approach involves the following tasks:

  1. Identifying questions
  2. Accessing the right data
  3. Analyzing the data to provide the answers

If we pair up the five broad areas of data scientists’ mentioned at the beginning of the article with these three scientific method approaches it’s possible to understand how these skills are linked to the solution of specific problems.

According to Analytics Week’s survey the relationship between the five skills and three phases of data projects (using the scientific method) can be further explained with this example:

“Data professionals who describe themselves as “Business Management,” are the most proficient in business skills. Researchers are the most proficient in Math & Modeling and Statistics skills. Developers are the most proficient in Technology and Programming. The creative types have some proficiency in all skill areas.”

Getting feedback from data professionals themselves about their skills can actually help organizations manage their teams, and pinpoint and solve talent gaps.

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Data Science Skills