 | | Word of the Day |  | | Daily updates on the latest technology terms | October 26, 2019 | | | data scientist | |
A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician and computer science professional.
In business, data scientists typically work in teams to mine big data for information that can be used to predict customer behavior and identify new revenue opportunities. In many organizations, data scientists are also responsible for setting best practices for collecting data, using analysis tools and interpreting data. The demand for data science skills has grown significantly over the years as companies look to glean useful information from big data, the voluminous amounts of structured, unstructured and semi-structured data that a large enterprise or internet of things produces and collects. In job postings, necessary skills typically include the following: - Advanced degree, with a specialization in statistics, computer science, data science, economics, mathematics, operations research or another quantitative field.
- Expertise in all phases of data science, from initial discovery through cleaning, model selection, validation and deployment.
- Knowledge and understanding of common data warehouse structures,
- Experience with using statistical approaches to solve analytical problems.
- Proficiency in common machine learning frameworks.
- Experience with public cloud platforms and services.
- Familiarity with a wide variety of data sources, including databases, public or private APIs and standard data formats, like JSON, YAML and XML.
- Ability to identify new opportunities to apply machine learning to business processes to improve their efficiency and effectiveness.
- Ability to design and implement reporting dashboards that can track key business metrics and provide actionable insights.
- Experience with techniques for both qualitative and quantitative analysis.
- Ability to share qualitative and quantitative analysis in a way the audience will understand.
- Familiarity with machine learning techniques, such as K-nearest neighbors, Naive Bayes, random forests and support vector machines.
- Ability to design and implement validation tests.
- Experience in data visualization tools, such as Tableau and Power BI.
- Coding skills, such as R, Python or Scala.
- Ability to aggregate data from disparate sources.
- Ability to conduct ad hoc analysis and present results in a clear manner
| |  | "Traditional data scientists will still be needed to run very complex analysis of data, but for the most part, basic analysis will move to citizen data scientist roles due to increasingly easy-to-use tools." - Kathleen Walch
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