Data scientists emphasize rigor and performance when obtaining, scrubbing, exploring, modeling, and interpreting data. Data scientists provide a different context than data analysts to their work, through high-powered math. Josh Wills, a software engineer, once described a data scientist as a “person who is better at statistics than any software engineer and better at software engineering than any statistician.” They can take the work of a data analyst and give a more in-depth answer as to what that interpretation means, conceptualize a data model, and make some predictions for the future. They excel at:
- Converting raw data into machine-readable form
- Data interpretation
- Data visualization
- In-depth analysis using tools and algorithms that are appropriate for a problem
- Interpreting results
- Telling the data story that probability, statistics, and experimental design describes
Many data scientists come from masters and PhD programs, in a variety of subjects from astrophysics to zoology, and were also software engineers in their previous lives.
Other Definitions of Data Scientists Include:
- “People who mine and develop predictive, machine learning, and prescriptive models and analytics for these and deploy results for analysis by interested parties.” (DAMA-DMBOK2)
- Roles that are “critical for organizations looking to extract insight from information assets for ‘big data’ initiatives” and require “a broad combination of skills that may be fulfilled better as a team.” (Gartner)
- People who have “mastery of machine learning, statistics, and analytics.” (Harvard Business Review)
- “Big data wranglers” who “take an enormous mass of messy data points (structured and unstructured) and use their formidable skills in math, statistics, and programming to clean, manage, and organize them.” (Master’s in Data Science)
- “A new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.” (SAS)
Businesses use Data Scientists to:
- Leverage machine learning and AI technologies.
- Get a better handle on insights and results from self-service platforms and other technologies.
- Inform business decisions around efficiency, inventory, production errors, and customer loyalty.
- Help e-commerce companies improve customer service.
- Detect financial fraud and ensure compliance.
- Improve products, such as telecommunications and the Internet of Things.
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