The Real Life of a Data Scientist

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Transformation: Manipulating the acquired data for analysis, diagnosing data quality and understanding what assumptions can be made.

The most time-consuming component of the analysis process, transformation, involves reformatting, validating data to make it palatable for databases and visualization tools, diagnosing the data for quality issues and trying to understand what assumptions they can make about it. In the transformation phase, data scientists encounter numerous challenges, including data sets that may contain missing, erroneous or extreme values. As a result, the assumptions that data scientists make about such data sets turn out to be wrong and misled.

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In early 2012, a group of Stanford University researchers interviewed 35 data analysts from 25 organizations across a variety of sectors, including health care, retail, marketing and finance, and identified the various challenges data scientists face in the data analysis process.

Despite being in high demand and hailed as one of the hottest professions of the 21st century, much of the work of a data scientist is actually dominated by the incredibly time-consuming process of changing data into a usable form. The data analysis process involves four tasks - discovery, transformation, modeling and reporting – with data scientists spending as much as 60 to 80 percent of their time in the data transformation stage.

In this slideshow, Trifacta, a provider of productivity platforms for data analysis, takes you through each of these tasks in greater detail, highlighting the pain points data scientists face at each stage. It’s clear tools are needed that can simplify the data analysis process while at the same time increasing productivity and collaboration among data scientists.   


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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