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Michael Moss, Ph.D.
18 followers -
Information Research Scientist | Data Scientist | Writer
Information Research Scientist | Data Scientist | Writer

18 followers
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Outliers are a bane to an analyst’s attempts to detect patterns within data. Using a linear regression model as an example, Selva Prabhakaran provides a process for identifying and treating outliers.

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Every company has a story to tell. Joshua Reynolds presents a model as to how companies can use explanatory analytics to engage their customers, influence their behavior, and in so doing, improve their organization’s competitiveness. 

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Instead of focusing on their big data strategy, Gabriel Lowy suggests that companies should concentrate their efforts on data governance and management. He states that the establishment of a formal data governance program combined with efficient data management increases an organization’s return on data assets metric, resulting in improved competitiveness within the marketplace.

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If you are looking for communities in LinkedIn related to data science, check out this list published by Salford Systems.

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Those looking to integrate personalization technologies into their marketing efforts may want to explore deep learning, a subset of machine learning. Huba Gaspar explores three areas where deep learning may enhance an organization’s current recommendation system.

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Ed Jones provides a nice overview regarding the role, required skills, and tools of data scientist. I like the figure depicting various areas of focus within the data science discipline. I also like his simple, but effective overlay showing the interaction of skillsets (storyteller, business analyst, and data engineer) that combine to make a data scientist.

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Asim Jalis presents a nice overview of the seven leading Big Data technologies with which he believes all data engineers should be familiar. Additionally, he discusses the role and use of each technology.

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According to Larisa Bedgood, retailers should know not only the names and email addresses of their customers, but also their individual preferences and interests. She cites that 80% of retailer marketers fail to personalize their marketing efforts, resulting in a sub-optimal customer experience. In her article, Larisa offers an introduction regarding what types of data retailers should collect and how they should use this information to provide their customers with a more personalized and memorable experience.
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