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MiraiDensei: AnalyticsQ

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M iraiDensei Pvt. Ltd.   presents  AnalyticsQ , a platform for Google Analytics members that uses simple function wizards to get your reporting done without programming or complicated formulas. Refreshes with a simple click. https://analyticsq.miraidensei.com Free version can automate exports in various formats such as json, csv, google sheet, xml, xls/xlsx. Convert Excellent Analytics queries in place and gain access to the latest metrics and dimensions. Users can load from files or worksheets, combine, compare, remove duplicates, split columns, sort and filter quickly, easily, and without programming! All your actions are automatically recorded, easily edited, and quickly replayed. It is even easy to register and use! Not just these, it let you collaborate with your team and create charts with different customized theme-based reports such as annual, marketing etc. Create new report from different downloaded reports over the primary metric. Isn’t it productive and cost/time-effective?
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Geek&Chic: Analytics redefining fashion instincts

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In recent years,  advanced analytics  has become a core component across a wide range of consumer-facing industries to address increasing complexity. Digital companies—with Amazon leading the pack—are at the forefront, constantly finding new and innovative ways to apply analytics throughout the value chain, from merchandising to marketing to operations to human resources. The apparel industry,  comparatively, lags behind in embracing analytics, often favoring merchant-and designer-driven “gut feel” over insight-driven decision making. Apparel players point to several core challenges as constraints for investing in their analytics capabilities, including poor data quality, a rapidly changing assortment and competitive landscape, high SKU and logistics complexity, and limited analytics expertise within the current employee base.  To effectively start with  fashion retail analytics , players in the fashion retail sector need to first decide where analytics will help them achie
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The Evolving Role of Data Scientist'18

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“The world is one big data problem.”  - Andrew McAfee, Center for Digital Business, MIT/Sloan  “Without Big Data, you are blind and deaf in the middle of a freeway.”  - Geoffrey Moore Data Science – once a new and novel niche in the enterprise – has now emerged as a driving force in the enterprise: a key component (if not the key component) of BI, overall strategic planning, and knowledge gathering.  Let’s look at the timeline.  It started out and had its infancy in the domain of academia, especially the social sciences. Academic researchers developed skills and algorithms for detecting economic, housing, and medical trends in specified populations. Their work was generally funded by grants. Their focus was on soft, non-commercial questions regarding the quality of life and related issues, these questions addressed using traditional structured database models.  Following this, Data Science moved into corporate culture with the simple role of solving “point
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Data Science: what is this? Imminent or ephemeral? a new code?

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Computer Scientists seem to have settled on the following talking points:   (a) data science is concerned with really big data, which traditional computing resources could not accommodate    (b) data science trainees have the skills needed to cope with such big datasets. A successful data scientist needs to be able to become one with the data by exploring it and applying rigorous statistical analysis ... But good data scientists also understand what it takes to deploy production systems, and are ready to get their hands dirty by writing code that cleans up the data or performs core system functionality ... Gaining all these skills takes time [on the job].  Framework? However, a science doesn’t just spring into existence simply because a deluge of data will soon be filling telecom servers, and because some administrators think they can sense the resulting trends in hiring and government funding. Fortunately, there is a solid case for some entity called ‘Data
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