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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Those who work in the field of data science are known as data scientists. This iterative process is known as the data science lifecycle, which usually follows seven phases: Identifying an opportunity or problem Data mining (extracting relevant data from large datasets) Data cleaning (removing duplicates, correcting errors, etc.)

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Why Analytics Are Essential in Times of Crisis

Sisense

Predict: Lastly, look to forecast trends in supply and demand and track fast-moving changes in leading indicators. To foster the art of the possible, below are examples of how regular businesses use analytics to maximize customer revenue, reduce costs, forecast outcomes, and drive efficiency. Insights over instinct.

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How Skullcandy Uses Predictive and Sentiment Analysis to Understand Customers

Sisense

We fed Kraken (BigSquid’s predictive analytics engine) information about historical warranty costs, claims, forecasts, historical product attributes, and attributes of the new products on the roadmap. Then we ran Kraken’s machine learning and predictive modeling engine to get the results. Lessons Learned. Be patient!

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Solving the Data Daze – Analytics at the Speed of Business Questions

Rocket-Powered Data Science

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167
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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptive analytics for business forecasting and optimization, respectively. Broken models are definitely disruptive to analytics applications and business operations.