Category: Analytics

The Philosophy and Process of Data Science
Insight Paper 11.3.2016

The Philosophy and Process of Data Science

Let’s start by defining data science and being clear that the most important word in the term “data science” is science; data science usually involves using the scientific method to prove or disprove a hypothesis with data. My colleague Mike Schillmoeller points out that some methods (e.g., dimension reduction, image<span. . .
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Pragmatic Analytics to Help Gain a Decision Advantage
Insight Paper 10.26.2016

Pragmatic Analytics to Help Gain a Decision Advantage

Information of all kinds is flying at us at a tremendous and increasing rate and volume, but many companies are fruitlessly trying to extract value and meaning from that information. Ever since data science has become an integral part of our daily lives, our news feeds, inboxes and water cooler<span. . .
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2016 FARCON
Event Summary 9.1.2016

2016 FARCON

The 2016 FARCON (Financial and Retail Conference on Analytics) took place August 24, 2016. FARCON is Minnesota’s premier event for Data Science in Financial Services and Retail, run by MinneAnalytics, a nonprofit organization dedicated to serving Minnesota’s data science and analytics community.
Tagged in: Analytics, Financial Services
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Marketing Medical Devices In An Age Of Value -Based Care
Case Study 8.30.2016

Marketing Medical Devices in an Age of Value-Based Care

Our Client, a rapidly growing medical device company, sought a partner to help them better understand fluid management for their hemodynamic monitoring product line. They were looking to generate abstracts in partnership with several physician groups who were thought leaders in the area of fluid management, and their goal was<span. . .
Tagged in: Analytics, Growth & Acceleration, Healthcare & Life Sciences, Life Sciences
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Complexity in Big Data
Insight Paper 6.30.2016

Dealing With Complexity in Big Data

Big Data is esoteric. Maybe too esoteric. In concrete terms, Big Data refers to large data sets that are often analyzed by powerful computers to reveal patterns. Not too far off on the analytical spectrum from Big Data is machine learning—the ability of computers to learn without explicit guidance. Like<span. . .
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