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Analytical Experimentation in the Cloud


  • Webcast


  • Data Science
  • Cloud
  • Analytics

The value that the adoption of advanced analytics can unlock for an organization is well-documented; it is at the top of the agenda for most CIOs and, even CEOs. But unlike more traditional technical endeavors with fixed outcomes, identifying high-value analytics opportunities often begins with something more experimental.

The advent of cloud computing, availability of open-source libraries, and evolution of analytic tools have made it possible to apply statistical techniques in novel ways and to do so at a scale and speed that was once impossible or at least impractical for many organizations.

Through analytics experimentation, data and business understanding are combined with statistical techniques to create machine learning models capable of illuminating business opportunities that may not be visible using more basic approaches.

In this recorded webcast Thorogood consultants Scott Stieritz and Allan Spessoto guide you through the foundations of analytics experimentation and discuss a few of the modern tools available in different Cloud platforms.

Running Quick Data Science Experiments in the Cloud (27 mins)

What we cover:

  • Setting the context: how we arrived at this point, what analytics and machine learning can do for you, and how to identify use cases
  • Introductory statistical thinking – common inquiries and techniques
  • A walkthrough of technology offerings across different Cloud providers and how they can support experimentation
  • Overview of what comes after experimentation, i.e. Productionized Analytics

Is it for you?

  • Are you starting on a journey to implement statistical analysis on your data and want to know how to get started?
  • Do you have specific business scenarios where you believe advanced analytics can add value but need some guidance?
  • Does your organization use mostly ‘offline’ tools for data science and do you want to learn about how to start working with it in the Cloud?
  • Do you hear too much about large machine learning initiatives and want to learn about how to start on a smaller scale?