Building Data Science

A 3-part Series Dedicated to Building the Predictive Organization

I T I N F O R M A T I O N B R I G H T S E M A N T U I B O S T O N I A R . S E R I E S I L E U X P H I I L A D E L P H I A T Y P O G R A P H Y C L I E N T W O D A T A E G I O N C O M M U N I C A T E A R T M C O D E R N R A T H C T O R I D E A G A L L E R Y S P P S C I E N T I S T C R E A T E D A T A A C O N C W P T S I N F O S C I U Y P B R R Z W S Q P L K G B X N G R O U P T Y S C I E N T I S T S K S P U R S U H I T I N F O R M A T I O N B R I G H T S E M A N T O I C I S E M I N A R . S E R I E S I L E U X P H I M R J U L Y A U G U S T S E Y Q M R I X O 2 E N B T W

April 2014 | Boston, MA

Purpose  

Building data science is hard work. Leaders must understand where to start; how to vision; how to get buy-in; how to organize; how to operationalize; how to drive down the mean time to insight; and last but not least how to enable the wider organization to not only benefit from it, but teach aspiring analysts how to do predictive analytics themselves.

In this series practicing data science leaders will share their experiences wrt how they built sophisticated data science capabilities. The format of each seminar will include a kick-off presentation by a leader recommending a general framework per the material in each seminar, a break, then panel of leaders, of whom each will share anecdotal experiences. All seminars will be held on Tuesday evenings, 6:30-8p unless otherwise noted.

Agenda

Tuesday, April 8th

Conceiving the Data Science Vision Deciding to evolve your existing data strategy away from what happened yesterday to what will happen tomorrow requires a well-crafted strategy detailing exactly how data science benefits the organization not only now but also well into the future. In this series we’ll tackle how to

• Create a case for data science and its benefits to the organization

• Define the appropriate skillsets and implementing the right

leadership role for data science

• Create strategies for scoping and converging on reasonable

expectations of the team both near- and long-term

• Messaging the benefits of data science to prospective beneficiaries

Speakers

Keynote Panel

David Dietrich,

Data Science

Curriculum Architect

Costas Boussios,

VP Data Science

Angela Bassa,

Director of

Analytics

Ed Cuoco,

Director of Data

Science

Tuesday, April 15th

Operationalizing Data Science

Once the business decides to invest in data science, leaders soon realize that efficiently delivering insights is a complicated and confusing process. Making the right decision about the team to hire, tools to buy or build and the processes to create to efficiently manage the analytics lifecycle can be overwhelming. In this series we’ll suggest ways to • Determining the right skillsets needed using a skills vs responsibilities

matrix

• Suggestions on luring the best talent in an intensely competitive

market

• Build an effective distributed prediction platform

• Effectively managing the analytics lifecycle through empirical, on the

job, experience

Speakers

Keynote Panel

Chris McCubbin,

Dir. of Data Scientist

TBD,

TBD

Tuesday, April 22nd

Proliferating Data Science Throughout the Organization

Today, advanced analytics is typically the domain of the data scientist. As new tools emerge that simplify the process of managing the analytics lifecycle, more and more of the organization will be empowered to do analytics. At the same time, the business will expect that the mean time to insight goes down over time. First the business user will be enabled to do prediction on the fly, then over time--provided that the right tools emerge—analytics will be crowd sourced by everyone in the organization. In this series we will describe how • Analytics will be diffused throughout the organization

• Likely tools which will enable data scientists to tamp down the mean

time to insight

• Hindrances to business and non-analysts users to perform prediction

• How building data science teams will evolve over time

Speakers

Keynote Panel

Available Available Available

Amit Phansalkar,

Chief Data Scientist