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From Theory to Data Product: Applying Data Science Methods to Effect Business Change.

Topic Overview

This tutorial is a primer on crafting well-conceived data science projects on course toward uncovering valuable business insights. Using case studies and hands-on skills development, we will teach techniques that are essential for a variety of audiences invested in effecting real business change: (1) academics looking to transition to roles applying the scientific method in a business environment, (2) business professionals looking to expand their analytical skillsets, and (3) business non-analysts working with data science teams.

We will start our discussion with case studies demonstrating advanced analytics entry points (the initial impetus for the project). Our case studies were chosen to demonstrate how a project’s entry point impacts its scope and approach, and how that can diverge from the critical business drivers that ultimately measure successful data science projects. We will also show you how to avoid missteps that can lead to less than stellar results or wasted effort, with a checklist to follow to get started on the right path from the beginning!

The next portion of the session will outline a framework to help you define, refine and assess value for business questions that are candidates for data science projects. Many organizations struggle with identifying and prioritizing these questions, but this step is critical to ensure your project teams are focused on the right work! Finally, we will demonstrate a pragmatic approach to frame your data driven decision making projects in an agile project methodology. An agile approach lets the project team quickly adapt, based on findings, as the project progresses. This framework helps to manage uncertainty while ensuring the project is focused on constant progress toward a stated goal.

Who Should Attend

Academics looking to transition to roles applying scientific methods in a business environment

Business professionals looking to expand their analytical skillsets

Business non-analysts working with data science teams


Danielle Leighton

Danielle helps clients approach, design, implement, and integrate new insights and advanced analytics data products that align with their business goals. She’s passionate about keeping data in context and applying research methods, best practices, and academic algorithms to industry business needs. With a strong background in machine learning, Danielle identifies the math, visualizations, and the business questions and processes necessary to create reliable predictive models and, ultimately, good, data driven business guidance. Danielle has worked in healthcare, academia, government, retail, gaming, and energy, and with quantified selfers, biohackers, hacklabs, and makerspaces. She is notoriously unreadable to GSR wearables. In her previous life, Danielle worked with the world’s most sophisticated wearable to date, the hearing aid. Currently, she focuses most of her time on data science in the energy sector.

Lindsay Brin

Lindsay is a motivated, curious, and analytical data scientist with more than a decade of experience with research methods and the scientific process. From generating testable hypotheses, through wrangling imperfect data, to finding insights via analytical models, she excels at asking incisive questions and using data to tell compelling stories.

Lindsay is passionate about teaching the skills necessary to analyze data more efficiently and effectively. Through this work, she has developed and taught workshops and online courses at the University of New Brunswick, and is a Data Carpentry instructor and Ladies Learning Code chapter co-lead. Having recently made a career pivot from biogeochemistry to data science, she is also well-positioned to provide insight into the applicability of academic research and analysis skills to business problems.

Janet Forbes

Janet Forbes is an experienced Enterprise, Business and Senior Systems Architect with deep understanding of data, functional and technical architecture and proven ability to define, audit and improve business processes based on best practices. She has extensive experience in leading multi-functional teams through the planning and delivery of complex solutions.

With over 25 years of experience in various roles and organizations, Janet has proven capability in enterprise, functional and technical architecture with specific focus on Business and Data Architecture. As a trusted advisor, Janet works closely with clients in assessing and shaping their data strategy practices.

Tutorial Outline

Advanced Analytics Entry Points:

  • What are Business Drivers
  • Case Studies – Energy, Health, Retail
  • Project entry points
  • What are they?
    • Recognizing your entry point
    • Common missteps
    • How to recognize and adapt
  • Checklist to set up for success
  • Small group exercise

Asking the Right Questions:

  • Common issues for data driven decision making projects
  • What is a valuable business question?
  • How do you define criteria specific to your business to identify valuable business questions?
  • Defining and refining questions
  • Evaluating and prioritizing
    • Prioritization Criteria
    • Scoring
  • Before and after examples
  • Small group exercise

Agile Approach to Data Driven Decision Making:

  • Managing Uncertainty
  • Types of Experiments
  • Agile Principles
  • An Agile Approach
    • Key Activities
    • The Makeup of a Sprint
    • Who Should Be Involved
  • Benefits
  • Small group exercise

Recap and Q&A to follow.

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