Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Data science for managers (Day 2)

Angie Ma (Faculty)
Location: 212 D

Angie Ma offers a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn’t) possible with these exciting new tools and how they can benefit your organization. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.

Outline

Introduction to data science

  • Course objectives: AI for everyone
  • Definitions: Data science, machine learning, big data, data analytics, customer science, etc.
  • Historical context: How data science started, integration with computing, etc.
  • Present-day usage
  • Data science within the corporate context
  • Significance and urgency: Why now, and why you?

Data science in industry today: Practical applications and benchmark leaders

  • A selection of practical applications
  • Strategic approaches: Commissioning versus procurement (build or buy?)
  • Data science maturity models and the enabling preconditions within your organization to
    use data science well (data, skills, tools, etc.)

Data in your organization: What are your raw materials?

  • What data assets do you have, where does it live, who owns those systems, where are the
    gaps, and how could you fill them by (e.g. buying data or installing sensors)?
  • What skills do you have access to from your part of the business, and what gaps might there be?
  • What tools do you use, where are the gaps, what other options are there?

Project selection: What makes a good data science project?

  • Overview of the four key criteria for successful project selection and how to assess them
  • Group exercise: Create a project longlist (often categorized broadly within marketing,
    operational efficiency, operational effectiveness, and commercial optimization)
  • Scoring longlist against key criteria and plenary discussion

Managing a data science project: How is it is different from other projects?

  • Similarities and differences
  • Project lifecycle and timescales
  • Project delivery methodology
  • Typical staffing profiles
  • Data scientist culture
  • Cost estimates for external suppliers (for data science as a service and for consulting)
  • Performance metrics
  • Operations and maintenance
  • Governance and risk management

Leading or supporting a data transformation

  • What is data transformation (as opposed to gradual reform), and when might it be
    necessary?
  • The ABCs of data transformation
  • Common pitfalls facing successful data transformation initiatives
  • Different operational models for data science within a modern organization
  • Next steps
Photo of Angie Ma

Angie Ma

Faculty

Angie Ma is a cofounder and chief operating officer of Faculty, a London-based AI technology company that provides products and services in strategy, software, and skills. Faculty has delivered more than 300 commercial data science projects across 23 sectors and 8 countries. Angie is passionate about real-world applications of machine learning that generate business value for companies and organizations and has experience delivering complex projects from prototyping to implementation. She supports senior leaders to build AI capability, advising on skills transformation. A physicist by training, previously, Angie was a researcher in nanotechnology working on developing optical detection for medical diagnostics.