Full Stack Data Science, Washington DC

Leading a Full Stack
Data Science Laboratory

The term identifying the top level person responsible for the full exploitation of an organization's data resources is apparently settling on the Chief Data Officer (CDO). This individual, in effect, must function as a true scientist. This is not broadly understood and appreciated by those without physical science training.

Algorithms without Understanding

Curve fitting is not modeling. In its most general form, the process of modeling usually involves the formulation of an idealized data set that represents a real-world “something,” and a family of algorithms that allows you to “play” with that ideal data set in ways that conform to a larger, more general theory.

Falsifiable Questions

Physical scientists collect specific data in order to investigate specific questions. Business data are collected to answer one basic question: Who owes what to whom? The Chief Data Officer must learn how to repurpose these narrow-focus data sets. That means building new questions that will lead to novel and powerful insights. Such a CDO will be a powerful business asset.

— Consulting —
— Presentations — Books —

Leadership Services for Tech Research and
Major I.T. Initiatives

Digital Clones can supply pre-paid, retained services for major project design and launches. Retainers based on a 160 hour (four week) engagement. Send consulting enquiries to Consultants At DigitalClones Dot Biz.

Presentations on LO+FTTM in the R&D Setting

We would also be happy to supply speakers to your organization to present on the principles of LO+FTTM and how they work in research and development teams. Send speaking enquiries to Speakers At DigitalClones Dot Biz.

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Full Stack Data Science in Washington, DC

There are a number of Information Technology practice areas where the label “full stack” gets applied. The two most common ones these days are “full stack web developer” and “full stack data scientist.” But increasingly, such a person is called a unicorn. Why? Because they are mythical creatures that don't exist.

On the other hand, I can compose a team that can embody all of the skills of a given “full stack.” In the context of data science, what skills could this team bring to your problems? While there is no standard stack, here in the Washington DC region we often speak of the following capabilities as the basis:

  • Big Data, or perhaps more meaningfully, Data Engineering
  • Analytics and modeling
  • Deep learning, machine learning, and AI
  • Natural language processing
  • Dev/Ops
  • Visualization

The successful full stack data science team is a problem-solving factory. This group can empower digital transformation, generate high-impact innovations, and support extremely adaptive organizational cultures. Please click through the rest of the videos to learn more about Full Stack Data Science.


How it works — LO+FTTM, Problems, and Innovation

Optimizing Luck Cover

Even in pure research contexts
it's all about problem solving.


Problem solving always begins with
careful problem characterization.


Innovation is the art of turning
a great solution into a great application.

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