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  • UX & Social

    Wearables & Internet of things


    Quality Engineering


    Product Innovation

    Consumer Experience

    Cloud Computing & Infra

    Digital Content

    Big Data & High Performance

    After Going Live

    Enterprise Consumerization

    Mobile Product Dev / Native & Hybrid

    Cloud Computing/ Managed Services / Information Security

    E-Commerce / Travel / Front End Engineering

    Product Innovation / Innovation as a Service / Product Landing

    Design Thinking / User Experience Design / Visual Design / Service Design / Creative Workshops

    Test Automation / Mobile Testing / Game QA

    Hardware Design & Integration / Native Wearable / Wearable App Usability / Interface Design

    Digital Platforms / Game Development / Graphic Engineering

    Content Management / Digital Marketing / Video Content Production / E-Learning

    Software Archaeology / Software Maintenance

    Data Architecture / Data Science / Data Visualization

    Collaboration Solutions / Process Engineering Tools


  • Wisdom Nuggets from practicing Data Science in the “Real World”

  • Different people have different ideas about what Data Science actually is

    ● Common definition as key success factor

    ● Interdisciplinary approach

    ● Effective decision-making models & Production Environment

  • The intended use of the output may differ from a given technique’s purpose

    ● It all begins with a question

    ● Predicted Values, Decision Logic, Feature Relevance, Variable Impact

    ● Predictive vs. Explicative

  • Model complexity is a wolf in sheep’s clothing

    ● Simplest Model that gets the job done

    ● Degrees of freedom, risk of overfitting, chances of unintended consequences, less stability, false discovery

    ● Well Done usually looks “Simple”

  • Neverending discovery

    ● Highly Iterative, not chaotic

    ● Frameworks as communication tools (CRISP-DM)

    ● Refining the original question as much as actually finding an answer

    Image: Kenneth Jensen


  • It’s just Semantics ● Getting an actual piece of quality data may be the most difficult part

    ● Mocking data: integration and visibility tasks, like UI, dashboards, visualization, pipeline

    ● Simulation: insights about mechanics, not patterns

  • Big data volumes breeds a different kind of beast, even when it’s not “Big Data”

    ● False discovery rate upon many trials

    ● Statistical significance must relate to actual impact to be meaningful

    ● Cross validation schemes, regularization, minimizing degrees of freedom (parsimony), variable selection strategies and analysis of the magnitude and importance of variables

  • Outliers

    ● Outlier has a cause behind it, business logic, reason to measure, reason to use the model, and requirements of the objective

    ● Both extreme and middle dwelling may be “normal” or outlier

    ● Clean for the sake of clean is sterile data

  • Reproducible, or it didn’t happen

    ● Build the processes rather than the analysis

    ● Visibility, peer review, disconnections between a conclusion and expected outcome

    ● Inputs, parameters, scripts, environments, library versions, fixed random seeds

    ● Trust is lost but once

    image: http://blog.f1000research.com/2014/04/04/reproducibility-tweetchat-recap/

  • A data science “deliverable” is not necessarily completed features

    ● Completed features is an incomplete measure

    ● Committable strategies and steps to achieve a set objective

    ● Progress includes all the knowledge gained about the problem

  • The role of the Anthropovangelist

    ● Get to know the customer’s culture from within

    ● Get the client and the team to trust and empower the solving strategy

    ● From “so what” to “what if”

  • Keep in touch!

    Juan José López Murphy - [email protected]

    Tomás E. Tecce - [email protected]