Licensed to Analyze? Who Can Claim to be a Data Scientist? #DemystifyDS 2019

Conference Talk Recording:

Licensed to Analyze? Who Can Claim to be a Data Scientist?

Usama Fayyad Ph.D., Chairman, Open Insights

Watch on Youtube Metis Data Science Training channel:

Licensed to Analyze? Who Can Claim to be a Data Scientist? | Usama Fayyad Ph.D.#DemystifyDS 2019

Watch Link 2.

Download the video: link.

Answered questions at broadcast:

Question:            How do you draw the line between Data Analyst or Data Scientist? For example, my title is Data Analyst but I built our Data Warehouse pipeline and build models and insights. Does that put me more in the Data Scientist or Data Analyst category?

Answer:               Exactly. I mean so great question, number one. Number two, this is basically the core of the dilemma, right? So where is it that you know, I draw my own line. I have my box, right? So a data scientist, I expect them to have a good degree of programming. I expect them to know statistics. I expect them to meet certain bars that I have, but who says that my bars should be the right bars? And that’s what we’re trying to get to is what are the right bars? What do companies need and how do we call these people with the right titles?

So for example, because data scientist is such a highly paid and highly coveted position, lots of people are claiming to be data scientists, probably competing with Fiona. Most of them probably aren’t qualified and they wouldn’t even know a data warehouse if it hit them in the face. They wouldn’t understand how to like query it. They don’t how to do storytelling with analytics.

So this is why we need to kind of solve the problem, but excellent question because it really hits on the topic.

Question:            Yeah, yeah. So I’m going to throw another wrench in your discussion, just for the sake of sort of strong manning things. There are many fields where sort of expertise in the specific topic is as important or more important than things that are sort of standard like statistics. And I will give you an example from my own background.

                                I’m trained as a biochemist, so as a traditional bench lab scientist and the data science and machine learning jobs that I’ve had, I could not have done those jobs without that training. So it’s actually probably more important.

                                So how do you propose that we would evaluate people where domain expertise is paramount?

Answer:               Yeah. Look, I mean domain expertise is a must in almost any real tasks that you do kind of outside research or even in some cases in research, if you’re doing research on a certain field.

But here’s what I would say. The core skills for doing data science are applicable in many areas and many domains. The way I try to think of this, is this is very much like engineering. If you understand the principles of design, principles of problem solving, the principles of how to represent the problem abstractly, you can apply it to many domains and many areas from manufacturing to transportation to construction to whatever.

Now, the trick here is (a) how do you work with the right experts because you can never get, I mean of course the best combination is an expert like you in a certain area who actually picks up the data science skills. That is very rare and very difficult.

It’s much easier to find somebody who really, really knows how to do the data science that are machine learning expert or they know their statistics inside out. They can program, they can dive into data and grab stuff who are working very, very closely with deep domain experts.

And this was kind of my lucky, at least exposure when I first graduated with my PhD in AI and machine learning from the University of Michigan. My first job was with NASA jet propulsion lab, which is a Caltech lab.

So I ended up hooking up with many scientists, real scientists in astronomy, planetary geology, atmospherics, many of these areas who really, really knew the domain inside out. But I could bring a new perspective and we could solve problems that the scientists struggled with for 30, 40, 50 years without being able to solve them because they didn’t know what’s possible with machine learning and algorithmic approaches to analysis.

So I think it’s the fusion of both. How do you, you got to find ways to know how to talk to domain experts and you got to know how to collaborate with them and figure out how to make your tools and your knowledge useful.


About the Conference:


Demystifying Data Science Conference

FREE Live Online Conference for Aspiring Data Scientists, Data-Focused Business Leaders and Practitioners

July 30 – July 31, 2019


Interactive Talk Presenters

  • Tarry Singh headshot
    Tarry Singh

    Co-founder, CEO and AI Neuroscience Researcher

  • Hilary Mason headshot
    Hilary Mason

    General Manager of Machine Learning


  • Natalie Evans Harris headshot
    Natalie Evans Harris

    Co-Founder and Head of Strategic Initiatives

    BrightHive, Inc

  • Kunal Jain headshot
    Kunal Jain

    Founder & CEO

    Analytics Vidhya

  • Atif Kureishy headshot
    Atif Kureishy

    VP – Global Emerging Practices | AI & Deep Learning


  • Jacqueline Nolis headshot
    Jacqueline Nolis

    Principal Data Scientist

    Nolis, LLC

  • Safiya Noble, Ph.D. headshot
    Safiya Noble, Ph.D.

    Associate Professor


  • Tom Schenk Jr. headshot
    Tom Schenk Jr.

    Director of Analytics


  • Gabriela de Queiroz headshot
    Gabriela de Queiroz

    Sr Developer Advocate/Sr. Engineering & Data Science Manager


  • Adrian Cartier, Ph.D. headshot
    Adrian Cartier, Ph.D.

    Director of Data Science


  • Alberto Cairo headshot
    Alberto Cairo

    Knight Chair in Visual Journalism

    University of Miami

  • Emily Robinson headshot
    Emily Robinson

    Data Scientist


  • Kate Strachnyi headshot
    Kate Strachnyi

    Data Visualization Specialist

    Story by Data

  • Usama Fayyad, Ph.D. headshot
    Usama Fayyad, Ph.D.


    Open Insights

  • Peter Guerra headshot
    Peter Guerra

    North American Chief Data Scientist


  • Aubrey HB headshot
    Aubrey HB

    Director of Advanced Analytics

    Nationwide Building Society

  • Bryan Bumgardner headshot
    Bryan Bumgardner

    Senior Data Scientist


  • Michelle Gill headshot
    Michelle Gill

    Senior Applied AI Researcher and Developer Relations, Healthcare


Workshop Instructors

  • Kerstin Frailey headshot
    Kerstin Frailey

    Senior Data Scientist and Head of Corporate Training Executive Programs


  • Jonathan Balaban headshot
    Jonathan Balaban

    Senior Data Scientist, Bootcamp


  • Ashley Purdy headshot
    Ashley Purdy

    Career Advisor


  • Sophie Searcy headshot
    Sophie Searcy

    Director of Bootcamp Curriculum


  • Damien Martin headshot
    Damien Martin

    Data Science Instructor


  • Kimberly Fessel headshot
    Kimberly Fessel

    Senior Data Scientist, Bootcamp



(All times in EST)

Day 1: July 30

For Aspiring

Data Scientists

Day 2: July 31

For Business Leaders,

Managers & Practitioners

30-Minute Talks

    • 10:00am
      From Aspiring to Full-fledged Data Science Professional
    • 10:30am
      Qualities of an Exceptional Data Science Team
    • 11:00am
      Structured Thinking and Communications for Data Scientists
    • 11:30am
      How Charts Lie — Getting Smarter About Data Visualization
    • 12:00pm
      Deep Learning for All
    • 12:30pm
      Joining the Data Science Community
    • 1:00pm
      Tech Won’t Save Us: Reimagining Digital Information for the Public
    • 1:30pm
      You’re Not Paid to Model

Beginner & Intermediate Workshops

    2:30pm to 4:00pm
    Introduction to Python
    Kimberly Fessel
    4:00pm to 5:00pm
    ProTips: How to Get Hired as a Data Scientist
    Ashley Purdy
    2:30pm to 5:00pm
    Introduction to Pandas
    Jonathan Balaban

    Conference website.

Leave a Reply