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:

 

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

July 30 – July 31, 2019

SPEAKERS

Interactive Talk Presenters

• 

  Tarry Singh

  Co-founder, CEO and AI Neuroscience Researcher

  deepkapha.ai

• 

  Hilary Mason

  General Manager of Machine Learning

  Cloudera

• 

  Natalie Evans Harris

  Co-Founder and Head of Strategic Initiatives

  BrightHive, Inc

• 

  Kunal Jain

  Founder & CEO

  Analytics Vidhya

• 

  Atif Kureishy

  VP – Global Emerging Practices | AI & Deep Learning

  Teradata

• 

  Jacqueline Nolis

  Principal Data Scientist

  Nolis, LLC

• 

  Safiya Noble, Ph.D.

  Associate Professor

  UCLA

• 

  Tom Schenk Jr.

  Director of Analytics

  KPMG

• 

  Gabriela de Queiroz

  Sr Developer Advocate/Sr. Engineering & Data Science Manager

  IBM

• 

  Adrian Cartier, Ph.D.

  Director of Data Science

  Bayer

• 

  Alberto Cairo

  Knight Chair in Visual Journalism

  University of Miami

• 

  Emily Robinson

  Data Scientist

  DataCamp

• 

  Kate Strachnyi

  Data Visualization Specialist

  Story by Data

• 

  Usama Fayyad, Ph.D.

  Chairman

  Open Insights

• 

  Peter Guerra

  North American Chief Data Scientist

  Accenture

• 

  Aubrey HB

  Director of Advanced Analytics

  Nationwide Building Society

• 

  Bryan Bumgardner

  Senior Data Scientist

  Digitas

• 

  Michelle Gill

  Senior Applied AI Researcher and Developer Relations, Healthcare

  NVIDIA

Workshop Instructors

• 

  Kerstin Frailey

  Senior Data Scientist and Head of Corporate Training Executive Programs

  Metis

• 

  Jonathan Balaban

  Senior Data Scientist, Bootcamp

  Metis

• 

  Ashley Purdy

  Career Advisor

  Metis

• 

  Sophie Searcy

  Director of Bootcamp Curriculum

  Metis

• 

  Damien Martin

  Data Science Instructor

  Metis

• 

  Kimberly Fessel

  Senior Data Scientist, Bootcamp

  Metis

SCHEDULE OVERVIEW

(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

    TARRY SINGH

  • 10:30am

    

    Qualities of an Exceptional Data Science Team

    KATE STRACHNYI

  • 11:00am

    

    Structured Thinking and Communications for Data Scientists

    KUNAL JAIN

  • 11:30am

    

    How Charts Lie — Getting Smarter About Data Visualization

    ALBERTO CAIRO

  • 12:00pm

    

    Deep Learning for All

    GABRIELA DE QUEIROZ

  • 12:30pm

    

    Joining the Data Science Community

    EMILY ROBINSON

  • 1:00pm

    

    Tech Won’t Save Us: Reimagining Digital Information for the Public

    SAFIYA NOBLE, PH.D.

  • 1:30pm

    

    You’re Not Paid to Model

    JACQUELINE NOLIS

Beginner & Intermediate Workshops

• BEGINNER

  2:30pm to 4:00pm

  Introduction to Python

  

  INSTRUCTOR:

  Kimberly Fessel

  BEGINNER

  4:00pm to 5:00pm

  ProTips: How to Get Hired as a Data Scientist

  

  INSTRUCTOR:

  Ashley Purdy

  INTERMEDIATE

  2:30pm to 5:00pm

  Introduction to Pandas

  

  INSTRUCTOR:

  Jonathan Balaban

  Conference website.