Saturday, April 17, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Data Science

Explaining Data Science to a Non-Data Scientist

June 5, 2020
in Data Science
Explaining Data Science to a Non-Data Scientist
590
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Summary:  Explaining data science to a non-data scientist isn’t as easy as it sounds.  You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail.  You might try this approach.

 

You might also like

DSC Weekly Digest 12 April 2021

6 Limitations of Desktop System That QuickBooks Hosting Helps Overcome

Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft

We’ve all been there.  You’re at a party or maybe striking up a conversation with that pretty girl at the bar and sooner or later the question comes up, “what do you do?”  Since you have what is reported to be the sexiest job in the world you proudly respond “I’m a data scientist”.

OK, what happens next depends on exactly what you say.  Do your fellow party goers hang on your every word in anticipation?  Do you, as they say, get the pretty girl’s digits?  You respond:

“I’m working with deep neural nets with dozens of hidden layers on cloud based TPUs using Tensorflow.  Right now I’m working to put bounding boxes around images of people so I can create multi-class deep learning models to predict their…”

Never mind.  Your host’s eyes have glazed over.  The cute girl has turned to the guy on her other side who looks like a personal trainer at your gym.  TMI! TMI!  How do you keep it simple, brief, and still explain to a non-data scientist the essence of what you do without losing their interest in the first dozen words.  Next time you vow to keep it simple.

The next party comes.  You think, OK I’ll skip the specifics and just talk about the categories of tools that I use.  After the obligatory “I’m a data scientist” you continue:

“I use mathematical algorithms to answer questions in ranking, recommendation, classification, regression, clustering, and anomaly detection.  First we gather up massive data sets about the question we want to answer.  Getting that data and getting it ready for the algorithms is a whole different conversation.  But the fun part begins when I start creating models and testing them with different optimization methods like stochastic gradient descent to see which one is most accurate.  Then I score the unseen data…”

Never mind.  Same result.

After several years of trying, I’ve settled on a very simple explanation based mostly on Brandon Roher’s remarkable 2015 five-question explanation of machine learning.  Even with the additional complexity of Big Data and deep learning this is the explanation I’ve found most successful.  It basically has three parts following “I’m a data scientist”.

 

Part 1 You’re a Wizard

I help people answer question or make predictions about what will happen in the future.  So data scientists are kind of like fortune tellers except that we do it with math and data.  And most important, unlike fortune tellers we can get the right answer pretty often.

Keep in mind that 50% accuracy is the same as a coin toss, so generally we’re pretty happy when we get the answer right about 70% of the time and sometimes we can get it right upwards of 90% of the time.

(Ok I’m taking some liberties here but remember the audience).

 

Part 2 What You Work On is Easy to Understand – Sort of

There are really only five types of questions that all data scientists deal with.

  1. Is this A or B?
  2. Is this weird?
  3. How much – or – How many?
  4. How is this organized?
  5. What should I do next?

Now, if they’re still with you, you can move on to Part 3 for some examples – but keep it short.

 

Part 3 Some Examples – Keep it Short

  1. Is this A or B?

These questions are like predicting who will buy and who won’t.  Or with machines we might try to predict is that machine going to break down in the next week.

  1. Is this weird?

We help your bank and credit card company a lot with this type.  Is the transaction that just showed up on your credit card unusual for you so that maybe we should make sure it was really you.  This is also where the world of cybersecurity comes in.  We can look at individual incoming signals from outside your system and flag the ones that look suspicious.

  1. How much – or – How many?

These questions are about numbers in the future.  What will the price of oil be next month?  What will be my sales in each of the next 12 months?

  1. How is this organized?

Turns out that a lot of data, particularly about people naturally breaks into groups but those groups aren’t necessarily easy to see without some math.  So if we’re going to recommend what movie to see, what music you might like, or even who you should consider dating we’d answer them here.

  1. What should I do next?

Some of these questions have only a few logical answers.  Like, given two factors, like potential sales and the cost of the sale what’s the optimum combination of the two that maximizes profit.  The other types of questions here are even more interesting since they’re how we program self-driving cars where the question might be, the light just turned yellow, should I brake or accelerate through.

 

Part 4

Well there really isn’t any perfectly designed Part 4.  If you’ve been a great story teller then maybe your audience is ready to ask you some questions.  Maybe it’s time to just listen and make room for the next speaker.

You’ve devoted thousands of hours to perfecting your skills.  You’re proud of your knowledge and can speak at length about math, tools, data, computer architecture, deep learning, IoT, and even AGI.  What I’ve found is that what most non-data scientist want is your elevator pitch.  So keep it simple, keep it brief, and maybe try this approach to still get across most of the magic in what you do.

 

 

Other articles by Bill Vorhies

 

About the author:  Bill is Contributing Editor for Data Science Central.  Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.  His articles have been read more than 2.1 million times.

[email protected] or [email protected]


Credit: Data Science Central By: William Vorhies

Previous Post

Dow Stumbles as Wall Street Debates the Case for Negative Rates

Next Post

Google: Chinese and Iranian hackers targeted Biden and Trump campaign staffers

Related Posts

DSC Weekly Digest 01 March 2021
Data Science

DSC Weekly Digest 12 April 2021

April 14, 2021
6 Limitations of Desktop System That QuickBooks Hosting Helps Overcome
Data Science

6 Limitations of Desktop System That QuickBooks Hosting Helps Overcome

April 13, 2021
Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft
Data Science

Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft

April 13, 2021
Trends in custom software development in 2021
Data Science

Trends in custom software development in 2021

April 13, 2021
Epoch and Map of the Energy Transition through the Consensus Validator
Data Science

Epoch and Map of the Energy Transition through the Consensus Validator

April 13, 2021
Next Post
Google: Chinese and Iranian hackers targeted Biden and Trump campaign staffers

Google: Chinese and Iranian hackers targeted Biden and Trump campaign staffers

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Monitor Your SEO Placement with SEObase
Learn to Code

Monitor Your SEO Placement with SEObase

April 17, 2021
Google Project Zero testing 30-day grace period on bug details to boost user patching
Internet Security

Google Project Zero testing 30-day grace period on bug details to boost user patching

April 17, 2021
Teslafan, a Blockchain-Powered Machine Learning Technology Project, Receives Investment Prior to the ICO
Machine Learning

Teslafan, a Blockchain-Powered Machine Learning Technology Project, Receives Investment Prior to the ICO

April 17, 2021
The “Blue Brain” Project-A mission to build a simulated Brain | by The A.I. Thing | Mar, 2021
Neural Networks

The “Blue Brain” Project-A mission to build a simulated Brain | by The A.I. Thing | Mar, 2021

April 17, 2021
A new collective to fight adtech fraud: Friday’s daily brief
Digital Marketing

A new collective to fight adtech fraud: Friday’s daily brief

April 17, 2021
Cyberattack on UK university knocks out online learning, Teams and Zoom
Internet Security

Cyberattack on UK university knocks out online learning, Teams and Zoom

April 17, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Monitor Your SEO Placement with SEObase April 17, 2021
  • Google Project Zero testing 30-day grace period on bug details to boost user patching April 17, 2021
  • Teslafan, a Blockchain-Powered Machine Learning Technology Project, Receives Investment Prior to the ICO April 17, 2021
  • The “Blue Brain” Project-A mission to build a simulated Brain | by The A.I. Thing | Mar, 2021 April 17, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates