We’re All Data Scientists | Rebecca Nugent | TEDxCMU

so this is my mother the one on the

so this is my mother the one on the right so my mother graduated from the

right so my mother graduated from the

right so my mother graduated from the University of Texas with a degree in

University of Texas with a degree in

University of Texas with a degree in English she then received her master’s

English she then received her master’s

English she then received her master’s in Library and Information science and

in Library and Information science and

in Library and Information science and was a librarian for several years now

was a librarian for several years now

was a librarian for several years now after taking a break to raise small

after taking a break to raise small

after taking a break to raise small children she returned to the classroom

children she returned to the classroom

children she returned to the classroom and was a middle school teacher in

and was a middle school teacher in

and was a middle school teacher in English literature language arts reading

English literature language arts reading

English literature language arts reading in Latin for over 20 years she was a

in Latin for over 20 years she was a

in Latin for over 20 years she was a longtime coach of championship future

longtime coach of championship future

longtime coach of championship future problem solving teams an award-winning

problem solving teams an award-winning

problem solving teams an award-winning educator to hundreds of students and can

educator to hundreds of students and can

educator to hundreds of students and can spot a grammatical error on a street

spot a grammatical error on a street

spot a grammatical error on a street sign from about 300 yards she’s an

sign from about 300 yards she’s an

sign from about 300 yards she’s an extremely smart woman she’s also

extremely smart woman she’s also

extremely smart woman she’s also absolutely convinced that she has no

absolutely convinced that she has no

absolutely convinced that she has no idea what I do for a living so the word

idea what I do for a living so the word

idea what I do for a living so the word illiterate or literacy can be used

illiterate or literacy can be used

illiterate or literacy can be used generally but it’s most commonly

generally but it’s most commonly

generally but it’s most commonly associated with the inability to read or

associated with the inability to read or

associated with the inability to read or write at an appropriate level there are

write at an appropriate level there are

write at an appropriate level there are millions of people who struggle with

millions of people who struggle with

millions of people who struggle with illiteracy there are programs all over

illiteracy there are programs all over

illiteracy there are programs all over the country that offer assistance in

the country that offer assistance in

the country that offer assistance in training to have difficulties with

training to have difficulties with

training to have difficulties with illiteracy as a source of discomfort for

illiteracy as a source of discomfort for

illiteracy as a source of discomfort for first many a source of shame it’s not

first many a source of shame it’s not

first many a source of shame it’s not information that people volunteer in

information that people volunteer in

information that people volunteer in numeracy on the other hand is a

numeracy on the other hand is a

numeracy on the other hand is a different story and today I’m applying a

different story and today I’m applying a

different story and today I’m applying a numeracy more broadly to difficulties

numeracy more broadly to difficulties

numeracy more broadly to difficulties with mathematics or statistics or any of

with mathematics or statistics or any of

with mathematics or statistics or any of these related topics so that’s not a

these related topics so that’s not a

these related topics so that’s not a word that is commonly used but it’s more

word that is commonly used but it’s more

word that is commonly used but it’s more readily self-identified so we hear oh I

readily self-identified so we hear oh I

readily self-identified so we hear oh I am NOT a math person so Paul earnest at

am NOT a math person so Paul earnest at

am NOT a math person so Paul earnest at a from Exeter University this is a

a from Exeter University this is a

a from Exeter University this is a philosopher in mathematics who focuses

philosopher in mathematics who focuses

philosopher in mathematics who focuses on social constructivism which is the

on social constructivism which is the

on social constructivism which is the theory of how human development and

theory of how human development and

theory of how human development and knowledge acquisition come from social

knowledge acquisition come from social

knowledge acquisition come from social interaction with others and he has the

interaction with others and he has the

interaction with others and he has the following quote a widespread public

following quote a widespread public

following quote a widespread public image of mathematics is that it is

image of mathematics is that it is

image of mathematics is that it is difficult

difficult

difficult cold abstract theoretical ultra rational

cold abstract theoretical ultra rational

cold abstract theoretical ultra rational but important and largely masculine it

but important and largely masculine it

but important and largely masculine it also has the image of being remote and

also has the image of being remote and

also has the image of being remote and inaccessible to all but a few super

inaccessible to all but a few super

inaccessible to all but a few super intelligent beings with mathematical

intelligent beings with mathematical

intelligent beings with mathematical minds in contrast to the shame

minds in contrast to the shame

minds in contrast to the shame associated with illiteracy numeracy is

associated with illiteracy numeracy is

associated with illiteracy numeracy is almost a matter of pride among

almost a matter of pride among

almost a matter of pride among educated persons in western Anglophone

educated persons in western Anglophone

educated persons in western Anglophone countries and then from my personal

countries and then from my personal

countries and then from my personal experience it seems like every person I

experience it seems like every person I

experience it seems like every person I sit next to on an airplane has a story

sit next to on an airplane has a story

sit next to on an airplane has a story to tell about how terrible they are at

to tell about how terrible they are at

to tell about how terrible they are at statistics and how they cheated on all

statistics and how they cheated on all

statistics and how they cheated on all of their exams in college so to be

of their exams in college so to be

of their exams in college so to be illiterate as a problem and to be

illiterate as a problem and to be

illiterate as a problem and to be enumerate is is what is cool

enumerate is is what is cool

enumerate is is what is cool well is numeracy actually a problem yes

well is numeracy actually a problem yes

well is numeracy actually a problem yes the answer is yes every few years the

the answer is yes every few years the

the answer is yes every few years the results of the latest national adult

results of the latest national adult

results of the latest national adult illiteracy and numeracy survey get

illiteracy and numeracy survey get

illiteracy and numeracy survey get released and there’s a frenzy of

released and there’s a frenzy of

released and there’s a frenzy of articles trumpeting and sounding the

articles trumpeting and sounding the

articles trumpeting and sounding the call to devote more substantial

call to devote more substantial

call to devote more substantial resources to a numeracy which includes

resources to a numeracy which includes

resources to a numeracy which includes educating people about how terrible this

educating people about how terrible this

educating people about how terrible this is and the importance of this issue yeah

is and the importance of this issue yeah

is and the importance of this issue yeah this is not so much helping the cause

this is not so much helping the cause

this is not so much helping the cause yeah so can you imagine Mattel creating

yeah so can you imagine Mattel creating

yeah so can you imagine Mattel creating a Barbie that giggled as she said I

a Barbie that giggled as she said I

a Barbie that giggled as she said I can’t read right that’s just not going

can’t read right that’s just not going

can’t read right that’s just not going to happen yeah but this is somehow okay

to happen yeah but this is somehow okay

to happen yeah but this is somehow okay all right oddly this is not really

all right oddly this is not really

all right oddly this is not really helping our cause either in a different

helping our cause either in a different

helping our cause either in a different way so the characters from the popular

way so the characters from the popular

way so the characters from the popular TV show The Big Bang Theory are gifted

TV show The Big Bang Theory are gifted

TV show The Big Bang Theory are gifted in mathematics and physics and they’re

in mathematics and physics and they’re

in mathematics and physics and they’re portrayed as kind of these loveable

portrayed as kind of these loveable

portrayed as kind of these loveable quirky nerds and we laugh at their

quirky nerds and we laugh at their

quirky nerds and we laugh at their interactions and how they solve problems

interactions and how they solve problems

interactions and how they solve problems but for many people in the country this

but for many people in the country this

but for many people in the country this is what a mathematician looks like and

is what a mathematician looks like and

is what a mathematician looks like and so some of us are laughing with them but

so some of us are laughing with them but

so some of us are laughing with them but many people are laughing at them so this

many people are laughing at them so this

many people are laughing at them so this this is not helping either our other

this is not helping either our other

this is not helping either our other option is this we see the computer

option is this we see the computer

option is this we see the computer genius creepily surrounded by computers

genius creepily surrounded by computers

genius creepily surrounded by computers staring at all of the data searching

staring at all of the data searching

staring at all of the data searching searching possibly stalking searching

searching possibly stalking searching

searching possibly stalking searching for information until they find that one

for information until they find that one

for information until they find that one significant piece and that’s the

significant piece and that’s the

significant piece and that’s the character that often saves the day but

character that often saves the day but

character that often saves the day but that is not a man you would take home to

that is not a man you would take home to

that is not a man you would take home to dinner yes

so what I’m going to push back on a

so what I’m going to push back on a little bit today and claim is that what

little bit today and claim is that what

little bit today and claim is that what our issue is really about the self

our issue is really about the self

our issue is really about the self perception of a numeracy so how do you

perception of a numeracy so how do you

perception of a numeracy so how do you feel about your ability to do math and

feel about your ability to do math and

feel about your ability to do math and statistics because research has shown

statistics because research has shown

statistics because research has shown that there is no type of math person and

that there is no type of math person and

that there is no type of math person and that when we self assess our skills it’s

that when we self assess our skills it’s

that when we self assess our skills it’s often done in relative comparison to

often done in relative comparison to

often done in relative comparison to other skills so for example if I’m

other skills so for example if I’m

other skills so for example if I’m strong in rhetoric or language

strong in rhetoric or language

strong in rhetoric or language acquisition I automatically downgrade my

acquisition I automatically downgrade my

acquisition I automatically downgrade my skills and math regardless of what they

skills and math regardless of what they

skills and math regardless of what they are in vice versa if I’m good at

are in vice versa if I’m good at

are in vice versa if I’m good at mathematics and statistics then I might

mathematics and statistics then I might

mathematics and statistics then I might assess my writing skills as being less

assess my writing skills as being less

assess my writing skills as being less than they really are

than they really are

than they really are so who are you are you tEEN TOP Barbie

so who are you are you tEEN TOP Barbie

so who are you are you tEEN TOP Barbie are you a lovable physics nerd or are

are you a lovable physics nerd or are

are you a lovable physics nerd or are you this guy yeah and that’s what

you this guy yeah and that’s what

you this guy yeah and that’s what everybody is always self-assessing who

everybody is always self-assessing who

everybody is always self-assessing who they are and in actuality you’re not who

they are and in actuality you’re not who

they are and in actuality you’re not who you think we all have slightly incorrect

you think we all have slightly incorrect

you think we all have slightly incorrect perceptions of our skills so let’s let’s

perceptions of our skills so let’s let’s

perceptions of our skills so let’s let’s get back to my mom here for a second

get back to my mom here for a second

get back to my mom here for a second while I would be a little cautious in

while I would be a little cautious in

while I would be a little cautious in saying this directly to her face my

saying this directly to her face my

saying this directly to her face my mother is wrong so my mother understands

mother is wrong so my mother understands

mother is wrong so my mother understands mathematics she understands probability

mathematics she understands probability

mathematics she understands probability she understands statistics she

she understands statistics she

she understands statistics she understands all of these things she does

understands all of these things she does

understands all of these things she does them every single day as she’s making

them every single day as she’s making

them every single day as she’s making decisions throughout her life for

decisions throughout her life for

decisions throughout her life for example when she goes to cross the

example when she goes to cross the

example when she goes to cross the street yes she is standing there

street yes she is standing there

street yes she is standing there she looks both ways and perhaps she

she looks both ways and perhaps she

she looks both ways and perhaps she she’s a car coming and as that car is

she’s a car coming and as that car is

she’s a car coming and as that car is coming she’s building a probabilistic

coming she’s building a probabilistic

coming she’s building a probabilistic model in her head of can I walk into the

model in her head of can I walk into the

model in her head of can I walk into the street right now as the card changes

street right now as the card changes

street right now as the card changes direction slows down speeds up makes

direction slows down speeds up makes

direction slows down speeds up makes different decisions she is constantly

different decisions she is constantly

different decisions she is constantly updating the probability of what’s going

updating the probability of what’s going

updating the probability of what’s going to happen next and at some point she

to happen next and at some point she

to happen next and at some point she optimizes she makes a decision and she

optimizes she makes a decision and she

optimizes she makes a decision and she steps into the street doesn’t get hit by

steps into the street doesn’t get hit by

steps into the street doesn’t get hit by the car it’s probability and statistics

the car it’s probability and statistics

the car it’s probability and statistics at its best another thing that we

at its best another thing that we

at its best another thing that we commonly do all the time if we’re

commonly do all the time if we’re

commonly do all the time if we’re walking down the hall we’re walking in a

walking down the hall we’re walking in a

walking down the hall we’re walking in a crowded area we continually assess the

crowded area we continually assess the

crowded area we continually assess the space around us we predict whether or

space around us we predict whether or

space around us we predict whether or not we’re going to run

not we’re going to run

not we’re going to run into somebody and then we make kind of a

into somebody and then we make kind of a

into somebody and then we make kind of a probabilistic decision of is that person

probabilistic decision of is that person

probabilistic decision of is that person going to move this way I’ll move this

going to move this way I’ll move this

going to move this way I’ll move this way and you’re constantly adjusting as

way and you’re constantly adjusting as

way and you’re constantly adjusting as you move and when two people both move

you move and when two people both move

you move and when two people both move to the same spot there’s kind of a oh

to the same spot there’s kind of a oh

to the same spot there’s kind of a oh sorry and kind of laugh because you know

sorry and kind of laugh because you know

sorry and kind of laugh because you know you’ve guessed wrong somehow you update

you’ve guessed wrong somehow you update

you’ve guessed wrong somehow you update your model and then you fix it and you

your model and then you fix it and you

your model and then you fix it and you move back to the other side these are

move back to the other side these are

move back to the other side these are complicated statistical processes we do

complicated statistical processes we do

complicated statistical processes we do we also make decisions like do we need a

we also make decisions like do we need a

we also make decisions like do we need a coat today do we think it’s going to

coat today do we think it’s going to

coat today do we think it’s going to rain

rain

rain these are non-stop probability and

these are non-stop probability and

these are non-stop probability and statistics modeling decisions we are

statistics modeling decisions we are

statistics modeling decisions we are making all the time so if I’m having

making all the time so if I’m having

making all the time so if I’m having this conversation with my mom let’s look

this conversation with my mom let’s look

this conversation with my mom let’s look at something that might be kind of more

at something that might be kind of more

at something that might be kind of more in her in her wheelhouse so this is a

in her in her wheelhouse so this is a

in her in her wheelhouse so this is a poem the road not taken’ by Robert Frost

poem the road not taken’ by Robert Frost

poem the road not taken’ by Robert Frost and this is an example of a type of

and this is an example of a type of

and this is an example of a type of annotation assignment that you might see

annotation assignment that you might see

annotation assignment that you might see in an English class or a writing class

in an English class or a writing class

in an English class or a writing class etc this is the stuff my mom excels at

etc this is the stuff my mom excels at

etc this is the stuff my mom excels at so there are a couple of ways that we

so there are a couple of ways that we

so there are a couple of ways that we could think about this assignment or

could think about this assignment or

could think about this assignment or this piece of analysis the first is I

this piece of analysis the first is I

this piece of analysis the first is I could just think about analyzing the

could just think about analyzing the

could just think about analyzing the theme write the words in the actual poem

theme write the words in the actual poem

theme write the words in the actual poem and when I’m looking at I might think

and when I’m looking at I might think

and when I’m looking at I might think all right I see the word yellow does

all right I see the word yellow does

all right I see the word yellow does that mean that it’s autumn I’m going to

that mean that it’s autumn I’m going to

that mean that it’s autumn I’m going to start looking for other words that match

start looking for other words that match

start looking for other words that match that potential theme I start looking for

that potential theme I start looking for

that potential theme I start looking for things like do I see more positive words

things like do I see more positive words

things like do I see more positive words do I see more negative words do I see

do I see more negative words do I see

do I see more negative words do I see questioning do I see repetition of words

questioning do I see repetition of words

questioning do I see repetition of words all of this is data that is being

all of this is data that is being

all of this is data that is being processed and helping you build a model

processed and helping you build a model

processed and helping you build a model toward what is the actual theme here

toward what is the actual theme here

toward what is the actual theme here what is happening inside this poem how

what is happening inside this poem how

what is happening inside this poem how am I going to interpret it these are all

am I going to interpret it these are all

am I going to interpret it these are all variables that have you been keeping

variables that have you been keeping

variables that have you been keeping track of the entire time that’s one way

track of the entire time that’s one way

track of the entire time that’s one way to look at this assignment another way

to look at this assignment another way

to look at this assignment another way might be how to actually teach students

might be how to actually teach students

might be how to actually teach students how to annotate so how to find sort of

how to annotate so how to find sort of

how to annotate so how to find sort of strategies that contribute to better

strategies that contribute to better

strategies that contribute to better learning outcomes and I mean things as

learning outcomes and I mean things as

learning outcomes and I mean things as simple like where did you write on the

simple like where did you write on the

simple like where did you write on the page did you write close to the words

page did you write close to the words

page did you write close to the words did you write far away from the words

did you write far away from the words

did you write far away from the words did you underline everything was

did you underline everything was

did you underline everything was everything important which words did you

everything important which words did you

everything important which words did you circle what time of day or night did you

circle what time of day or night did you

circle what time of day or night did you actually do these annotations all of

actually do these annotations all of

actually do these annotations all of this is information that

this is information that

this is information that contribute to how a student is learning

contribute to how a student is learning

contribute to how a student is learning this poem or learning the skills that

this poem or learning the skills that

this poem or learning the skills that we’re trying to teach with this poem so

we’re trying to teach with this poem so

we’re trying to teach with this poem so how I might handle that problem is all

how I might handle that problem is all

how I might handle that problem is all of this data that’s being collected

of this data that’s being collected

of this data that’s being collected I might input as variables into a

I might input as variables into a

I might input as variables into a spreadsheet I could build a kind of a

spreadsheet I could build a kind of a

spreadsheet I could build a kind of a complicated prediction model using some

complicated prediction model using some

complicated prediction model using some statistical software package it might

statistical software package it might

statistical software package it might take me weeks to do correctly my mom the

take me weeks to do correctly my mom the

take me weeks to do correctly my mom the English teacher does this in her head

English teacher does this in her head

English teacher does this in her head she just reads what the students are

she just reads what the students are

she just reads what the students are writing thinks about all of those data

writing thinks about all of those data

writing thinks about all of those data and the variables and creates a

and the variables and creates a

and the variables and creates a prediction model for how the student is

prediction model for how the student is

prediction model for how the student is learning which features might be more

learning which features might be more

learning which features might be more important for her to teach the next day

important for her to teach the next day

important for her to teach the next day in order to improve their chances of

in order to improve their chances of

in order to improve their chances of learning so she’s doing this all of the

learning so she’s doing this all of the

learning so she’s doing this all of the time she’s not thinking of it as doing

time she’s not thinking of it as doing

time she’s not thinking of it as doing statistics or data analysis but she’s

statistics or data analysis but she’s

statistics or data analysis but she’s doing this all the time as are the rest

doing this all the time as are the rest

doing this all the time as are the rest of us the amount of data that we process

of us the amount of data that we process

of us the amount of data that we process analyze model collect etc every day is

analyze model collect etc every day is

analyze model collect etc every day is staggering so by one set of estimates

staggering so by one set of estimates

staggering so by one set of estimates from UC San Diego it’s up around

from UC San Diego it’s up around

from UC San Diego it’s up around something like 16 hard drives worth of

something like 16 hard drives worth of

something like 16 hard drives worth of information for each person each day

information for each person each day

information for each person each day that’s unbelievable

that’s unbelievable

that’s unbelievable so how can people who process analyze

so how can people who process analyze

so how can people who process analyze model collect etc that much data every

model collect etc that much data every

model collect etc that much data every single day and also make fairly rational

single day and also make fairly rational

single day and also make fairly rational choices all day long based on

choices all day long based on

choices all day long based on probabilities how could they possibly

probabilities how could they possibly

probabilities how could they possibly think of themselves as not math people

think of themselves as not math people

think of themselves as not math people yeah so what is going on here and and

yeah so what is going on here and and

yeah so what is going on here and and why am i choosing to talk about this

why am i choosing to talk about this

why am i choosing to talk about this right now why do I think this is more

right now why do I think this is more

right now why do I think this is more important to be considering right now

the explosion of data science all right

the explosion of data science all right so data science as a field as ubiquitous

so data science as a field as ubiquitous

so data science as a field as ubiquitous now there’s constant press coverage and

now there’s constant press coverage and

now there’s constant press coverage and interest from students from industries

interest from students from industries

interest from students from industries from parents who incessantly ask me will

from parents who incessantly ask me will

from parents who incessantly ask me will my children have a job when they

my children have a job when they

my children have a job when they graduate data science is one of the hot

graduate data science is one of the hot

graduate data science is one of the hot buzzwords right now let’s take a look at

buzzwords right now let’s take a look at

buzzwords right now let’s take a look at what’s happening in terms of an

what’s happening in terms of an

what’s happening in terms of an education standpoint with programs so we

education standpoint with programs so we

education standpoint with programs so we have 40

have 40

have 40 bachelor’s degrees right now that

bachelor’s degrees right now that

bachelor’s degrees right now that includes minors

includes minors

includes minors so that’s majors and minors we have 93

so that’s majors and minors we have 93

so that’s majors and minors we have 93 certificates and a certificate could be

certificates and a certificate could be

certificates and a certificate could be viewed as kind of a supplementary thing

viewed as kind of a supplementary thing

viewed as kind of a supplementary thing so you’re majoring in one and then you

so you’re majoring in one and then you

so you’re majoring in one and then you have a supplementary certificate

have a supplementary certificate

have a supplementary certificate there are 19 doctoral degrees and there

there are 19 doctoral degrees and there

there are 19 doctoral degrees and there are almost 400 master’s degrees in data

are almost 400 master’s degrees in data

are almost 400 master’s degrees in data science and these are in departments

science and these are in departments

science and these are in departments like statistics information systems

like statistics information systems

like statistics information systems business machine learning computer

business machine learning computer

business machine learning computer science many of these data science

science many of these data science

science many of these data science degrees are interdisciplinary across

degrees are interdisciplinary across

degrees are interdisciplinary across several departments this is amazing this

several departments this is amazing this

several departments this is amazing this isn’t like the last 10-15 years this is

isn’t like the last 10-15 years this is

isn’t like the last 10-15 years this is an incredible number of degrees in

an incredible number of degrees in

an incredible number of degrees in programs and with respect to the data

programs and with respect to the data

programs and with respect to the data scientist it is a sought-after job it is

scientist it is a sought-after job it is

scientist it is a sought-after job it is considered a top job to have in America

considered a top job to have in America

considered a top job to have in America and there are lots of opportunities but

and there are lots of opportunities but

and there are lots of opportunities but what actually is data science so here’s

what actually is data science so here’s

what actually is data science so here’s one graph here’s one proposed kind of

one graph here’s one proposed kind of

one graph here’s one proposed kind of picture of data science we have math

picture of data science we have math

picture of data science we have math stats and algorithms on the top we have

stats and algorithms on the top we have

stats and algorithms on the top we have software engineering on the Left data

software engineering on the Left data

software engineering on the Left data communication on the right this is a

communication on the right this is a

communication on the right this is a Venn diagram so as the circles overlap

Venn diagram so as the circles overlap

Venn diagram so as the circles overlap those areas are people who have all

those areas are people who have all

those areas are people who have all those skills from the overlapping

those skills from the overlapping

those skills from the overlapping circles and you notice that data

circles and you notice that data

circles and you notice that data scientists is in the middle so a data

scientists is in the middle so a data

scientists is in the middle so a data scientist is someone who theoretically

scientist is someone who theoretically

scientist is someone who theoretically has math stats software engineering and

has math stats software engineering and

has math stats software engineering and data communication you might notice that

data communication you might notice that

data communication you might notice that the intersection between software

the intersection between software

the intersection between software engineering and data communication is

engineering and data communication is

engineering and data communication is empty and are you can make of that what

empty and are you can make of that what

empty and are you can make of that what you will

you will

you will I’m not sure what that means I

I’m not sure what that means I

I’m not sure what that means I personally I personally like this

personally I personally like this

personally I personally like this graphic a little bit better there we go

graphic a little bit better there we go

graphic a little bit better there we go this graphic we have computer science

this graphic we have computer science

this graphic we have computer science we have math and statistics and

we have math and statistics and

we have math and statistics and importantly we have subject matter

importantly we have subject matter

importantly we have subject matter expertise I’ll come back to that and

expertise I’ll come back to that and

expertise I’ll come back to that and having all of those is the mythical

having all of those is the mythical

having all of those is the mythical unicorn yes so when I take from this

unicorn yes so when I take from this

unicorn yes so when I take from this graphic as it’s very complicated to

graphic as it’s very complicated to

graphic as it’s very complicated to think of what a perfect data scientist

think of what a perfect data scientist

think of what a perfect data scientist is so what is somebody who has all of

is so what is somebody who has all of

is so what is somebody who has all of these skills in some sense it’s very

these skills in some sense it’s very

these skills in some sense it’s very flexible and interdisciplinary and to be

flexible and interdisciplinary and to be

flexible and interdisciplinary and to be able to find someone who has all of

able to find someone who has all of

able to find someone who has all of these skills and call them a data

these skills and call them a data

these skills and call them a data scientist is is probably unreasonable my

scientist is is probably unreasonable my

scientist is is probably unreasonable my concern is is that Edie as data science

concern is is that Edie as data science

concern is is that Edie as data science is exploding in order to keep up or

is exploding in order to keep up or

is exploding in order to keep up or compete the number of programs has been

compete the number of programs has been

compete the number of programs has been proliferating so universities are

proliferating so universities are

proliferating so universities are starting data science programs to

starting data science programs to

starting data science programs to attract students to help them give them

attract students to help them give them

attract students to help them give them the skills they’re going to need for

the skills they’re going to need for

the skills they’re going to need for industry but at a national level the

industry but at a national level the

industry but at a national level the conversation is centering more on what

conversation is centering more on what

conversation is centering more on what are the foundations of data science so

are the foundations of data science so

are the foundations of data science so how do we understand and how do we

how do we understand and how do we

how do we understand and how do we understand and define data science as a

understand and define data science as a

understand and define data science as a discipline so for example the National

discipline so for example the National

discipline so for example the National Science Foundation in the National

Science Foundation in the National

Science Foundation in the National Academy of Sciences have recently

Academy of Sciences have recently

Academy of Sciences have recently devoted a non-trivial amount of

devoted a non-trivial amount of

devoted a non-trivial amount of financial and manpower resources to this

financial and manpower resources to this

financial and manpower resources to this topic and as a result we have we might

topic and as a result we have we might

topic and as a result we have we might we can build the programs but will the

we can build the programs but will the

we can build the programs but will the people actually come to them who’s going

people actually come to them who’s going

people actually come to them who’s going to join these programs

to join these programs

to join these programs who’s going to think yes that’s for me

who’s going to think yes that’s for me

who’s going to think yes that’s for me and who is going to think oh I’m going

and who is going to think oh I’m going

and who is going to think oh I’m going to opt out of that and my concern is is

to opt out of that and my concern is is

to opt out of that and my concern is is that this is going to lead to a stronger

that this is going to lead to a stronger

that this is going to lead to a stronger bifurcation of programs and people so

bifurcation of programs and people so

bifurcation of programs and people so people who do data science and people

people who do data science and people

people who do data science and people who do not do data science and that

who do not do data science and that

who do not do data science and that decision will be made based on some

decision will be made based on some

decision will be made based on some self-perception of skills that’s

self-perception of skills that’s

self-perception of skills that’s inaccurate so for example everybody does

inaccurate so for example everybody does

inaccurate so for example everybody does everybody crosses the street everybody

everybody crosses the street everybody

everybody crosses the street everybody builds a probabilistic model and give

builds a probabilistic model and give

builds a probabilistic model and give getting through their day in some sense

getting through their day in some sense

getting through their day in some sense okay so everybody is able to communicate

okay so everybody is able to communicate

okay so everybody is able to communicate and talk to people working in data

and talk to people working in data

and talk to people working in data science they do this all of the time now

science they do this all of the time now

science they do this all of the time now let’s do you think that I am letting the

let’s do you think that I am letting the

let’s do you think that I am letting the people who think they’re good at math

people who think they’re good at math

people who think they’re good at math Institute

Institute

Institute 6 etc off-the-hook know if you do not

6 etc off-the-hook know if you do not

6 etc off-the-hook know if you do not understand how people think and write

understand how people think and write

understand how people think and write and create and behave then you are also

and create and behave then you are also

and create and behave then you are also part of the problem is going to be

part of the problem is going to be

part of the problem is going to be necessary for these disciplines to

necessary for these disciplines to

necessary for these disciplines to communicate with each other so I’m not

communicate with each other so I’m not

communicate with each other so I’m not advocating for example that everyone

advocating for example that everyone

advocating for example that everyone take a data science course or that

take a data science course or that

take a data science course or that everyone be in data science I’m

everyone be in data science I’m

everyone be in data science I’m advocating for collaboration and

advocating for collaboration and

advocating for collaboration and communication across the disciplines I’m

communication across the disciplines I’m

communication across the disciplines I’m advocating for everyone in this entire

advocating for everyone in this entire

advocating for everyone in this entire room to think about your skillsets and

room to think about your skillsets and

room to think about your skillsets and what kind of person you think you are

what kind of person you think you are

what kind of person you think you are and really take a hard look at that

and really take a hard look at that

and really take a hard look at that because you probably know more than you

because you probably know more than you

because you probably know more than you think you do and you can communicate

think you do and you can communicate

think you do and you can communicate with these areas in a stronger way than

with these areas in a stronger way than

with these areas in a stronger way than you probably believe you can and when

you probably believe you can and when

you probably believe you can and when you come to several roads diverging we

you come to several roads diverging we

you come to several roads diverging we want you to be able to see yourself on

want you to be able to see yourself on

want you to be able to see yourself on several paths and not be forced to take

several paths and not be forced to take

several paths and not be forced to take one of them so coming back to my mom the

one of them so coming back to my mom the

one of them so coming back to my mom the woman on the left she has a

woman on the left she has a

woman on the left she has a undergraduate degree in math and

undergraduate degree in math and

undergraduate degree in math and statistics she has multiple graduate

statistics she has multiple graduate

statistics she has multiple graduate degrees in statistics she works in

degrees in statistics she works in

degrees in statistics she works in statistics she spends a lot of time on a

statistics she spends a lot of time on a

statistics she spends a lot of time on a computer she is a data scientist the

computer she is a data scientist the

computer she is a data scientist the woman on the right

woman on the right

woman on the right she has degrees in English literature

she has degrees in English literature

she has degrees in English literature speaks Latin she analyzes texts teaches

speaks Latin she analyzes texts teaches

speaks Latin she analyzes texts teaches people how to write teaches people how

people how to write teaches people how

people how to write teaches people how to read critically she is also a data

to read critically she is also a data

to read critically she is also a data scientist you are all data scientists

scientist you are all data scientists

scientist you are all data scientists and imagine what we could accomplish

and imagine what we could accomplish

and imagine what we could accomplish together thank you

together thank you

together thank you [Applause]