Intro to Data Science: Historical Context

Summary

This lecture explores the concept of data science, emphasizing its long-standing roots in human history. It distinguishes between data-driven science and the emerging field of data science, which involves handling, cleaning, storing, visualizing, and modeling data. The talk uses the historical example of Tycho Brahe's meticulous planetary observations, crucial for Kepler's laws and Newton's theories, to illustrate data science's impact. It also highlights the importance of moving from descriptive models like Kepler's to generalizable theories like Newton's, a goal for modern data scientists and machine learning practitioners.

Takeaways

• 🔬 Data science is not a new concept; humans have been collecting and modeling data for centuries.
• 📚 The term 'data science' can mean different things to different people, often referring to data-intensive science, engineering, or data-driven inquiry.
• 🌌 Astronomy is highlighted as an example of a data-intensive science, where data collection and analysis have been pivotal in understanding planetary motion.
• 📈 Tycho Brahe's meticulous data collection on planetary and star movements was instrumental in Kepler's discovery of elliptical orbits.
• 🔍 Brahe's dedication to rigorous data collection and storage laid the groundwork for future scientific advancements.
• 🐃 Fun fact: Tycho Brahe was an intriguing character with a pet moose that enjoyed beer, reflecting his unique personality.
• 📚 Kepler's laws describe the elliptical motion of planets, while Newton's laws explain why planets move in these orbits, demonstrating the progression from description to cause.
• 🚀 Newton's generalized laws allowed for practical applications like the Apollo program, showing the importance of generalization in scientific theories.
• 🤖 Modern machine learning algorithms often describe the world as observed (like Kepler), but the goal should be to create models that generalize like Newton's did.
• 📈 The 'fourth paradigm' of data-intensive scientific discovery complements traditional methods like theory, experiments, and simulations, rather than replacing them.
• 📘 For those interested in the technical aspects of data science, the book 'Data-Driven Science and Engineering' and the associated website offer in-depth lectures and resources.

Q & A

What is the main focus of the lecture series on data science?

The lecture series focuses on providing an introductory overview of data science, explaining what it is, how it can be used, and its various aspects.

Why is it emphasized that data science is not a new concept?

It is emphasized because humans have been collecting and modeling data for centuries, and the concept of data science has evolved over time rather than being a completely new invention.

What are the different interpretations of the term 'data science' mentioned in the script?

The different interpretations include data-intensive science, data-intensive engineering, and data-driven inquiry, all of which involve using data to drive scientific investigation and discovery.

What is an example of a data-intensive science field mentioned in the script?

Astronomy is given as an example of a data-intensive science field, where the collection and analysis of data about celestial bodies have been crucial for scientific advancements.

Who is Tycho Brahe and why was he significant in the history of data science?

Tycho Brahe was a Danish astronomer known for his meticulous data collection on the motion of planets and stars, which was instrumental in Kepler's discovery of planetary motion laws.

What inconsistency did Tycho Brahe notice between the models of his time and his observations?

Tycho Brahe noticed inconsistencies between the predicted planetary conjunctions and the models of planetary motion of his time, leading him to collect rigorous and systematic data.

What is the significance of Kepler's laws of planetary motion in the context of data science?

Kepler's laws describe the elliptical orbits of planets, which were derived from the data collected by Tycho Brahe. This demonstrates the power of data in shaping scientific understanding and theories.

How did Isaac Newton's work build upon the foundation laid by Tycho Brahe and Kepler?

Newton explained why planets move in elliptical orbits by formulating the universal law of gravitation, which generalized the principles behind planetary motion and enabled further scientific and technological advancements.

What is the difference between Kepler's and Newton's approaches to modeling the world, as discussed in the script?

Kepler built a model based on observed data describing how the solar system works, while Newton generalized these observations into a physical principle that could predict and explain a wider range of phenomena.

What is the 'fourth paradigm' referred to in the script, and how does it relate to data science?

The 'fourth paradigm' refers to data-intensive scientific discovery, which complements traditional methods like theory, experimentation, and computation by leveraging massive amounts of data for scientific insights.

What resource is recommended for those interested in the mathematical aspects of data science?

The book 'Data-Driven Science and Engineering' co-authored by the speaker and Nathan Cutts is recommended, along with their website databook.udub.com, which contains lectures and videos on various topics.

Outlines

00:00

📚 Introduction to Data Science and Its Historical Roots

This paragraph introduces the concept of data science, emphasizing that it is not a new field but rather an evolution of human practices that date back centuries. The speaker clarifies that data science can mean different things to different people, such as data-intensive science, data-intensive engineering, or data-driven inquiry. The paragraph uses the example of astronomy to illustrate data science in action, highlighting Tycho Brahe's meticulous data collection on planetary motion, which was instrumental in Kepler's discovery of elliptical orbits. The historical narrative also touches on the significance of data in scientific discovery and the transition from observational data to the formulation of universal laws, as demonstrated by Newton's work on gravitational forces.

05:01

🚀 From Observational Models to Generalized Theories in Data Science

The second paragraph delves into the distinction between descriptive models like Kepler's elliptical orbits and the generalized theories that enable practical applications, such as Newton's laws of motion. It discusses the importance of moving from data description to data generalization, which is essential for advancements like the Apollo moon landings. The speaker also references 'The Fourth Paradigm: Data-Intensive Scientific Discovery,' a book that outlines the progression of scientific methods, from theoretical analysis to data-driven inquiry. The paragraph concludes with a recommendation for those interested in the technical aspects of data science to explore a book co-authored by the speaker and Nathan Cutts, which covers the mathematical foundations of data science algorithms, and mentions a website where lectures on various topics are available.

Keywords

💡Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data. In the video, it is emphasized that data science is not a new concept but has been practiced for centuries in various forms. The script discusses how data science is about handling data through collection, cleaning, storage, visualization, and modeling, which is crucial for understanding the world and making informed decisions.

💡Data-Intensive Science

Data-Intensive Science refers to scientific disciplines that rely heavily on large volumes of data for analysis and discovery. The video uses astronomy as an example, where the collection of planetary motion data by Tycho Brahe was critical for Kepler's discovery of elliptical orbits. This concept is integral to the theme of the video, illustrating how data-driven approaches can lead to significant scientific breakthroughs.

💡Data-Driven Inquiry

Data-Driven Inquiry is the process of asking and answering questions based on data analysis. The video script mentions this concept as a motivation for the field of data science, emphasizing the importance of using data to inform scientific hypotheses and theories. It is a key aspect of modern scientific research, where conclusions are drawn from the analysis of collected data.

💡Astronomy

Astronomy is the scientific study of celestial objects, space, and the physical universe as a whole. In the context of the video, astronomy serves as an example of a data-intensive science, where the meticulous data collection by Tycho Brahe on planetary motion was instrumental in advancing the field through Kepler's laws and Newton's theories.

💡Tycho Brahe

Tycho Brahe was a Danish astronomer known for his accurate and comprehensive astronomical and planetary observations. The script highlights his dedication to collecting rigorous and systematic data, which was crucial for Kepler's laws of planetary motion. Brahe's work exemplifies the importance of data collection in the scientific process.

💡Johannes Kepler

Johannes Kepler was a German astronomer and mathematician whose laws of planetary motion described the orbits of planets as ellipses. The video script uses Kepler's work to illustrate how data analysis can lead to fundamental scientific laws, which in turn can be built upon by subsequent researchers, such as Newton, to develop more generalized theories.

💡Isaac Newton

Isaac Newton was an English mathematician, physicist, and astronomer known for his laws of motion and universal gravitation. The video emphasizes Newton's quote about the 'preponderance of the evidence' supporting his theories, highlighting the role of data in validating scientific hypotheses. Newton's work represents a shift from describing the world to generalizing principles that can predict and explain phenomena.

💡Data Collection

Data Collection is the process of gathering and measuring data from various sources. In the video, it is highlighted as a foundational aspect of data science, with Tycho Brahe's systematic data collection on planetary motion being a key example. Effective data collection is essential for accurate analysis and informed decision-making in any scientific discipline.

💡Modeling

Modeling in the context of data science refers to the creation of mathematical or computational representations to understand and make predictions about data. The video discusses how data science involves not only collecting and analyzing data but also building models to represent and predict phenomena, as seen in Kepler's elliptical orbits and Newton's laws of motion.

💡Generalization

Generalization in data science is the ability of a model to make predictions or explain phenomena beyond the specific data it was trained on. The video contrasts Kepler's descriptive model of planetary motion with Newton's generalizable laws of motion, emphasizing the importance of creating models that can be applied broadly and predict new outcomes.

💡The Fourth Paradigm

The Fourth Paradigm refers to the era of data-intensive scientific discovery, as described in the book 'The Fourth Paradigm: Data-Intensive Scientific Discovery' mentioned in the video. It represents a shift towards using large datasets to complement traditional scientific methods like theory, experimentation, and computation. The video script positions data science as an integral part of this paradigm, highlighting its role in advancing scientific knowledge.

Highlights

Data science is not a new concept; humans have been practicing it for centuries.

Data science can mean different things to different people, such as data-intensive science, data-intensive engineering, or data-driven inquiry.

Astronomy is an excellent example of a data-intensive science, with historical roots in the work of Tycho Brahe, Johannes Kepler, and Isaac Newton.

Tycho Brahe's meticulous data collection on planetary motion was critical for Kepler's discovery of elliptical orbits.

Brahe's dedication to rigorous data collection led to a systematic format that was crucial for scientific advancement.

Kepler's laws of planetary motion were a result of analyzing Brahe's data, illustrating the importance of data in scientific discovery.

Isaac Newton's work built upon Kepler's model, providing a generalized physical principle that explained why planets move in ellipses.

Newton's famous quote emphasizes the significance of data in supporting scientific hypotheses and theories.

The difference between Kepler's descriptive model and Newton's generalized theory is a key concept for modern data scientists and machine learning practitioners.

Data science as a field is about handling data through collection, cleaning, storage, visualization, and modeling.

The fourth paradigm of data-intensive scientific discovery complements traditional methods like theory, experimentation, and computation.

Data science does not replace existing scientific methods but rather integrates and enhances them with data-driven insights.

The book 'The Fourth Paradigm: Data-Intensive Scientific Discovery' discusses the progression and impact of data-driven science.

Astronomy's historical context provides a clear example of how data science has been integral to scientific progress.

The story of Tycho Brahe's dedication to data collection and its impact on Kepler and Newton's work highlights the value of rigorous data in science.

The transition from Kepler's descriptive model to Newton's generalized theory represents a goal for modern machine learning algorithms to achieve broader applicability.

The book 'Data-Driven Science and Engineering' by the speaker and Nathan Cutts provides a deeper dive into the mathematical foundations of data science.

The speaker's website offers lectures and resources for those interested in the technical aspects of data science, machine learning, and their mathematical underpinnings.

Transcripts

welcome back so we're talking about data

science this is an intro overview

lecture series on kind of what is data

science how can you use it what are the

aspects and one thing I think is just

really important to emphasize is the

data science is not new we've been doing

data science as humans for hundreds

thousands of years collecting data

modeling the world through that data and

I think data science as a terminology

means different things to different

people so there's what I like to think

of as data intensive science data

intensive engineering or data-driven

inquiry and that's science that you do

based on data ok like if I want to solve

so astronomy is a great example of

something that is data intensive science

I think of the phrase data science this

is an emerging scientific discipline

which is motivated by data intensive

science but it's really the science of

how do you handle data collect clean

store visualize and model with data so

it's a little confusing you have data

driven science and that motivates this

whole new field of science and

engineering called data science and I'm

going to use them interchangeably but

that I just want to kind of deconflict

those two terms early on and astronomy

is a great example I want to walk you

through just this very interesting

history example that I loved about kind

of Tycho Brahe and Kepler and Newton to

give some idea of what data science

looks like in a historical context so

this is Tycho Brahe great Danish

astronomer who collected the rich data

set of the motion of planets and stars

that was critical in Kepler's discovery

of his his ellipses and planetary motion

so to some extent Tycho Brahe was

noticed inconsistencies between the

models of the time kind of the the old

law

of how the planets would move and he

noticed inconsistencies with what he

observed so you know he there was this

predicted conjunction of planets and it

didn't agree with the models to his

satisfaction and so he realized this I

think was as a teenager that he needed

to collect rigorous clean data to store

it in a systematic format and to to make

a science out of the data collection of

planets and stars and he dedicated his

life to this he had an island between

Copenhagen and Sweden I don't know if

you can see it here but this is his

science island of hven where he

collected all of this rich data and he

guarded this data so this was his life's

work and he knew how much value and it

turns out Kepler didn't even really have

full access to the data until Tico Bray

passed away and so so both of the knew

the value of the data and kind of moving

moving the theory of planetary motion

forward and this was a critical piece in

Kepler's famous law of the elliptic

planets elliptic motion of planets fun

fact about Tico very interesting

character I encourage you to read more

about him he lost the tip of his nose in

a duel when he was a young man arguing

about who was a better mathematician on

his Science Island he had a pet moose

which was apparently very fond of beer

and would entertain his guests by

drinking a tremendous amount of beer so

Chico Bray is a really interesting guy

you can only imagine what his

personality would be like he had to you

know he made his life's work of very

very very careful observations which

changed the world forever through

through those who came after and I think

this also laid the foundation so this

this data intensive inquiry laid the

foundation for what Newton would go on

to do so

Kepler described these elliptic motion

of the planets and Newton explained why

the plants move in these ellipses and

actually I think a great

quote by Isaac Newton's Newton when he

was explaining one of his theories he

said that it was because of a

preponderance of the evidence and that's

another way of saying the data supported

his hypothesis or his theory and

something else I think is really

fascinating that we should think about

as data scientists and modelers and

machine learning people today and this

is something I talk a lot about with my

colleague Nathan Cutts is this idea of

the difference between Kepler and Newton

so Kepler built a model of how things

work the way they work on these

elliptical planets this is kind of I

think of an attractor of how how the

world and how the the solar system works

in these elliptical orbits that theory

was useful but it wouldn't have allowed

us to to develop the Apollo program and

and put people on the moon okay and so

what Newton did was somehow a

generalization he distilled the abstract

physical principle that gave rise to

elliptic orbits but in a way that you

could tell you what would happen if you

left your elliptical orbit so what would

happen if you left or pushed on the

system out of the way that it always

behaves and we've always observed it and

his theory truly generalized F equals MA

generalized in a way that allowed us to

land people on the moon which is which

is really a huge achievement and so we

talk about this a lot a lot of machine

learning algorithms today most of them I

would say do what Kepler did they

describe the world as we observe it as

the data describes it and it takes this

epiphany this great leap to get a model

that truly generalizes like what Newton

did and so we should be aspiring to make

our algorithms go

you know from Kepler to Newton and that

that's a worthwhile goal it's also very

very challenging okay so data science

has been around for a long time there's

a really interesting modern book called

the fourth paradigm data-intensive

scientific discovery which basically

shows or describes this progression from

kind of theory and analytics

Mattox to experiments collecting data

from you know running experiments to

test hypotheses to simulations and

numerix and computations kind of the the

digital you know silicon age and now

this fourth paradigm of data-driven

inquiry and scientific discovery really

interesting and you know how this

complements this doesn't this doesn't

displace theory or numerix or

experiments it complements these

generate massive amounts of data and we

need a science that ties these together

okay just like simulations didn't

displace experiments they complement

each other okay so that's just a very

high-level overview I will point out for

those of you who are kind of more

interested in the nuts and bolts of

machine learning and modeling and kind

of the linear algebra and optimization

underlying these data science algorithms

I'll recommend a book that my colleague

Nathan cuts and I just wrote data-driven

science and engineering in Cambridge and

we have a website data book u-dub com

where we filmed up all of our lectures

for all of the chapters and sections so

for example you can go to our website

and find you know different topics

you're interested in and see our YouTube

videos so if you're interested hopefully

that's a resource to kind of get into

the more nitty gritty mathematical

aspects okay thank you