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comData Science is Hot; Here’s Why it’s CoolMark PalmerBlockedUnblockFollowFollowingFeb 15How would you explain data science to a high school student?“Can you talk to my class about Data Science?” asked Bill Gibson, our local high school computer science teacher.
As the head of analytics for a silicon valley tech company, I quickly said yes.
I talk analytics with bigwigs at big companies every day — 20 high school kids sounded like a piece of cake.
The talk was not a piece of cake; it was a different cake altogether.
It’s one thing to preach to the converted; this time, I had to do the converting.
I hate to admit it, but I started with a sucker punch.
We don’t live in California, so the idea of working in high-tech is new to kids here.
I told the class that the average data scientist makes about $113,000 a year, but that’s just the average.
If you get excellent, you can make more, and in some companies have stock that can be worth much, much more.
I had their attention.
Why Does Data Science Matter?I wanted to start by addressing the essential question: why does data science matter?Thanks to technology, more data was created in the last two years than in the previous 5,000 years — combined.
Mobile phones.
Refrigerators.
Drones.
They’re all chirping data, and that data has new, hidden meaning.
As a result, data science presents the opportunity to answer questions that have never been knowable before, about almost any topic.
We’re at the beginning of data science Renaissance, for nerds.
Data science effects every profession, from CPAs to CEOs, from cops to coaches, from butchers to builders.
It can help discover fraud, predict crop yields and find new drugs.
Data scientists help explain global warming, how border walls work, and how the New England Patriots pulled off another big Superbowl win.
The list of questions and potential answers that we can discover is endless.
What Do Data Scientist Do?My seven-word summary of what a data scientist does is this: a data scientist extracts meaning from data.
That is, a data scientist is a statistical sleuth.
A messenger of meaning.
A data detective.
We create between one and two trillion statistical graphics every year.
Somebody has to explain them!I wanted to make it concrete, so I projected a slide with these numbers, and asked them what they saw.
These numbers share no story, no information, no insight.
And that’s the point — the job of a data scientist is to uncover the hidden meaning in numbers.
And that’s what Joseph Minard did in 1869 with these numbers, which represent data about Napolean’s march on Russia.
Minard created what many consider the best statistical graphic of all time.
It shows six dimensions of data in one graphic, including the number of troops, their direction of travel, their location, and the temperature.
Then we explored the story and the meaning of his work.
Charles Minard’s map of Napoleon’s disastrous Russian campaign of 1812.
The tan area represents the size and direction of Napolean’s army.
Beginning at the left, we see that Napolean entered Russia in 1812 with 422,000 troops.
The tan area shrinks as they travel east, to the right on the chart.
They reach Moscow six months later.
At far right, the tan area is 75% smaller than it started.
322,000 troops had died.
Wow.
That’s how you share meaning.
Next, far right, they retreat, shown in black.
Minard adds a data dagger — a simple line along the bottom that shows the temperature, beginning at 0 degrees and dipping as low as 30 degrees below zero.
Brrrrrr.
Finally, the French army exits Russia at left as a thin black sliver: only 10,000 men survived.
Minard’s graphic is the Mona Lisa of data science — the longer you linger, the more you learn.
For example, on September 28, 1813, the thickness of the black line is cut in half as 22,000 men perish trying to cross the Berezina river, just outside of Minsk.
Why did Minard create this graphic?.It was his instrument to protest war, and it still plays its tune, 150 years later.
Did My Talk Work?I don’t know if the talk worked.
My son told me that everyone was talking about how much data scientists make.
Oh well, at least they were talking!.Maybe one of them become a statistical sleuth, a messenger of meaning, or a data detective, one day.
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