SDS 344: History of Data Science – Part 3

Welcome back to the SuperDataScience podcast, everybody. Super excited to have you back here on the show, and in today’s episode we are continuing the story of data science, the history of data science. We’ll be looking at the period 2010 to 2015, but before we jump in I’ve got a very exciting, super thrilling announcement for you, especially for those of you who are in Europe. We are kicking off DataScienceGO Europe. So yes, our conference, our US Conference that has been running for over three years, this is our fourth year running it, is coming to Europe this year. It will be running in addition to the US event, we’re running two events this year. But yes, you will be able to attend in Europe, if you’ve always wanted to come to DataScienceGO this is your chance. Head on over to datasciencego.com and select the European version of the conference. The dates are 15th, 16th, 17th of May and get your tickets today.

This is going to be happening in Berlin, it’s a weekend. And we are super thrilled to be bringing amazing speakers, I’ll be there, Hadelin will be there and lots of interesting guests like Rico who’ll also be speaking and many other people will be speaking. It is a very limited event, we only have 200 seats for this conference and that’s because we want to run it small to test things out in Europe and do our first event, very small intimate event in Europe compared to our US event, which is 800 people. So only 200 seats, 150 or 170 beginner and transitioning practitioners. So if you’re transitioning into data science, this is for you and 30 seats for advanced data scientists, another intimate group within the conference for the advanced data science track. So check it out it’s at datasciencego.com.

And now let’s head on over back to the history of data science. So what was happening in the years 2010 to 2015? As you can see our periods are getting shorter before we were dealing with everything before basically the 2000 and 2010, now 2010 to 2015. Well, an American journalist and author of books and technology and society Kenneth Cukier, kicks it off with a brilliant summary and he said, “A new kind of professional has emerged, the data scientist who combines the skills of software programmers, the decision and storyteller artist or artists to extract the nuggets of gold hidden under mountains of data.” So that’s a very succinct summary of what a data scientist is and it’s really cool that this was identified as early as 2010.

Well, why was data science becoming more and more popular? Because we could start seeing very clear and global examples of its practical use cases. And one of these examples, one of these developments was self-driving cars. This began earlier before 2010 but… And especially that happened in industry. However, like for instance, Rio Tinto had this mind of the future in Western Australia, they’ve had it for a long time and have self-driving trucks there. But the trend really took off, especially got publicity and started hitting the world of retail, getting into the world of our own normal day to day lives around 2010 or after 2010.

So here’s a couple of examples. In 2010 Audi’s driverless car performed a high speed test and soon after that partially self-driving cars were tested and showcased by General Motors, Volkswagen and Toyota. Next, the first Intercontinental, a land’s journey by autonomous vehicles was completed, that is crazy.

Intercontinental land journey with four electric vans traveling from Italy to Shanghai, they probably had four traveling just in case two breakdown or three breakdown, they still get one to the end. But anyway, they had four electric vans traveling from Italy to Shanghai to China and humans had to intervene only on very few separate occasions. And following that they were testing in real conditions also in the US and Germany. Next in 2014 tech giants started to get involved in the field such as Google and Tesla taking the lead, showcasing the increasing power of what are essentially data companies breaking into this traditional field of automobiles. How insane is that? We’ve got companies there, that have been around for hundreds of years and Google and Tesla break into the field with the power of data science and artificial intelligence.

Google trends, if you look at Google trends, it shows tremendous growth in searches for data scientists. Starting around 2010, with data science, machine learning, deep learning and python having similar growth. Interesting enough artificial intelligence or how this different tragic use, actually very popular somewhere around the year 2000 with the movies such as AI and the Matrix causing huge spurs and then dropped off and then it’s been picking up in recent years coming close to that popularity again. So had a bit of a different run, but it’s picking up again as well. And this trend of popularity didn’t stop. It actually kept growing and companies started catching on to the value of data that there’s a lot of value in this digital gold that is running in the veins of their businesses called data and data scientists who’s speaks started becoming more and more popular.

And by 2012, thousands of data scientists were helping both startups and established companies wrestle with information coming in at astonishing rates. And at that time as well a very now famous article was published by Tom Davenport, who’s a senior advisor to Deloitte analytics and DJ Patil who is the ex-Chief Data Scientist of the United States. How crazy is that? Well, he was the Chief Data Scientist of the United States. In this article for the Harvard Business Review, you probably know this one if you haven’t read it yet. Check it out it’s called the Sexiest Job of the 21st Century. Really gave that extra kick to data science and put some more fuel in the fire.

Some of the first conferences specifically for data scientists started appearing in around 2013 such as the European Conference on Data Analysis and more practical events like the Data Incubator fold soon after in 2014. Journals, events and sections were increasingly changing their names to reflect the growing importance of data science, both in public as well as business perception.

However, data science was still a relatively closed area and paths into led almost exclusively through traditional education. As normally as the case with something so new so I guess the difference with data science was that it’s just exploding growth with the, how quickly the fields of AI and data and machine learning are developing. So no curriculum could have kept up with all of that back in the day, the world was not prepared for all this data science coming along. So statisticians and mathematicians and people from different industries, physicist and finance analysts had to take on all that burden, all that heavy weight of data science. But eventually, as we will see in the following episodes of this series, education data science started becoming more and more available.

Let’s summarize this period, in a nutshell from 2010 to 2015 it was an incredible time for data science, going from obscurity to popularity from the next big thing to the thing. But the best is yet to come through modern educational approaches data science would soon empower millions of people across the world. So join me next week when we close out the History of Data Science up to the year 2020 and actually take even a peak into the future. It’s going to be a fun episode. I look forward to seeing you there. Until next time, happy analyzing.