Data Science to the Rescue
You’ve probably heard that being a data scientist is the sexiest career of the 21st century, one where you can earn a healthy salary, and a great work-life balance.
Google’s Chief Economist, Hal Varian, has said that, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades”.
GiveDirectly is just one example of how organizations win by using data to their advantage.
According to LinkedIn, Statistical Analysis & Data Mining were the hottest skills that got recruiters’ attention in 2014. Glassdoor ranked Data Scientist as the #1 job to pursue in 2016. Some people have even called it the sexiest career of the 21st century.
Google’s Chief Economist, Hal Varian, has said that, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades”.
But sending people to each village could take several trips at a crushing expense, creating overheads for an organization looking to operate leanly.
“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
You’ve probably heard that being a data scientist is the sexiest career of the 21st century, one where you can earn a healthy salary, and a great work-life balance.
Google’s Chief Economist, Hal Varian, has said that, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades”.
GiveDirectly is just one example of how organizations win by using data to their advantage.
According to LinkedIn, Statistical Analysis & Data Mining were the hottest skills that got recruiters’ attention in 2014. Glassdoor ranked Data Scientist as the #1 job to pursue in 2016. Some people have even called it the sexiest career of the 21st century.
Google’s Chief Economist, Hal Varian, has said that, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades”.
But sending people to each village could take several trips at a crushing expense, creating overheads for an organization looking to operate leanly.
“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
- Hal Varian, Google’s Chief Economist
Liaising with GiveDirectly, a pair of industry experts from IBM and Enigma set out to see if data science could help.
Using satellite images provided by Google, they were able to use computers to classify which villages had metal roofs on top of their houses, and which ones had thatch. They were able to determine which villages needed the most help without sending a single person to the area.
This required mining satellite data and making sense of massive amounts of data, something that would have been impossible a decade ago. It required implementing machine learning algorithms, a cutting-edge technology at the time, to train computers to recognize patterns.
These data scientists were able to pinpoint where GiveDirectly should operate, saving the organization hundreds of man-hours and allowing it to do what it does best: solving extreme poverty.
What is Data Science?
Using satellite images provided by Google, they were able to use computers to classify which villages had metal roofs on top of their houses, and which ones had thatch. They were able to determine which villages needed the most help without sending a single person to the area.
This required mining satellite data and making sense of massive amounts of data, something that would have been impossible a decade ago. It required implementing machine learning algorithms, a cutting-edge technology at the time, to train computers to recognize patterns.
These data scientists were able to pinpoint where GiveDirectly should operate, saving the organization hundreds of man-hours and allowing it to do what it does best: solving extreme poverty.
What is Data Science?
GiveDirectly is just one example of how organizations win by using data to their advantage.
Around the world, organizations are creating more data every day, yet most are struggling to benefit from it. According to McKinsey, the US alone will face a shortage of 150,000+ data analysts and an additional 1.5 million data-savvy managers.
According to LinkedIn, Statistical Analysis & Data Mining were the hottest skills that got recruiters’ attention in 2014. Glassdoor ranked Data Scientist as the #1 job to pursue in 2016. Harvard Business Review even called it the sexiest career of the 21st century.
GiveDirectly was able to save thousands of dollars and put their money where their mission is thanks to a team of three data scientists. Within the mass of data the world generates every day, similar insights are hidden away. Each may have the potential to transform entire industries, or to improve millions of lives.
Salary trends have followed the impact data science drives. With a national average salary of $118k (which increases to $126k in Silicon Valley), data science has become a lucrative career path where you can solve hard problems and drive social impact.
Since you’re reading this guide, you’re likely curious about a career in Data Science, and you’ve probably heard some of these facts and figures. You likely know that data science is a career where you can do good while doing well.
You’re ready to dig beyond the surface, and see real-life examples of data science, and get real-life advice from practitioners in the field.
That’s exactly why we wrote this guide. To bring data science careers to life, for thousands of data-curious, savvy young professionals. We hope that after reading this guide, you have a solid understanding of the data science industry, and know what it takes to get your first data science job. We also want to leave you
with a checklist of actionable advice which will help you throughout your data science career.
The Foundations of Data Science
DJ Patil, the current Chief Data Scientist of the United States and previously the Head of Data Products at Linkedin, is the one who first coined the term data science.
A decade after it was first used, the term remains contested. There is a lot of debate among practitioners and academics about what data science means, and whether it’s different at all from the data analytics that companies have always done.
One of the most substantive differences is the amount of data you have to process now as opposed to a decade ago. In 2020, the world will generate 50x more data than we generated in 2011. Data science can be considered an interdisciplinary solution to the explosion of data that takes old data analytics approaches, and uses machines to augment and scale their effects on larger data sets.
DJ posits that, “the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested.” There is no mention here of a strict definition of data science, nor of a profile that must fit it.
Baseball players used to be judged by how good scouts thought they looked, not how many times they got on base--that was until the Oakland A’s won an all-time league record 20 games in a row with one of the lowest paid rosters in the league. Elections used to swing from party to party with little semblance of predictive accuracy--that was until Nate Silver correctly predicted every electoral vote in the 2012 elections.
Data, and a systematic approach to uncover truths about the world around us, have changed the world.
“More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them,” concludes Patil.
To do data science, you have to be able to find and process large datasets. You’ll often need to understand and use programming, math, and technical communication skills.
Most importantly, you need to have a sense of intellectual curiosity to understand the world through data, and not be deterred easily by obstacles.
You might not think you know anything about data science, but if you’ve ever looked for a Wikipedia table to settle a debate with one of your friends, you were doing a little bit of data science.
Around the world, organizations are creating more data every day, yet most are struggling to benefit from it. According to McKinsey, the US alone will face a shortage of 150,000+ data analysts and an additional 1.5 million data-savvy managers.
According to LinkedIn, Statistical Analysis & Data Mining were the hottest skills that got recruiters’ attention in 2014. Glassdoor ranked Data Scientist as the #1 job to pursue in 2016. Harvard Business Review even called it the sexiest career of the 21st century.
GiveDirectly was able to save thousands of dollars and put their money where their mission is thanks to a team of three data scientists. Within the mass of data the world generates every day, similar insights are hidden away. Each may have the potential to transform entire industries, or to improve millions of lives.
Salary trends have followed the impact data science drives. With a national average salary of $118k (which increases to $126k in Silicon Valley), data science has become a lucrative career path where you can solve hard problems and drive social impact.
Since you’re reading this guide, you’re likely curious about a career in Data Science, and you’ve probably heard some of these facts and figures. You likely know that data science is a career where you can do good while doing well.
You’re ready to dig beyond the surface, and see real-life examples of data science, and get real-life advice from practitioners in the field.
That’s exactly why we wrote this guide. To bring data science careers to life, for thousands of data-curious, savvy young professionals. We hope that after reading this guide, you have a solid understanding of the data science industry, and know what it takes to get your first data science job. We also want to leave you
with a checklist of actionable advice which will help you throughout your data science career.
The Foundations of Data Science
DJ Patil, the current Chief Data Scientist of the United States and previously the Head of Data Products at Linkedin, is the one who first coined the term data science.
A decade after it was first used, the term remains contested. There is a lot of debate among practitioners and academics about what data science means, and whether it’s different at all from the data analytics that companies have always done.
One of the most substantive differences is the amount of data you have to process now as opposed to a decade ago. In 2020, the world will generate 50x more data than we generated in 2011. Data science can be considered an interdisciplinary solution to the explosion of data that takes old data analytics approaches, and uses machines to augment and scale their effects on larger data sets.
DJ posits that, “the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested.” There is no mention here of a strict definition of data science, nor of a profile that must fit it.
Baseball players used to be judged by how good scouts thought they looked, not how many times they got on base--that was until the Oakland A’s won an all-time league record 20 games in a row with one of the lowest paid rosters in the league. Elections used to swing from party to party with little semblance of predictive accuracy--that was until Nate Silver correctly predicted every electoral vote in the 2012 elections.
Data, and a systematic approach to uncover truths about the world around us, have changed the world.
“More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them,” concludes Patil.
To do data science, you have to be able to find and process large datasets. You’ll often need to understand and use programming, math, and technical communication skills.
Most importantly, you need to have a sense of intellectual curiosity to understand the world through data, and not be deterred easily by obstacles.
You might not think you know anything about data science, but if you’ve ever looked for a Wikipedia table to settle a debate with one of your friends, you were doing a little bit of data science.