Sep 29, 2016

What we learned doing data science on those learning data science

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Learning Data Science

At Springboard, we’ve had data science students who have gone from being a journalist to a data analyst with a Silicon Valley giant, and who have broken into data science at Boeing from a political background. There are so many different backgrounds that it’s become quite a mystery even to our team. What motivates students with such vastly different backgrounds to pursue the same course of learning data science?

What motivates somebody to learn data science? It’s a question that gets at the core of learning itself and the core of what we do as data science instructors. Is it the allure of high average salaries? Or the lure of being able to solve problems with novel approaches? Where do these students come from, and who are they?

Data science is a new field with plenty of exciting applications. Charities have used machine learning techniques to solve complex logistics problems and scale their social impact. Data science techniques have been used to find Banksy, and predict political outcomes. Learning data science has plenty of benefits and plenty of reasons behind it, so we wanted to uncover as many as we could. 

At Springboard, we’ve taught thousands of students data science skills with the help of one-to-one instruction. We dove deep into our own student data to get a handle on exactly what motivates somebody to start learning data science. We expected to find many of our students sponsored by corporate training. We expected to find a significant amount of people looking to skill up in the domain without getting a job right now. We didn’t expect many of our students to be older than 30, given that data science was a cutting edge field.

What we found when we looked at the data, however, often surprised us. We hope it provides as much food for thought for you as it did for us when it challenged many of our hypotheses.

Corporate Training

About 13% of our students have employers paying for their data science training. A selection of companies that have paid for their employees to receive training from us include Dell, Fjord (one of the world’s leading design agencies), Target, ExxonMobil, Udemy and Stanford Healthcare.

We often see companies want to train their employees and help them gain new skills in order to build better returns on their salaries. Corporate spending on employee training has skyrocketed year-on-year with over $70 billion spent in 2014. Surprisingly, this isn’t as large of a motivator as you would think it’d be, at least not for our courses. Over 85% of our students are self-financing their education with us. Perhaps they’re looking for new jobs or looking to gain new skills outside of corporate employment.

While a significant part of our students get support from their companies, many of our students decide to invest in their own learning. Seeing as how Springboard has helped our students increase their median salary by $18,000, we think the investment is well worth it.

Motivation

46% of people are looking for a job right now in either data science or data analytics within 6 months. 22% say they are looking to skill up because learning data analysis or data science would help them at their current jobs.

Overall, 66.4% of our data science students were looking for a career change in the next 2 years. That’s a huge amount of mobility and it implies that many people taking our workshops are in a transitional stage of their careers. It’s become obvious to us that the largest motivation for our students learning data science is breaking into a new job. For every 1000 students we graduate, you should expect close to 700 to explore the labor market within the next couple of years. 

learning data science with Springboard

Demographics

70% of our data science students are American. 4% of our students are Canadian. A grand total of 74% of our students are from North America. The United Kingdom and India follow with the largest amount, with about 3% of our students coming from the UK, and about 2% coming from India.

This means that in a room with ten of our students, Americans would take 7 spaces, more than double the amount of non-Americans.

The following is a pie chart that shows a comparison of just our top 5 countries by student count. As you can see, the United States increased its relative importance here due to the smaller sample size.

learning data science with Springboard 

Data science is global, but those of our users willing to invest in themselves are still by and large in America. This may be due to the anglophone focus and the targeted country-based paid marketing we use to advertise our workshop, but it can also make sense when you look at the areas where you can get the greatest return for your training in data science.

learning data science with Springboard

California has the most students at 28% of our base. Texas and New York both have around 9% each of our student base. Washington and Massachusetts have about 4% each. Many of our students learning data science are concentrated in high population density states with a heavy concentration of world-class university systems, urban centers, and a high number of technology businesses.

learning data science with Springboard

If one flips the dynamic a bit and looks at the effect of population on the number of students, a slightly different story emerges. In fact, we can now see that states like Texas and New York were being held up only due to their large populations. Data science programs like Springboard seem to be the most popular, when you adjust for state populations, in states such as Massachusetts, California, and Colorado, where there are large, prestigious universities (MIT, Harvard, Stanford), and booming startup/technology ecosystems (Silicon Valley, Boston, and Boulder).

Age

Here is the overall age group breakdown of our Springboard students learning data science. You’ll note that the majority of our students are between 24 and 35 years of age (about 58.8% of our students). This roughly correlates to students who are finishing or will have just finished their Master’s degrees or PH.Ds, showing that one of the top times to learn data science is during the same period as one would academically be diving into focused specializations of knowledge.

Springboard Students by Age Group 

learning data science with Springboard

There is also a small cohort of about 9% of our students who are looking to take an early jump on data science and who are pursuing the course in undergrad or perhaps even slightly before!

Finally, a significant percentage of our students are above 36, showing that data science still holds relevance even for professionals that have worked for many years.  

Interests

Here is a selection of our student interests:

Data mining, Data warehousing, SQL,                                                                                
Computer science, data science,  social impact, smart cities                                                        
Plants, Cars, Technology                                                                                             
Psychology, Consumer Behaviour                                                                                       
Video Games, Anime, Reading, Violin                                                                                  
Big Data, Information Technology, Data Analysis, Business, Economics                                                             Programming, Metal music, old books and latest tech                                                                  
History, Third Wave Coffee                                                                                           
Startup, consulting,  high tech, data                                                                                
Statistics, classical music, local history, French

As you can probably tell, Springboard data science students have quite eclectic tastes! What interested us the most about this list of interests was that while there was a bias to data science and data analysis terms, our students also demonstrated a passion for multiple interests that spanned different domains, from languages, music, coffee to botany.

Conclusion

Like any good data scientist, we made some assumptions about people learning data science with us, then set out to explore them with the data. We had some falsifiable hypotheses about their motivations, their demographics, and their interests. While some of those hypotheses were verified, others were contradicted by the data in front of us. It turns out we do have older students, and it turns out that our students have interests way beyond data science, in a surprisingly distributed fashion.

We did this analysis to learn about our students but really what we wanted to do was get at what ticks at the heart of a new education: self-motivated data science learners looking to invest time and money to master cutting-edge fields. What we found confirmed this story to us.

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