Becoming a Data Scientist: Master’s Program, Bootcamp, or MOOCs?

This post is contributed by Irmak Sirer, a partner and data scientist at Datascope, where he solves business problems with data by designing analyses and interfaces. Irmak has helped companies across industries solve problems with data, from small companies to members of the Fortune 50. Working with Metis, Irmak is an instructor for their Data Science Bootcamp program which will be starting on September 2nd in New York City. You can read the full version of this post on the Datascope website.

One of the most frequent questions we hear, right behind “so, what exactly is a data scientist” or “what makes a great data scientist”, is “how do I become one? I should probably just get a Master’s, right?” Perhaps not anymore; rising costs, changing demand, and the Internet are disrupting this traditional path and providing two viable alternatives. At one extreme, self-learning through Massive Open Online Courses (MOOCs) give access to courses at an extremely low cost (often free), but leave it “as an exercise for the reader” to identify a suitable set of courses and tools to round out a coherent skillset. Bootcamps offer a middle ground where students can pay for a structured learning environment at a far more affordable rate compared with obtaining a Master’s Degree. So, “which path do I take?”

We think the answer to that question largely depends on the student. In some cases a student will prefer attending a bootcamp whereas in other cases a student will prefer receiving a Master’s at a university or taking university courses online through MOOCs.

Here at Datascope we see great benefits from the bootcamp format, so when Metis (a part of Kaplan) contacted us about partnering to design a data science bootcamp, we jumped at the opportunity. We thought we could take all these points we see as the advantages of the format, and elevate them as much as we could. So, we designed a course that would give aspiring data scientists a lot of experience with 4-5 projects, and a guided route of several core data science concepts and approaches. Participants can quickly build the necessary foundation without the burden of teaching herself everything or paying the handsome price of a Master’s program before realizing her dream job. If you’re interested, our Data Science Bootcamp program is starting on September 2 in New York (applications due by August 11), and you can learn more about it here.

Since there are many things to consider when choosing which program works best for you, in a separate post, we do a thought experiment to compare the three experiences for a fictitious aspiring data scientist named Audrey. For the sake of brevity, the following table summarizes our thinking about what each of these experiences is like and, more importantly, who they are ideally suited for.

	Masters	Self-taught (MOOCs)	Bootcamp
Learning	Theory-rich learning	Self-guided learning	Experiential learning
Teachers	Live university faculty professors	Recorded university faculty professors	Practicing data scientists
Outcome	Diploma	Certificate	Portfolio of projects
Duration	9 - 20 months	6 - 18 months (part-time)	2 - 3 months
Tuition	$20,000 - $70,000	$0 - $500	$0 - $14,000
Networking	1.5 years of social networking	Isolated; no in-person networking	Collaborative networking
Projects	Internship + practicum projects	Projects on own time	Projects built in to experience
Job hunt	University-wide recruiting day	Self-driven job search	Hiring day organized by bootcamp; talent placement manager helps with hunt
Ideal for	People that enjoy immersing themselves in campus life and want to take time to let the new material absorb while learning in a structured environment with the full credentials of a University degree.	People that thrive with ambiguity and self-guided environments and are motivated enough to design their own curriculum around their own strengths and weaknesses.	People that want to switch or accelerate careers ASAP and want to have confidence that the switch will result in a job they will like while learning in a structured environment.

As technology increases the rate of change of society, the most successful workers will be those that can quickly shift to new specialties and learn on the job to meet market demands. In our opinion, the bootcamp format provides the benefits of personalization, credentialing, and social learning that a Master’s degree offers, but at an accelerated rate with experiential learning. Sure it is more expensive than being self-taught, but the connection with employers and the guided, experiential learning process increases your confidence to tackle the uncertain prospect of making a career switch.

To become a data scientist, you don’t need to have postgraduate degrees, or 20 years experience, or be proficient with every data-related technique and tool under the sun. What you need is to have enough baseline knowledge and experience, and the skill to constantly adapt and learn. Bootcamps, in our opinion, are the perfect medium for making the transition.

Tell Your Kids to be Data Scientists – Not Doctors

Recently I had the pleasure of being interviewed by John Phillips at CNBC about our data scientist salary study. His article, Why Your Kids Will Want to be Data Scientists, was published at the end of May, and in it he raised a very interesting point:

“According to Burtch Works’ 2014 study of salaries for data scientists… those responsible for a team of 1-3 earn [a median salary of] $140,000 and those responsible for a team of 10 or more earn $232,500.

By contrast, the mean average annual income for a lawyer in America was $131,990 in 2013, while doctors earned $183,940, according to data from the U.S. Bureau of Labor Statistics.”

Did you hear that? Data scientists earning more than doctors! For complete salary information for data scientists, Big Data professionals and market research professionals, download the full reports for free here. Salary is not the only reason however, that I would recommend encouraging your children to pursue statistics and coding over going to medical school.

How Data Scientists Are Supplementing Doctors

There are big changes happening in healthcare right now, and the implementation of EHR (electronic health records) in particular is a great example of how data scientists will be working with doctors in the future. The move to EHR is picking up steam, and the Center for Disease Control reports that 78% of office-based doctors are using EHR as of 2013, with that number only expected to grow as practices will face penalties for non-compliance. All of these electronic patient records spell out Big Data for the healthcare fields, and data scientists - like all quantitative folks - love data. These medical data could not only offer tremendous insights that change the face of modern medicine, but also offer rewarding opportunities to the data scientists who must decipher the data.

Patient care also stands to receive enormous benefits from data science. Venture capitalist Vinod Khosla was recently quoted at the Standford University School of Medicine’s Big Data in Biomedicine conference saying, “Humans are not good when 500 variables affect a disease. We are guided too much by opinions, not by statistical science.” While a doctor may be trained to look for many factors when diagnosing an ailment, some of these diseases are impossibly complex, and patients could stand to gain faster, safer treatment if left in the hands of a well-developed machine, or even a physician aided by one. For example, IBM’s Watson is already collaborating with Memorial Sloan Kettering Cancer Center to help doctors make better cancer treatment choices. The human interaction between patient and physician will continue to be important, but data scientists will have a measurable impact on the future of healthcare.

Career of the Future

One of my predictions for the analytics hiring market this year was that data scientists would be embedded in analytics groups, and with the internet of things, the increase in wearables, social media sentiment analysis and many more applications for data science, it’s no wonder this career has so much buzz around it. With the increase in demand, shortage of talent, high salaries and applications in every industry, data science is becoming a good option for career success. The road to that success begins with a strong early foundation in math, and (perhaps) some nudging from the parents. I can’t tell you how excited I was when my daughter, Becky (who started college this year at Macalester), changed her major to math, as I believe strongly that this is a career path that offers a bright future. Who knows, maybe one day I’ll have a data scientist in the family!

How to Get Your First Analytics Job

Over the past few months Burtch Works’ entry-level recruiting specialists Erin Craig and Erinn Tobin have been visiting colleges and universities to meet with students who are preparing to enter the field of analytics. After securing a degree in statistics, mathematics or other related fields, the next challenge for many students is their job search. This will be the first job search for many of them and I wanted to give Erin and Erinn an opportunity to share some of their most helpful tips for students. Since they receive a lot of questions from students on their campus visits, I will also be inviting them to post their answers on my blog as guest contributors in the coming months.

Burtch Works’ Top Tips for Entry-Level Candidates 

1. Utilize LinkedIn – Over 90% of corporate and independent recruiters who recruit using social media use LinkedIn. It is fast becoming the go-to resource for companies to check your references and resume, as well as a resource for job seekers to stay updated on company news, search for job postings and develop their network. Having an updated, professional profile on the site allows companies with whom you are applying or interviewing to see you as a person they might want to hire, not just another anonymous resume.

2. Complete an Internship – A great way to test your skills, continue learning and expand your network is to complete an internship. Without previous work experience to go on, prospective employers will look at internships (as well as coursework) to determine if you might be a good fit for their organization. Sometimes - if a company is looking to hire full time and you demonstrate an exceptional work ethic- an internship may also lead to a job offer.

3. Get Your Hands On Messy Data – One of the biggest challenges students will face in their first analytics job is the lack of experience they have with real-world data sets, so in addition to completing an internship your strategy to enhance your resume must include working with unstructured data. Two great online resources we would recommend are Coursera and Kaggle: Coursera is an MOOC (Massive Online Open Curriculum) where you can take free courses to further your education and Kaggle hosts data science competitions where you can not only test your abilities against other members, but also get access to large, unstructured data sets more similar to the ones you might use at an analytics job. Completing your SAS certification can also add credibility to your analytic skills and as many companies adopt other tools - such as R, Python, SQL, etc. – you will have a significant advantage if you diversify your skill set.

4. Leverage a Recruiter – Developing a relationship with a recruiter early in your career has many advantages: companies will often have open positions that they fill by working with recruiters (not by posting them on job boards), your resume will be seen by a hiring manager instead of disappearing into a pool of other resumes in their online tracking system, and it lends a more personal experience to what can be a very daunting hiring process.

Burtch Works sends out monthly emails to students that cover all the topics that, in our experience as quantitative recruiters, can help you prepare for the road ahead. Want to learn more about the interview process, how to get high-quality references and what you can expect at your first job? Don’t miss out! Contact Erin Craig ecraig@burtchworks.com to receive more expert advice from our recruiters and be on the invitation list for our career webinars. Check back soon, when Erin and Erinn will tackle some of your job-search questions.

The Case for Saving Algebra

In his op-ed piece “Is Algebra Necessary?” Andrew Hacker struggles to justify the teaching of traditional advanced mathematics in our public schools. I’ll admit that the statistics he provides are jarring: community colleges across the country report that fewer than 25% of new students passed their required algebra classes; at The City University of New York, 57% of students fail the mandatory algebra course. High school numbers are more shocking with millions of students from several states falling well below the level of “proficient” in mathematics.

Clearly we have a problem and it’s one that might be avoided by simply eliminating algebra and trigonometry from the curriculum, but does that really solve anything? Hacker argues that advanced math is irrelevant to most career paths. While that may be true, taking algebra out of the equation doesn’t make the fact that millions of children are frustrated and intimidated by math any less of an issue.

I think we owe something to these students. Hacker points out that less than one percent of bachelor’s degrees awarded each year are in mathematics. While he uses this as a way to support his claim that advanced math has become unnecessary, I see this as an argument to the contrary: now, more than ever, we need to encourage students to appreciate math and help them excel in it.

College is obviously too late to start. High school is not soon enough. This is one point that I think Hacker and I are in agreement on. As early as kindergarten children should be exposed to math and as they grow older and hone their analytical skills, math should evolve with them.  Many kids can’t succeed in advanced math because their early foundation in math fundamentals is so weak. It isn’t any surprise that by the time they have to take college placement exams the results are what we see today. The real solution is to start early.

To do this we need passionate educators. We need teachers that make math fun and treat it like a puzzle.  Unfortunately, many elementary school teachers are also less than enthusiastic about math. They may not be confident in their own skills and whether intentionally or not, it’s passed on to the students. It only makes sense that math be taught by a math specialist at all levels, not just in secondary education.

In 2010 Hacker writes that 15,396 bachelor’s degrees in mathematics were awarded. Let’s see if we can get some passionate teachers from that group.