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.

Burtch Works' Best Advice on the Interview Process, Consulting Jobs, MBA's, and More

In the thousands of conversations that Burtch Works’ team of specialized recruiters have had with analytics and marketing research professionals, we’ve answered many questions about the recruiting process, career paths, and the hiring market. For this blog post, I asked our recruiters about the most common questions they answer on a daily basis, and how they advise their candidates.

The Interview Process

Q: How long does the whole interview process normally take?

BW: Once a company has expressed interest in your resume, the interview process will involve one or two phone interviews, an in-person meeting (or two), and any follow up activity (i.e. background, reference, or credit checks), so 4-8 weeks is pretty standard. 

There are, of course, some extreme cases: if a company has an urgent need they may make an offer very quickly, or on the other end of the spectrum, a very bureaucratic organization may take two months or even longer to make a hiring decision. Especially around vacation/holiday times, coordinating around multiple travel schedules can be an additional challenge when trying to arrange interviews.

Bottom line: Expect the process to take a little over a month, but it’s important to be flexible and patient. Check out our series of whitepapers which has great tips for professionals at every step of the interview process.

Consulting Jobs

Q: I hear consulting jobs pay well, what skills would I need to be a consultant?

BW: Consulting positions do tend to pay well in marketing research and analytics (to see how salaries break down in every industry check out our Burtch Works studies). The industry is seeing a lot of growth right now, and for the professional with great client-facing skills who doesn’t mind a rigorous travel schedule, there are a lot of great opportunities out there. I wrote a blog piece not too long ago discussing some of the other traits I typically see in successful consultants.

Bottom line: The consulting industry is growing and pays well, but make sure your communication skills are up to snuff and that your lifestyle is well-suited to a heavy travel schedule.

Pursuing an MBA

Q: Do I need to go back to school and pursue an MBA if I already have a quantitative degree?

BW: Although it never hurts to further your education, we generally don’t see employers specifically looking for candidates with a business degree in addition to a quantitative Master’s degree (or MMR for marketing research professionals). For certain leadership positions, employers may favor a candidate who can demonstrate their business knowledge and develop a strategic approach over a candidate who does not, but you may be able to develop those skills in leadership roles at work, not just from a business degree.

Bottom line: Developing business acumen might not necessarily mean pursuing an additional degree, if you can develop an understanding of important business concepts on your own.

Marketing Research Hiring Market

Q: What about for us marketing research folk, how’s the market looking?

BW: Our marketing research team wrote a great piece for American Marketing Association Magazine that offers an in-depth view of changes in the hiring market for marketing researchers. I highly recommend it if you want to stay aware of shifts in the landscape that may affect your career.

Bottom line: Lots of client-side opportunities are available, with more supplier side openings in the past six months. Most roles are Manager to Senior Manager level, with increasingly more Director-level and above.

Interested in more tips? Check out our new slideshares with career tips, hiring advice, and salary info, and download our studies or view all of our webinar recordings for free on our website.

Want the latest career news and research delivered straight to your news feed? Then be sure to follow us on LinkedIn, or follow @BurtchWorks on Twitter!

Burtch Works' Top Career Tips for Analytics & Marketing Research Professionals from Linda Burtch

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!

Job Market Explodes For Quantitative Students

With the market for quantitative candidates continuing to gain momentum, Burtch Works spoke with Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University - and friend of Burtch Works - about the job market for quantitative students, the  beginnings of her MS in Applied Statistics program, and her thoughts on SAS vs. R.

Burtch Works: Tell me about the makeup of your class, do you find that many of your students are coming straight from undergrad or mid-career and retraining?

Jennifer Priestley: I would say 40% of our students come in direct from undergrad; the other 60% are coming in with work experience, and are doing a mid-career change. What’s fascinating to me is that we’re getting increasing numbers of MBA’s. For a long time, an MBA would suffice, and it almost didn’t matter where it came from, but those days are over. Maybe from the top five programs they still matter, but for the most part an MBA is a way to round out qualifications. It would be naïve to think that an undergrad degree in business and an MBA will get you somewhere.

BW: I’ve heard similar things from other programs about the influx of MBA students. Do you get many international students in your program?

JP: Some, but not as many as I’ve heard in other programs. Many of the students are first generation college students, so upon taking their first course here they will be the most educated in their family. Our students are motivated, driven, and hungry for success. We have a 0% unemployment rate, and when these undergraduate students are placed into white collar, professional jobs making $50,000 a year, they’ll be making more money than their parents. That’s what inspires me to do this, because you’ve fundamentally changed their path and lineage. They may start their education at X County Comprehensive High School, where only 3% of students even go to college at all, and by the time they graduate our program they’re working as a business analyst at Equifax. That’s a huge step.

BW: Can you elaborate on how the Master’s program came about? When did it start, and how did you choose what to include in the program?

JP: It launched in 2006. When we started, we looked hard at what other universities in the regional footprint were doing. We didn’t want to go head to head with Georgia Tech, University of Georgia, or Emory. We wanted to do something in statistics that didn’t compete with them, since we didn’t want to be competitive - we wanted to be complementary.’ So we started with a blank sheet of paper, and created our program to fill the gap. We wanted to build the program so that our students could graduate, and the next day walk into a Fortune 500 company and add value. The entire curriculum was SAS based, and programming in base SAS. We also stress the importance of learning how to extract, transport, load (ETL) and cleanse your own data. Statisticians don’t have the luxury of being a “data diva”.

Statisticians can’t just create models; they have to do computer science. The days of getting pristine data sets are long gone, so students need to know how to do these things. Basically, we were teaching data science before it was cool. In our program, students spend weeks in some classes just cleaning data before they learn the modeling techniques. We recognized a gap in the current education system, and combined the idea of ETL and cleaning with the mathematics and the statistics.

BW: What are some of the other challenges that your program aims to prepare students for?

JP: A big point in our class is communication skills. Nobody cares what you did if it is so complex that you can’t translate it. If you can’t explain what you did to a marketing major, then you can’t improve the decision making process, and you may need to consider hanging it up. The people who are going to take your stuff and do something with it are on the business side. As you move up the chain, you will have 5 minutes to explain what you’ve found to a c-suite executive who doesn’t care about your incredible skills, they just care about how it will profit the organization and affect the bottom line. You need to be able to convert the results into something meaningful, and if you can’t then it will reflect on you as a professional.

BW: How has the career outlook for your students changed over the past 3-5 years?

Exploded. Completely exploded. Like I said, we have a 0% unemployment rate. Companies will contact me, saying “Can you send us resumes for your students? We’re trying to hire for internships.” The press says there are no internships out there, but we actually don’t have enough students to place. When we launched in 2006, and basically we didn’t know, ‘if we built it would they come’. But, we graduated our first cohort in 2008, and they did well. I would say starting salaries in the $60,000-70,000 range, which is not bad. Now, we place kids in the $90,000 to six-figure range, so an amazing increase in salaries.

Another indication is the number of applicants that come to us. When we started the program we kind of said whomever wants to apply, we’ll let them all in. Now? We have so many applicants that we receive four applications for every slot. We also took the GRE requirements way up. Up until last year they had to be above the median for us to look at the application, and now they must be in the 75^th percentile for us to consider the application. Keep in mind, as I said, we don’t have that many international students.

BW: So I’ve been hearing a lot about SAS Day at KSU, and it seems like a fascinating concept. When did that start, and how has it evolved over the past few years?

JP: It started in 2007. We work very closely with SAS Institute, so the year after we launched the program I got a call from a sales representative for SAS. He does business development for Fortune 500’s like Delta Airlines, Equifax, all companies in the region, and he said to me, “I’m always challenged with, once they get a multi-million dollar infrastructure with SAS, the next question is always: How do I find people to run it?” It’s easy to find someone with 5+ years of SAS experience for $200,000. What’s harder is finding someone with 2-3 years experience for $50,000-100,000. He said, “You have a natural pool of talent, and we have a built-in demand from companies.”

It eventually evolved into what we have now, where the first half of the event is the student poster competition, with students creating a poster for a SAS-based project, and the alumni come back and judge. We probably had 70 posters last year. We look for the perfect intersection of correct math and an elegant, efficient, and visually appealing presentation. Students set up a booth, and companies can visit the students and drop off their business cards. It’s basically a reverse career fair.

BW: What kind of companies go to SAS Day, and how many of them?

JP: Of the 200 or so people who came last year, I’d say there were about 50-70 different companies represented. Companies like Autotrader, Deloitte, Blue Cross Blue Shield, Equifax, KPMG, Coca-Cola, The Southern Company, Maxum Insurance, Teradata, CarMax, State Farm, Aspen Consulting, Midtown Consulting Group, Slalom Consulting, and AT&T.

BW: Do you have any examples of other schools that do this? Is this a common thing?

JP: SAS had said they wanted to partner with other schools, and I’ve heard of similar events at Texas A&M, UConn, and University of Illinois. I know University of Alabama has something similar, like a Data Analytics Week that’s aligned with the business school.

BW: What do you think of the popular debate about SAS vs. R?

JP: It’s not even a debate. We don’t see SAS and R as competition. They’re complimentary, and a lot of companies use both. Although we partner with SAS, we still teach both programs. I’d say we’re 70-80% SAS dominant, but we teach R also, and we encourage our students to know both. That’s our philosophy. We’ve actually had an R Day also for 2 years now; the next one will be in the fall.

I would say the results of your survey, 65% SAS and 35% R, is a perfect example of what we use.

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.

Your Webinar Questions Answered Part 1

As many of you know, here at Burtch Works we have been hard at work on The Burtch Works Study - a comprehensive look at the salaries of Big Data professionals. Indicative of the previous scarcity of this information, it has been downloaded over 850 times to date. About a month ago I presented the study via webinar and I am so glad that so many of you could attend! In case you missed it, both the study and the webinar are available for on our website.

 Due to the volume of questions (we had 750 registrants) I was unable to answer them all during the Q&A session so I wanted to share a few more insights on some of the other questions I received. I will be sharing these in two parts, so make sure to check back to see the rest of them posted next week.

1.) What would you recommend for a recent graduate who is hoping to become a Big Data professional? What are the most important skills to have?

LB: Securing a statistics or mathematics degree is crucial to becoming a Big Data professional. Since employers know you won’t have a lot of work place experience try to get as much experience with real, messy data sets as you can. Internships offer a great chance to test your skills and can also offer great references on your working capabilities.  They can help you figure out what you like! Some computer and government agencies have started to open their data stores, which is a great opportunity for students or beginners to practice.  Kaggle offers the chance to compete at solving challenges with real-life data sets, some of them specifically aimed at entry-level job candidates. The more rigorous your quantitative training, the better prepared you will be for the challenges ahead.

2.)  Considering the nature of the job, I think skills should play a greater role than X years workplace experience. How are assessments made regarding skill sets?

LB:  Skill sets are certainly important in the hiring consideration. Savvy with analytics and Big Data tools such as SAS, R, Hadoop etc. is continuing to be important for analytics professionals at all levels. For junior level candidates you must be able to code and tackle big challenges with these tools. For senior level professionals it is important that you maintain a strong knowledge of these tools so that you can not only mentor your team, but jump in when deadlines are tight (which is becoming increasingly true). There is no magic formula for hiring, but I agree that depth of knowledge is important and discounting a candidate solely based on the years experience criteria is a misguided approach.

3.)  I am not sure this is accurate that the bulk of the data scientist talent pool is in west coast. Boston is a huge incubator for Data Scientists.

LB:  There is definitely a large pool of what I would call “Data Scientists” working on the west coast with firms who have access to continuous streams of data. You’re right though, that there are other pockets of professionals in other regions including Boston. Firms in Boston tend to focus more on science, insurance and healthcare related industries. Data scientists have been cropping up everywhere – such as Chicago, New York, Dallas, Minneapolis – since the need for them is no longer limited to Silicon Valley.

4.)  I would love to know if you have a general cost associated with sponsoring a candidate that needs a Visa transfer. I routinely ask our legal team but they resist sharing the expense with me.  It's difficult for me fight for a candidate that is worth the investment when I don't know what the investment is. And certainly my own expectations of a candidate would also be very different if the cost is $2k versus $15k.

LB:  From what I’ve been told, the ballpark cost of sponsoring a visa is between $6k and $10k. I know it's not cheap ($2k) nor extremely expensive ($25k+).  I covered some information about the OPT/H-1B process in previous a blog post as well as more about the green card process in another blog post. For more about the residency status of quantitative analytics professionals, see this blog post.

5.) Why does the Retail industry pay so low for IC level 2?

LB:  It's an interesting question, and I'm not sure that I have an exact answer. This trend holds, though, not only in our study, but also in my experience recruiting for retailers. Generally, retail as an industry is notorious for being extremely tight with expenses due to the very small profit margins. For analytics in particular however, this inclination may be hurting retailers who are trying to compete for Big Data professionals with more competitive tech firms like Amazon and Netflix.

6.)   Did you find that salary is related to the name of the university as well? If you graduated from a top 10 graduate school will your salary be higher?

LB:  Not necessarily. Although a degree from a big-name school may boost your salary right after you graduate, the effect diminishes over time as your career success becomes the most important indicator for how your company should compensate you. Also if you didn’t graduate from a top school but were successful at a rigorous, quantitative internship that can definitely tip the scales in your favor!

7.) You mentioned that the higher salaries in the Northeast and West Coast don't come close to covering the higher cost of living there.  When candidates take new positions and move to these regions are they accepting small pay increases or increases that will cover the higher cost of living? Thank you!

LB:  In my experience, we see an average salary increase of 14% across the US and sometimes just above that for individuals in the Northeast and West Coast. We just rarely see a substantial increase, even though quant professionals are often moving from an area of lower to higher cost of living. However Big Data professionals will each have their own ideals when it comes to industry, work environment, compensation and degree of challenge at their job. Money is not the only factor to consider when evaluating a career move.

8.)  Is it fair to draw the connection between job descriptions (going down from Data Scientist to Insights Manager) and the levels of IC 1-3 and Mgt 1-3? i.e., are they linked closely enough to assume Data Scientist is IC Level 1?

LB: Very good point! That is why we kept Data Scientists and Market Research individuals i.e. Insights Managers out of the salary pool because they tend to be substantially different than the quantitative professionals. This helped achieve a consistency in results across all levels.

9.)  Since the Big Data field is relatively new, how are salaries bench-marked to know what is the right salary to expect for a role? Have your salary survey results been compared to Information Week's annual survey of IT pros or with the self reported numbers on glassdoor.com?

LB:  Salary surveys are common in other areas like IT (with reports readily available) but analytics professionals are very different therefore it would be inaccurate to directly compare the two. Glassdoor is also a good resource if you're interested in self-reported salaries from people working at specific companies.

Remembering an Inspirational Teacher

As most people in the quantitative job field would agree, math education in the United States has been mediocre at best over the last few years. Earlier this month, John Holdren, the Director of the White House Office of Science and Technology Policy (OSTP) reiterated the steps that the Obama administration is taking to improve test scores in math and science and further detailed plans to set aside money to overhaul math education.  My pessimistic side can’t help but think this is too little too late, but at least the President is acknowledging that we have a problem that must be addressed. I have made my opinion well known about this issue and with three teenagers at home, the topic is one that affects me not only as a recruiter but as a mother.  

On a personal note, I am filled with excitement and pride as I watch my two oldest children, Jay and Becky, graduate high school this year and embark on a new journey at college. As parents, we have to step aside in moments like these and hope that our children have made the right choices and will choose the best paths for their futures. Looking back on the opportunities that they have had in school, I am most struck by the fantastic efforts of Sandy McDermott who taught math to both Jay and Becky in middle school. Her life was sadly cut short in 2008 and a scholarship was set up in her memory that asks students what math education means to them. As I read my son’s essay honoring Ms. McDermott, there isn’t a doubt in my mind that if there were more teachers like her in the world instilling a passion for math from an early age, we wouldn’t have the problems in our education system that plague so many students and schools.

Ms. McDermott was such a lovely woman and inspirational teacher to hundreds of children and I feel humbled to share just a glimpse of the impact she had on her students with my son’s essay.

When I was a fifth grade student at Dewey Elementary School, I took an algebra course taught by Ms. McDermott at Nichols Middle School.  I had always enjoyed math, but this course was different.  For the first time, I had a hard-cover math text book.  More importantly, I learned to use variables and no longer just did arithmetic with numbers.  I learned to apply math to solve problems.

I remember Ms. McDermott’s response to a question I heard in that class and every other math class I have taken since: “When am I ever going to use this stuff?”  Ms. McDermott’s response was, “Whenever you want to.”

Her response provoked some kids to say, “So you mean never?” or “Okay, whatever.”  However, I got her point.  I silently finished her sentence: “Whenever you want to . . .” to solve a problem.  There are many problems that might seem impossible to solve at first, but if you see the math in the problem – if you turn it into a math problem – it is easy to get to an answer.

I have seen examples of this lesson learned from Ms. McDermott again and again in many classes, not just my math classes, but also other classes, such as physics and economics.  I also apply math to solve problems at home.  My hobby is to write video game programs using languages such as Visual Basic and Java, and I use math to write programs that are efficient.

Because I enjoy using math to solve problems, I am planning to study applied math and computer science in college (at the University of Washington) and then to make it my job.  Thank you, Ms. McDermott.  For me, “whenever you want to” will be every day.

American Math Education: Worse Than We Think?

As a recruiter of quantitative talent I’m always interested to hear how math education in the United States compares to other countries. As a mother of three high school kids, I’m personally invested in the state of public school education and how our children can compete internationally. This is why I found Arthur Levine’s recent article in the Wall Street Journal particularly troubling.

Mr. Levine points out the obvious truth that most Americans are aware of: the United States is mediocre at best in math education. Every year recent grads are competing with the best in the world and they are unfortunately ill-prepared. If we don’t focus on fixing this problem now, it will only get worse. Math education has fallen to the wayside and apart from a new statistic coming out every few months to support what we already know, it doesn’t look like the government is making any effort to make a substantial change.

What Mr. Levine points out though is that there’s another form of competition within the US as students in urban and inner city schools compete with their counterparts in the suburbs. Shockingly, he specifically points to Evanston, IL and Scarsdale, NY as exceptions to general low performance in American schools. Mr. Levine writes that students in Evanston outperform students in Finland and Singapore, the top school systems in the world.

My children are currently enrolled in Evanston Township High School, and while I’d love to believe they are part of a competitive, high performing system, I was skeptical. Based on experience this did not ring true, and sure enough the facts prove otherwise. According to the Prairie State Achievement Exam, only 42% of student at ETHS met federal education standards in math. This is clearly substantially lower than the 75% proficiency rate in Shanghai and even 50% in Canada.

My children are fortunately able to partake in ETHS’s high level math program. As frustrating as the national statistics are, the students in this program are outstanding, motivated, and sure to succeed. I can’t speak highly enough of the fantastic teachers who devote so much to math education and inspire their students.

I wish there were more programs like this available to more students nationwide. I’m sorry to say that the overall picture, though is bleaker than Mr. Levine paints it. If even these so-called “affluent suburbs” are not up to par with the international community, something has to change.  

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.