Getting a first class education without the luxury price tag: ITunes University

Permit me to paint a picture. You have just finished your undergraduate degree in a business or quantitative discipline (eg, economics, computer science, or mathematics). You finished with first class honours, so you have landed a great job in a medium-sized firm. You are fed up to the teeth with formal study, and are looking to gain some experience in the real world and finally repay your student loans. The time has come for your study to start paying off...or has it?

The problem is that in the modern world there is an arms race for qualifications. As demand for skilled graduates has increased, so have the number of students completing university. But as a result, there are more and more members of the work-force completing advanced degrees. In addition, this has been fueled by the behemoth style corporate factories financing postgraduate education. Masters degrees have become the norm, and no sooner have students left university than they are back, but this time in the evening after work.

I am highly sceptical of these Masters programs, particularly those in my home country of Australia. The entry requirements for these degrees are extremely low, most notably in my own area of applied mathematics. Demand for postgraduate qualifications in mathematical finance is so great that universities have slowly lowered the level of prerequisite knowledge. Many of these students have barely completed first year mathematics, but are placed in classes alongside honours students (who often coach them through the course).

As the quality of candidature drops, so does the quality of the course. Students are so concerned about passing that they have no interest in engaging with the material. Courses are crammed into a single evening, usually a three to four hour block, allowing those who work full-time to attend with minimal inconvenience. Of course, by half way through the second hour no one is paying attention, and everyone is secretly hoping that an impromptu visit from the fire department will put everyone out of their misery.

If you are looking for an extra line on your resume then go right ahead and sign up to one of these programs. But what if you genuinely want to gain further education. I guess you can always consider a PhD. It still represents a real qualification that is going to last you for the duration (we hope!). But if you were squeamish about signing up for a Masters, a PhD is likely to make you throw your rifle into the cornfield and run for the hills. After all, you don't want to become an academic, you just want to ensure that you continue learning.

Until recently the obvious answer was self-education and, for all intents and purposes, it still is. Picking up a few books on your area of expertise (eg, computational economics, operations research, stochastic analysis) and working through them as though you were embarking on a PhD is a reasonable way to simulate the experience. Many of my friends who have stayed at university to complete doctorates have noted that they could do most of their work at home, provided that they were still funded to do so. Of course, the greatest benefits of being enrolled in a formal program stem from interaction with other researchers, and this is hard to replicate on your own. However, there are also advantages to working in industry where you are constantly faced with real world problems that can direct and focus your research.

It is, however, this focus or structure that so often is lacking from self-education. However, with the advent of the internet this too can be achieved without needing to drop everything to go back to university. The solution is now freely available on ITunes.

Truly open universities: ITunesU

The availability of online university courses has exploded in the last year. My first exposure to online courses was through MIT Opencourseware. I watched a series of lectures on Linear Algebra to supplement a course that I was studying at the time. To be honest, the course I was taking was far more in depth and of a far higher quality, but I really benefited from being able to watch a completely different course in its entirety and from the comfort of my own home. What was so revolutionary about MIT Opencourseware was its attempt to allow online users to have the full course experience. Every lecture was posted online, and all class materials were available for download. Its not exactly like being enrolled, but it is about 90% of the way there. The problem was that only a very limited number of courses were available. The 101 course was there but 102 often was not. Darn.

In the last 12 months, however, more and more courses have become available online. Some of the best are offered by Stanford, MIT, and Yale and are all available at the ITunes store in the ITunes University Section (ITunesU). Below is a short list of some of the highlights:

• Convex Optimization (A and B)– Taught by Stephen Boyd – Stanford University
• Financial Markets – Taught by Robert Schiller – Yale University
• Information and Entropy – Taught by Paul Penfield & Seth Lloyd – MIT

Sure, you are not going to be able to replicate a degree course-for-course, but chances are you can cover most of the main subjects and fill in the gaps with self-study by reading through the recommended texts.

Better than the real thing – the multiplier effect

The major advantage of online courses is that they are free and thus available to the financially challenged (or alternatively those who don't want to fork out a hundred grand for a degree). As a consequence of this, these courses can actually be better than the real thing. Pray tell how so, I hear you ask?

I found during my university degree that it was sometimes subjects outside of one's discipline that were the most valuable. In my case, I gained more from a couple of well-chosen courses in computer science than I would have from a year of further mathematics. I like to think of cross-disciplinary study as having a multiplier effect – the military meaning as opposed to the economic term. On the battlefield snipers are often referred to as having a multiplier effect, as the presence of a sniper increases the effectiveness of all other members of the unit. In the same way, a basic training in a neighbouring  discipline can leverage your existing knowledge.

As online courses can be taken without cost, they are custom made for this type of 'field-hopping' (tell me if you come up with a better term). With no program requirements, or restrictions on choices of electives you are free to study what you like. I have found that this improves the learning experience, and provides a truly liberal education.

Of course, you still have to put in the hours, do the tutorial exercises, and gain mastery of the material.  Whatever degree you enroll in, no matter how prestigious, the final responsibility will always rest with you. Sandstone spires and grassy quadrangles do not a genius make. As online courses increase in quality and number, I expect that this simple truth will become evermore evident. Make sure you aren't left behind!

Survival skills for today's analyst

I suffer a little from the age-old affliction of contrarianism. If a software package is used by the majority of the population, I assume it is flawed, highly limited, and its continued use will ultimately result in the downfall of the human race. Conversely, I am always extremely interested in a piece of software that has spread no further than the ivory tower in which it was first conceived.

The most longstanding example of this is my profound preference for the statistical computing language, R, over Microsoft Excel–a program in which I have begrudgingly developed an extremely high level of expertise. As every analyst knows, in the world of statistical software Excel is like McDonald's, Burger King, Pizza Hut, and KFC all rolled into one. It is so prepackaged and devoid of customization, yet so ubiquitous that we cannot do without it. Like the fast-food chains, we loathe Excel because it always produces the same graphs, the same simple statistical analyses. Yet when we find ourselves lost in a strange, unfriendly foreign country we go running back to the grid lines of excel to order a Big Mac. As soon as we enter the jungle, our survival skills are found wanting.

Yes my friends, like it or not, Excel is here to stay although not for lack of alternatives. The fact is that it is the user-friendly nature of this program that has been the key to its success. A friend of mine once put it thus: "Excel has allowed a generation of knowledge workers to survive without being able to program."

In truth, the driving force behind Excel's success is simple: Excel is easy. Oh I know that the die-hards will talk about how it is a superior visual tool, and that spreadsheets allow for increased transparency in financial models. But this argument falls flat on its face when we introduce macros to the equation; if spreadsheets are about transparency then why do we add VBA scripts that the user can neither see nor understand. And if we are happy to use scripts at some level, why on earth do we need to do everything else in a cumbersome visual environment.

Furthermore, the simple ends to which Excel users put their tools is demonstrated by the tiny fraction of users use the (admittedly limited) functionality afforded by VBA. That so many users can get by without loops, functions, or any notion of encapsulation is testament to the primitive uses to which Excel is put: it is just a big button calculator with an autofill feature. Surely there are more skills that we need to survive in the analytical jungle.

Finding an alternative
As I write this, I am sure that I have just alienated the entire community of so called "Power Excel Users". But I am sure that many engineers, economists, and scientists will agree that Excel is too limited to be the only quantitative tool that you have available in your office. The problem is finding software that you can successfully use in an office environment, and that is worth investing the time in learning.

The most important obstacle to overcome is the cost barrier. One of my friends, Rex, works for a major insurance group in their risk division. As far as I can tell, there are few organisations as willing to shell out money on analytical software as an insurance company. As a result, Rex regularly tells me about the wonderful software package that they just bought for $X million. These packages are highly customised and very user friendly (that's why they cost big dollars). The problem is, what happens when the company's systems change, or when you need to solve a new problem? Moreover, how does Rex do his job when he no longer has access to the software (ie, if he moves to another job). The cost of these highly customized packages means that they are not useful tools to acquire for your repertoire. As a rule of thumb, if it costs more than the latest version of Excel then assume that it is not portable: you cannot take it with you.

Enter R
Since I am a contrarian, I am sure that my advice should be taken with a grain (if not a barrel) of salt. However, I believe that there is now a viable alternative to Excel: R. R has been around for a long time, but it has taken a while to gain the following that it so rightfully deserves.

R is completely free and thus available at your fingertips wherever you go. No need to negotiate with the boss about breaking the budget for some fancy new piece of software. Download the binary, install it, and you are good to go. The advantage of this is not just that it is freely available, but that you can rely on it being available.

That just leaves its functionality, and my friends the good news is that R has functionality in spades. Take a quick look at its graphical features and you will see that almost any chart or graph you can dream of can be generated in R. In addition, the R community is continually adding new packages with new functions. In the last few years, the development of these packages has exploded in line with growth in the user base.

Transcending Excel and transitioning to R

Having used R for a reasonable amount of time, I find it hard to see why other analysts struggle day-in day-out with Excel. However, the great barrier to using R is that it is one step closer to all-out coding. Run through an interpreter, R seems strange and frightening to the non-programmer. If you have never learned a programming language, then chances are it will take you some time to shift to R.

Another issue is the need for other people to have the ability to review, check, and edit your work. Unless your boss is up to speed with R or is willing for your work to be checked by another R-literate colleague, you may have to stick with Excel for the moment.

There is, however, great scope for the analyst to grow their organisation into R over time. Whenever you are asked to do a self-contained piece of work independently, try doing it in R. I tend to go overboard and try to create advanced graphics that showcase R's capabilities. The majority of the time, people ask how I made the graph and are then keen to see what else R can do.

Into the jungle
As the old saying goes, "to the man that has only a hammer, every problem looks like a nail". At the moment, there are an awful lot of organisations that are filled with people who only have Excel and every problem sure looks like a spreadsheet.

I believe that analysts that fail to expand their toolkit tend to lose the ability to solve new problems. The generation of knowledge workers who are now in their 40s may have been lucky enough to survive on nothing more than their spreadsheet skills. However, as a twenty-something making my way in the business world, I cannot see how an analyst will be able to survive without some high-powered programming in their utility belt. R may not be enough on its own, but it seems like a good starting point.

Good luck in the analytical jungle.