A Review of John Hopkins University’s Online Data Science Specialization (Coursera.org)

So for all those loyal subscribers out there (hey mum!) you might wonder what the hell happened to my constant stream of insightful, relevant and handsome blog posts.

Well, I’d have to say you’re thinking about me a little too much – I’d suggest committing yourself to a hobby like me.

Perhaps regular blogging?

Surviving Nay Pyi Taw

In truth, Myanmar has also kept me pretty busy. That is until recently, when I was handed a steaming pile of free time as a result of moving to the traffic-free social desert that is Nay Pi Taw, Myanmar.

And how would any sane person use this time?

Well you’d be best to ask them. As for me, I decided to enroll in a six month dose of data science administered by John Hopkins University (JHU).

So consider this your warning, as that’s where this blog is going.

But to make escape easy here’s a link to YouTube trending and for those of you with a short attention span I’ve also created a TLDR (short) version at the end.

Cyanide and Happiness

Rationalizing Self-Harm

That’s right. I know what you’re thinking – why would anyone volunteer to learn about data?!

Well you see, I was a curious child and time has turned me into a curious man-child, as a result, a surprising amount of my career has been defined by being asked difficult questions by difficult people.

While this has meant that I’ve been able to do a lot of interesting work, it has also made it increasingly apparent that many interesting problems go unsolved because people aren’t sure how to approach data.

Dilbert Comics

Which is where this niche blog post begins, as it was from this observation that I decided it would makes sense to arm myself with a statistical tool that:

  1. Is capable – allowing it to be applied to a range of data-related geekery;
  2. Is portable and cheap – allowing it to be easily adopted regardless of an organization’s size and financial resources;
  3. Can work with data in a variety of formats – making it easier to transport analysis to/from a wide variety of sources; and
  4. Is useful across disciplines – making it suited across fields and in multidisciplinary teams/organizations.

In essence, I was looking for the ‘spork’ of data science software. Which is apt because like a spork, R can do a lot of things but is a little awkward and unwieldy.

However, unlike a spork R is popular.

So popular, that it’s a global standard in the data world. But not so popular that you’re going to get invited to more parties :(.

Which brings me to another disadvantage of R – it’s known for being a little unwieldy:Basic Analysis of Workshop Data

Don’t get me wrong, I’m not claiming your learning experience will see as many deaths as the figure above. But it’s best to go in expecting that learning R is more like walking on lego than cake.

Which is why I chose to do JHU’s Data Science specialization. As if I’m going to be walking on lego I’d prefer to do it quickly and with more decorum than a monkey with a typewriter.

So, the choice was made and a high standard set: Don’t be a monkey.

An Overview

Now for those of you with a short attention span, remember I’ve include a short summary at the end of this post but in essence JHU’s Data Science specialization is made up of nine courses which can be roughly divided up into two main ‘flavors’:

  • The basics of working with R – such as writing scripts, using GitHub, importing/exploring data, and generating statistics; and
  • The actual reason we want to work with R – such as creating interactive visualizations on the web, creating catterplots, running regression models and encouraging your computer to become sentient via machine learning.

Once completing these nine courses students are then provided with the option to complete the final ‘Capstone’ course which is meant to provide an opportunity to apply your skills on a real-world problem.


So in the spirit of [insert closest holiday here] and to spoil the ending, let me just say that completing the specialization was worthwhile. It covers a range of useful topics, is delivered by world-class lecturers and forces you to apply what you learn. The course also fulfilled my embarrassing desire to apply some science to data, which is essentially the only way to learn R, via R-ing (?).

For instance, the quizzes and programming assignments give you messy data, complicated problems and ask you to use R to present analysis in a digestible format. As a result, if you legitimately complete the courses you’ll come out having learned a lot.

Although it’s hard to compare online courses with those offered by a traditional university, I’d probably say that JHU’s Data Science specialization might be something close to a four-course graduate certificate. This is based both on the level it’s pitched at, the workload and the fact the entire specialization took me a little over six months with a background in statistics (although your experience may vary).

It’s also relatively inexpensive when compared with the more traditional alternatives at a little under $300 USD or around five percent of the cost of a comparable program.

This is Fine

Yet all is not well in the world of the JHU Data Science series.

Gunshow comics.

You see, although I’m glad I did the course, it was not without shortcomings.

Firstly, I was originally attracted to the course as it appeared to cover an impressive array of topics. Yet courses were only a month long which meant subjects often had to sacrifice depth and/or place unrealistic learning outcomes on the students. Unfortunately, the JHU Data Science Specialization often chose both by skimming through essential topics then grading students on them.

Take the Statistical Inference course, which tries to quickly illustrate how to respect the rules of the God of numbers and explain why we care about infinity, even when we’re unlikely to get there anytime soon. While interesting, a frolic through discussions in the message boards made it pretty clear that the ‘vomiting equations onto a PowerPoint presentation’ wasn’t a particularly effective teaching approach.

A similar story could also be told of the regression course which gently introduces learners to the concept of linear regression before abruptly lobbing a grenade of generalized-linear models, probits, logits and something to do with a hockey stick.

This I found to be particularly unfortunate as regression analysis is a useful tool for so many types of analysis. It’s also conceptually useful, as it reminds budding statisticians that there isn’t usually a ‘silver bullet’ explanation for what’s driving something and usually your conclusion relies as heavily on statistical assumptions as it does the data.

More generally, when you’re applying statistics in the real-world, abstract concepts aren’t particularly helpful until you’ve internalized them – something I suspect for most mortals would require more time than the course allowed.

The Sound of One-Hand Clapping

War and Peas

Of course whether the course did include other mortals is an open question, with discussion boards mainly filled with generic ‘please mark my assignment’ requests from past sessions of the course. Although this might have been a natural consequence of the field not attracting social superstars (myself excluded of course…), even for a mixed-gender game of dungeons and dragons human-to-human interaction was low.

Relative to other online courses I’ve done, this led to a much poorer learning experience. This was both because you weren’t able to rely on the hive-mind when you had a problem and as it meant you didn’t get the benefit of understanding how others are applying what they learn outside of the course.

Assessment Structure

Given online courses can have thousands of students, quizzes and ‘peer-graded’ assignments tend to be the backbone of the assessment structure in the world of MOOCs. In JHU’s case, online quizzes were typically run each week while peer-grading (where students mark each other) was used for major projects.

For those unfamiliar with ‘peer-grading’ basically you submit your assignment, mark five of your peers and receive a grade based on the most common score given by five students that have marked your submission. Generally, it can work quite well and I’m a big fan – you see how others have approached a problem, get a sense of where you stand relative to your peers and hopefully receive useful feedback to improve your work.

Alas, in the JHU specialization it wasn’t always done well, with much of the feedback I received being minimal. Although I suspect this is in part due to me having attained perfection, I’d also say that this is a result of:

  • The courses being run within a short timeframe – discouraging students from assigning more time and thought to marking;
  • The marking criteria sometimes not providing much scope to differentiate adequate assignments from the exceptional;
  • The age of the course meaning that the internet is now awash with past assignments, making plagiarism easier for the lazy; and
  • The system not encouraging quality feedback – such as by rewarding those that give good feedback by assigning them markers that are likely to give good feedback in return.


The Capstone

Finally, there’s the final project or ‘Capstone’ which was described as “a project drawn from real-world problems and will be conducted with industry, government, and academic partners.”

I of course assume that was a typo as a more apt description was “A project randomly drawn from a real-world problem largely unsuited to the R language, principally unrelated to the other courses in the specialization and unlikely to be useful at any point in the near future.’

In the words of one reviewer “Of all the offerings in the specialization, this one felt like it was thrown together in less than hour.” And while this might seem unfair, this thought definitely crossed my mind as I was cobbling together an interactive predictive text application that will unlikely be useful to anyone unless they’re looking to generate gibberish.

A disappointing end given the effort that was required to get there.

Two-parts contentment. One part complaining.

But again, the specialization wasn’t all bad. Far from it.

For instance, while the regression modelling, machine learning and statistical inference courses could definitely be better structured and longer, my experience is that teaching these topics is harder than learning them. I also imagine this is all the more difficult when you’re teaching a classroom of 100+ whiny nerds.

I’d also say that some of the potentially boring topics were well done.

For instance, although both ‘Getting and Cleaning Data’ and ‘Exploratory Data Analysis’ could have been more tightly focused, I came out of both courses with a much better appreciation of what’s possible. The courses also made me remember why I was doing the course in the first place as it demonstrated why R is so useful.

Finally, while the final lecture for ‘Reproducible Research’ appeared to be from a different subject altogether, the course was one my favorites. This is for one as it explained what the hell the ‘knit’ button in R Studio does, but also as it covered the how/why of making research reproducible in R – something that is rarely achieved in economics.

While at first glance this might appear as a solely academic issue, as an applied economist I can see many times during my career that the tools would have been tremendously useful for naturally building in reproducibility and transparency into my team’s work as it:

  1. Makes collaboration easier;
  2. Allows the analysis to be quickly repeated with new assumptions and/or data; and
  3. It provides a more reliable way of recording what was done for archival purposes.

While this might still sound somewhat abstract, in the world of economic policy it’s not uncommon to be asked to repeat several iterations of politically sensitive analysis in a short-time frame.

Get it right and you can keep your job.

Make a mistake and you might just make history.


So what would I say to someone thinking of making the arduous journey to complete the specialization?

Well, firstly although parts of the specialization were disappointing, it’s a great overall program and I’ve learned much of what I was hoping to. I understand R, have a better sense for when meaningful insights can be gained from data outside of economics and have a better feel for how analysis can be made interactive and accessible to a wider audience.

The course is also a bargain, costing less than five percent of the tuition of a comparable 6-month course at University.


Of course, it also seems that the JHU Data Science specialization has been largely abandoned, with the world of online data science courses becoming more competitive in the meantime, with Harvard, Berkley, Microsoft and the University of Michigan all providing their own data science specializations both in R and Python.

As such, while I’m glad I endured through the 10-course JHU data science extravaganza, if I was going to do it now I’d be inclined to go with one of the competitors.

This is both because I would place my bets on the competing options having learned from the strengths of the JHU course, while dropping its weaknesses. But also, because unless JHU updates their specialization its prestige and its power to signal the recipient’s determination will diminish over time.

Of course, in the world of online learning it doesn’t have to be all or nothing – pick one, pick two or decide to prioritize your social life by picking none of them, whatever you choose it’s a great time to conquer your fear of data.

I’m looking at you Darren.


TLDR Version:

Good course, glad I did it but would recommend checking out the alternatives from Harvard, Berkley, Microsoft and the University of Michigan.

The Good:

  1. Getting and Cleaning Data­­­­­ – Learn how to get data into R and make it useful for analysis.
  2. Exploratory Data Analysis – Make graphs with different plotting systems and be given a brief and unsatisfying crash course on PCA.
  3. Reproducible Research – Learn what the ‘create R markdown’ document option means in Rstudio and the philosophy of reproducible research.
  4. Regression Models – Be gently introduced to linear regression through a series of intuitive lectures before being rushed through the more complicated logistic and poison regression in the final week.
  5. Practical Machine Learning – Learn the basics of machine learning models.
  6. Developing Data Products – Briefly learn about some of the coolest parts of R such as creating interactive dashboards for the web.

The Bad

  1. R Programming – Learn the essentials of R and lose sleep while writing functions needed to complete the assignments. Bonus: Watch a large proportion of the class drop out.
  2. Statistical Inference – Be quickly rushed through essential statistical concepts with insufficient explanation. Bonus: Watch a large proportion of the class drop out.
  3. Capstone Project – Be given minimal instructions about solving a problem which will likely be useful for 0.5 per cent of R users during their career.

The Neutral

  1. The Data Scientist’s Toolbox – Install R, Rstudio and set up a github account.

Day trip to Dala (Yangon, Myanmar)

When I recount my time in the Philippines I often remember how living in the concrete jungle that is Manila felt somewhat claustrophobic. Although this was for a range of reasons, it is perhaps unsurprising given Manila has the highest population density in the world.

In fact, when comparing where I lived then (Manila), with where I live now (Yangon), it is pretty why this is no longer a problem with Manila’s population density 6 times that of Yangon. Consequently it is possible for everybody’s inner-hermit to find some solitude.

Unfortunately, if there were a party of inner hermits, mine would still be the one hiding behind a curtain in the corner. Which is why, he was so excited to hear about my weekend plan: a day trip to somewhere even quieter than Yangon; Dala.

Dala is a township on the outskirts of Yangon, on the south of the Yangon River. Although it is relatively close to the urban hub of Yangon (1.5 km away), it is only accessible by ferry which seems to have made all the difference to how urbanised it is.

Ferry to Dala

The first step to getting across the river required that we meandered to Pansodan Jetty, directly opposite the Strand hotel.

I say meander as the area attached to the jetty terminal also functions like most markets in Yangon, serving the hundreds of locals who commute from Dala to Yangon each day (during the day the ferry leaves every 20 minutes).

Heading past the many stalls towards the Yangon river, eventually you come to the ferry terminal where there will no doubt be a line in the door and ample crowd waiting inside.

Upon arriving, we were pulled aside and pointed into the manager’s office to buy our tickets. Unfortunately, I couldn’t convince him of being a local no matter how convincingly I wore my longyi and spoke broken Burmese. Unfortunately this meant I couldn’t get a 100 kyat local ticket, rather having to pay the tourist price of 4,400 kyats for a return ticket (or 4 USD if you have dollars on you).

And although I was sure to make a point about how outrageous this exorbitant $4 foreigner fee was, it was to no avail. Besides, there really isn’t much to complain about with it actually being a pretty quick and comfortable trip with it taking around 20 minutes and there being an ample number of traders willing to sell you cigarettes, coconut and cowboy hats.

Of course, in Myanmar it pays to be careful so if you decide to purchase a cowboy hat please consult this chart to ensure you live to tell the tale.

Except for the trader selling snacks and cigarettes t’s a pretty standard ferry ride over to Dala

Unfortunately it’s illegal for foreigners to take these boats across.

It’s easy to forget how big the boat is until you see the masses of people exiting the boat.

Arriving in Dala

As you might expect, taking the ferry in itself is a pretty worthwhile in and of itself, albeit a cushy one. Still, it’s a great opportunity to see how day to day commerce takes place with many of those living in Dala, working (or selling their goods) in Yangon (did you know they transport chickens in bundles?!).

Although it seems the ferry is predominately populated with locals, there are apparently enough tourists to foster a generous number of traders and tour guides who operate at the Dala jetty terminal, so prepare be swamped.

Now while when it comes to the town itself, you could walk around yourself I wouldn’t recommend this as everything is quite spread out. Given this, I’d say you’re best to hire a tri-shaw, the going rate which seems to be around 1500 kyats per hour, with a full tour taking around 2 to 3 hrs.

This is of course unless you happen to be me, who may have paid a bit more than as a consequence of the driver telling me that I’m “handsome like a movie star”. My mum was right.


Cruising Around Dala

There are three main sights that tourists typically come to see while in Dala. The Pagoda, Fishing Village and Bamboo Village, however, truth be told it’s a pretty worthwhile experience just for the purposes of seeing just life in Dala, which, as you’d expect, is similar to other rural communities in Myanmar.

Shwe Sayan Pagoda, Dala

Let’s face it. If you’re not seeing a pagoda a day when touring Myanmar, you’re doing something wrong.

Dala is of course no exception, with the township having a surprisingly well maintained pagoda. Although it is seemingly like any other pagoda in Yangon a number of things make it a bit different.

Firstly, there seemed to be around 20 children who hang around the thing during the day, climbing the stupa and mobbing hapless foreigners when the opportunity arises.

Secondly, the colours used have much more variety than typical pagodas in and around Yangon. However, perhaps the most significant difference is that this pagoda includes a now deceased monk who it is said predicted cyclone Naga.


Fisheman Village, Dala

The Fisherman’s village is located along the banks of Dala river. Many of the fishermen who work along the Yangon River live with their families in huts along the shore. Perhaps for me the highlight of this was the fact that they were building and repairing a number of their boats on the shore, a feat all the more impressive to me given that I have trouble cooking oatmeal without setting myself on fire.

Some Final Thoughts

I have to admit I’ve still got a long list of sites to see in Yangon, I think my half day in Dala was without a doubt the best touristy thing I’ve done in Yangon. It’s also an unbelievable convenient way to get out of the concrete jungle for a breather. Although I don’t mean to suggest it’s going to be as relaxing as lying beside the pool, martini in hand, it is a beautiful side of Yangon to see.

Swatting at Magpies

At the outset, I’d like to wish everybody subscribed to my blog a happy new year. I personally am not overly superstitious, but it appears to me that ending a year with ’13’ in it can only be a good thing.

So to celebrate, I am going to post a slightly edited version of the first speech I gave to Toastmasters.Obviously I’ve used a bit of poetic license when giving this one, but they’re both based on true events.


Good evening.

Tonight I’d like to make my introductions to the audience. You see in addition to this being my entry into the humorous speech contest, it is also my first as a member of toastmasters.

My name, is Giles.

Giles Dickenson-Jones to be precise.

And with a name like ‘Giles’ you might think that I know which piece of cutlery to use first during my dinners with the highest echelons of society.You may imagine that I spend my nights smoking a cigar in a leather arm chair, in a brandy-fuelled daze.

You might even imagine that my weekends are packed to the brim with polo, murder mysteries and wine tastings.

However, tonight I’d like to start my time at Toastmasters by making my introduction in a way which illustrates exactly who this new face called ‘Giles’ is.

You see, Giles is the guy who brings cider and wedges to a formal meeting of toastmasters.

What I mean by this is that no matter how hard I might try to be the Giles that you expect.

The awkwardness of the Giles that I am will always prevail.


Now, although as an economist you might assume that I can skate past this claim without a shred of evidence, let me introduce you to exhibit A:

October 1990

Stuarts Point Public School, New South Wales.

Me, a small, not particularly popular child with hair bleached white from the sun.

It’s recess, and although young, I was wise for my age, having already discovered the unmistakable sting of the bull ant, speed of the goanna and roar of the koala.

But until that fateful recess in October, I had not known the peck of the magpie.

Now, for those of you who don’t know, being more than an hour from Sydney means that I can claim an affinity with Animals, not unlike crocodile Dundee or Steve Irwin.

However, even I had not been prepared for the aerial terror of the native Australian magpie.

Nor was I able to hide my fear after my first encounter.

Week after week.

Day after day

Recess after recess

The Magpie sought out my bright white hair, like the target that it was.

So I hatched a plan.

But this wasn’t any plan, it was the playground equivalent of the great escape.

And It required, guts, determination and access to the sport shed.


In the words of Sun Tsu:

If you know the enemy and know yourself you need not fear the results of a hundred battles.

Whether he was talking about magpies is still subject to debate and may never be fully known.

But as a child seeking to become a man, I knew this was the key to victory.

For myself I knew my greatest weakness was my hair.

In fact it was my Achilles heel.

But what was the magpies?

Well, under cover of darkness with access to a library I found out….


Anything solid you could swing above your head.


And so there I was, in the middle of the playground, wearing a comically oversized hockey mask, wildly swinging a metal baseball bat over my head, while the bemused teachers and students looked on.

I can assure you since that day, magpies and I have had an unspoken understanding.

They don’t bother me and I don’t swing inanimate objects at them.


Exhibit B:

I’ve never been the sporty type.

I know what you’re thinking, ‘oh come on Giles, nobody is that good looking by accident’.

But hear me out.


God may have had a plan for my exceptional good looks, but it’s no fault of my own.

You see, throughout my school life, my least popular pastime was always sport.

Although I’m not sure where exactly this came from, it may very well have been from one of my first swimming carnivals.


Now let me set the scene.

There I was, a suitably awkward child of 10, dressed in my standard issue speedo.

As was typical at the Macksville public pool during that time of year, the sun was blaring almost as intensely as the hundreds of children crowding the grandstand.


Fortunately for me, underneath the grandstand there was respite.

So there I was.

Hiding under the stand with my friend, strategically avoiding as much physical activity as possible.


That is, unless it involved trying to escape outside from teachers by squeezing ourselves under the back wall.

Unfortunately, apparently I had a head which was sufficiently larger than my friends.

Large enough, to thwart my escape.


So there I was, ten years old with my head stuck between a slab of cement and a corrugated iron wall.

Hundreds of kids screaming, just loud enough to swamp my whimpers as I attempted to absolve myself of the corrugated iron and concrete prison through force.

But, it was to no avail.


The only choice that remained was to do the unthinkable and bring our Narnia to an end.

So my friend fetched the teacher.


Unfortunately, the adult world’s solution was no more sophisticated than the human equivalent of WD40.

What I mean by this, is that to add insult to injury, the teacher proceeded to pour inexpensive moisturizer on my head in an attempt to ‘slide’ me from the concrete’s clutches.


So there I was, lying in the hot sun, with hundreds of my schoolmates watching me.

My face covered in moisturizer and my eyes filled with tears.


But at this point, I would like to make something clear to you.

This story real…

In fact it’s so real, that all the time this was happening somebody was filming it.


That’s right.

In a time when portable cameras were far from common.

Somebody had the foresight to bring one.

And thank God for that. Otherwise they would have missed what was next:


A fire-crew and the Jaws of Life.

So there I was, lying on my side, dressed in my speedos, head covered in moisturizer with tears in my eyes, a jaws of life, a fire crew, hundreds of my friends watching and somebody was recording it.


This, my friends was the thing of nightmares and perhaps why sports has never been my thing.

But this is very much the guy who brings wedges to a toastmasters meeting.

And this is who I am.

Try as I might to be the Giles you might expect, the real Giles is still swatting at magpies in the playground.

Thank you.

San Francisco

I’ve often been asked by friends ‘Giles, how can you have a phobia of hipsters but be so fond of San Francisco’?

Well that’s a good question. Such a good question as a matter of fact, that I’m going deal with it in the only responsible way: by all together ignoring it. You see, hipsters are people too and the only bartenders who don’t look at me funny when I ask for a cocktail involving pickle brine.

As a result, they’re okay by me. Kind of like bears, they’re probably as afraid of me as I am of them.

Now although I’m not one to claim myself as a scholar of American history, I do know that San Francisco holds a special place in its books, being a metropolitan hub during the California Gold Rush, a stage for large scale immigration, a nexus of the gay rights movement and a focal point for an unfolding wave of liberalism.

But before I discourage my readers by packing paragraphs with more parables than puns, let me assure you, like all my blog posts, this will be targeted towards a readership with a low attention span (just like its author). So much so, that I fully intend to include a whole array of random photos in completely inappropriate places. Like here:

Rectangle frames are too mainstream for San Francisco.

Fortunately, that photo serves as more than just eye candy, it provides a (not so) clever segway to my opening point: San Francisco is cool. So cool in fact, that no matter where I went, I always felt like something was going on that I wasn’t invited to.

Unfortunately for San Francisco, unlike at my neighbour’s parties, there was no fence to keep me out.

Any place which is willing to risk its financial viability for the sake of humour is okay by me.

The Golden Gate Bridge

Now, as my well-travelled and no doubt learned readers know, San Francisco is home of the Golden Gate Bridge, which is kind of like San Francisco’s equivalent of the Statue of Liberty, as it was the first sight for immigrants entering the United States through the bay.

Wikipedia is a better photographer than me…

China Town

Unsurprisingly, San Francisco’s history of immigration played an important role in shaping the area. In fact, as of 2010 San Francisco had the highest share of Chinese-born immigrants in the US, which is perhaps why it is also home to the largest Chinatown outside of Asia.

Now for anyone who has seen my previous travel blogs you’ll realize that I have a thing for immersing myself in markets and whatever other obscure attractions I can find. As a result, I spent a lot of time in Chinatown.

In fact, after spending around 4 hrs walking around in this one, I can assure you it’s impressively large. In fact, in the world of eating random street food and buying solar power waving cats, I’m king.

But SF’s CT almost had me beat, with a seemingly endless supply of toys, balms and disconcertingly food, which I find fascinating. You see generally for there to be a product, there has to be a buyer and understanding who they are and what they might be buying it for interests the hell out of me.

Of course, I already know who purchases fish ice cream, because it’s me. But who is purchasing solar powered plants?

And then there is the random assortment of graffiti:

Wait to ruin a perfectly awesome dragon Banksy!

Of course, I’m not going to be so bold as to claim it to be a major attraction of SF’s CT, but there are some pretty cool pieces of street art around the place. And although typically I’m vehemently against the defacing of dragons, for Banksy I’ll gladly make an exception.

It’s also hard to be mad when faced with the world’s largest LOL Cat.

Also home to the world’s biggest LOL cat.



As you probably also know, I’m a geek.

Typically I’d rather sit in a library writing, than at a pub drinking. In fact even better is being at the library drinking. And while I was lucky enough to be taken on a number of whirlwind tours of bars in the area, they’re not included in this blog because touring Stanford trumped them.

Although it’s hard for me to objectively reflect on why I liked Stanford so much, I dare say it was mostly to do with how magnificent the campus is. You see, although I think it’s pretty cool to be walking around a campus full of nerd, a high nerd density is not sufficient for me to be impressed.

The reason I can attest to this, is that I have also toured Harvard…. although that might have something to do with me being escorted off campus after making too many references to Animal House. 

The Gates Computer Science building.

In any case, I couldn’t help but be impressed by Stanford, partially I suspect as a result of what the buildings at my university typically looked like.

Okay, my university looked nothing like that. We didn’t have walls. But check out this next photo:

It’s a car park.

That’s right, not content with just any old building to park their cars, somebody has constructed what is a Sydney Opera House for cars.

Don’t get me wrong, I didn’t spend ample amounts of time fawning over this thing, but it does make my point pretty directly that the Stanford campus is nothing short of epic, even when they’re just dealing with the temporary storage of cars.

Of course the explanation for this rather extravagant storage of cars is quite simple. You see Stanford is an amazing campus, with smart students and generous benefactors, and in such a place you can’t have your cars slumming it in a ‘car hold‘.

This is particularly because the university is built on such noble origins. Of which, I was lucky enough to be regaled with after ascending the illustrious Hoover Tower:

Hoover Tower.

You see, the founders of Stanford university did so in the memory of their 15 year old son, who died of typhoid in 1884. But as part of the endowment they stipulated that all Stanford roofs must be red, their son’s favorite color, so he could see them from heaven.

The view from Hoover Tower.

Now, maybe it was the fact that I’m a sucker, but I have to admit when I was told this story I shed a tear, which is in my defense is pretty easy when you’re staring down the barrel end of a view like the one above.

But let me assure you it was a manly tear. In fact it was so manly, that it impregnated the ground.

Unfortunately, like many origin stories, outside of Marvel, this one and by extension my whole Stanford experience, was a lie. The roofs are just red because that’s the style, and there is no heaven.

Okay, a tad melodramatic, but it really didn’t make a difference as I didn’t tip the tour guide. Take that, thoughtful stranger!

Overall though, I have to say San Francisco stands out as one of my favorite places outside of Asia.

Which is why in an attempt to get closer to living there I’m already devising a plan to become a billionaire.