# How to Get a Job That You Don’t Hate

First of all, if you have not seen the movie Office Space, stop reading this blog post and watch it. I will wait.

Welcome back.

I am often asked to give career advice. This is strange since I don’t think I ever had a career. I had jobs, some terrible, some pretty good ones. I started a company in 2010. I am starting another one now. But a career, never. OK, so with that out of the way …

Earlier this month I was invited to discuss job search strategies with students in the MA in Statistics program at Columbia University. After the discussion, I posted the following blurb on my Facebook page.

Talking about careers in data analysis and stats with students in the MA in Statistics program at Columbia. My key messages: 1) if you want to work for banks, make sure you know what you are getting into; 2) think of an interview as a two way street: they interview you, you interview them; 3) if you hate your job, quit (if you can) and don’t worry about what it would look like on your resume; 4) don’t apply online, get a referral, go to meet ups, etc.; 5) learn some Bayesian stats — you will be a better human and know more than most of your peers.

I thought it would be useful to people if I elaborated on these a bit so here it goes.

## If you want to work for banks, make sure you know what you are getting into

A lot of students in the MA in Stats program want to work for banks. I am not sure why that is but it must have something do with the geography and expectations of high earnings. Whatever the motivation, it is a good idea to know what you are getting into. Not everyone hates working for banks, but in my experience, technical people who end up working there are not very happy. I think they find that the culture does not agree with them very much. My advice is always to ask to speak with your potential future peers and ask them, the future peers, about three things they love and three things they hate about their work. You would be surprised what you will learn. Having said that, I have met people on the “business” side of banks that absolutely love it. Like with anything else do your research and make your decisions based on conditional probabilities, not population averages.

## Think of an interview as a two-way street: they interview you, you interview them

This should be obvious, but most people don’t do it. The thing to recognize is that there is an inherent risk asymmetry between you and your prospective employer. You are just one candidate or employee out of many. They can make a mistake with you and they would probably be ok, but you are about to commit several years of your life to them (in expectation) and so you should be the one doing the interviewing! Of course, the realities of the sparse labor market is such that usually, you need them more than they need you, and so the roles are flipped. This fact, although daunting, should not deter you.

You want to find out what it would be like spending most of your waking hours at a job you do not yet have. This is not easy. To get started, make a simple two-category list: 1) culture; and 2) technical. For example, if you want flexible working hours, put that in the culture column and if you just must program in R, put that in the technical. Once you are done making the list, rank order the items. Do this before you take any interviews. After the interview, try to score the prospective employer along those dimensions. Where is the money column, you ask? That part is easy: know your minimum number and don’t be afraid to let them know what that is … but be reasonable, which means know what the market is paying and where you are on the skill / experience curve.

## If you hate your job, quit if you can and don’t worry about what it would look like on your resume

Some jobs are just plain awful. If you do what I recommended above, you will probably avoid most of those, but every now and again one will creep up on you. What to do? Quit! Sure, this is easier said than done, but at the very least immediately start looking for a new job and make some notes about how you were duped with this one. Introspection is a great tool and I use it often.

A friend of mine spent years working at a company for a horrible boss and even though he eventually quit he still has emotional anxiety over the whole affair. Life is way too short to work for assholes. Get out now. But what about the resume, you ask, and I answer: if you are a technical person, github (or something like that) is your resume.

## Don’t apply online, get a referral, go to meet-ups, and so on, but I am sorry I can’t refer you because I don’t know you

When I was working for a bank we had an opening for a business analyst. Now, here is the thing: business analyst does not analyze the business. What does she do? She writes requirements for a proposed piece of software. Anyway, that’s beside the point. When this job was posted by the HR department we received over 200 resumes! I don’t remember if we hired anyone, but you can imagine your chances of getting such a job. (Well, you can just compute them, but whatever.) The short story is, don’t apply online.

The best jobs I ever got were referred to me by my friends and classmates. Meetups are also some of the best places to get technical jobs. New York Statistical Programming Meetup is a great one for stats people and they often advertise jobs during their events. Another great way is to start contributing to some open source software. Where can you find great open source projects? Github, of course.

But Eric, why can’t you introduce me to some of those friends of yours that have all these great jobs? The truth is that they will not be my friends for much longer if I started doing that and you should not do it either. Your referral is a reflection on you — use it wisely and only introduce people you know well.

## Learn some Bayesian statistics — you will be a better human and know more than most of your peers

When I was getting my MA in Stats at CU, they did not have a masters level Bayesian class. This is a tragedy of modern statistical education, but things are getting better. My friend and co-founder Ben Goodrich is teaching an excellent Bayesian class for masters students in the QMSS program. The stats department also offers the class and Andrew Gelman teaches a PhD level Bayesian course. If you are not at Columbia, Coursera recently started offering Bayesian classes. This one looks particularly interesting.

So why all the hype about Bayes? It’s a long story, but here were my initial views on the subject. I now work exclusively in the Bayesian framework. In short, Bayes keeps me close to the reasons why I fell in love with statistics in the first place — it lets me model the world directly using probability distributions.

Even if you are not a statistician you should learn about conditional probabilities and Bayes rule so you do not commit many common fallacies such as the prosecutor’s fallacy, especially if you are a prosecutor.

## Bonus feature: why do you want a regular job anyway?

Recently I was on the Google hangouts call with a friend of mine who works as a contract programmer. His arrangement with  the company is that he works remotely. For most people remotely means working from home. For him, it means working from anywhere in the world. Right now he lives in a small apartment in Medellín, Columbia. He showed me the view from his window. It looks approximately like this:

To quote Tina Fey: I want to go to there.

The idea that an employer dictates both the hours during which you must work and the location of where the work must be performed is somewhat outdated. Sure, there are lots of jobs out there that legitimately require this kind of commitment, but it is no longer the norm. Take a look at that culture column I mentioned before and see where you stand relative to hours / location flexibility and choose accordingly.

## Note to people seeking H1B visa

A lot of people I speak with are in the US on F1 (student) visa. It is really tragic that the US does not award work visas to foreign graduates, but this is unlikely to change anytime soon. The common misconception is that you need to find a large company to sponsor your H1B (work visa). You do not. Lot’s of small companies can and do sponsor H1s. When I was working for a small startup in San Francisco in the mid-90s, we sponsored several H1Bs for Eastern European immigrants. The key is finding an experienced attorney who processes many applications and ask her for advice. Reputable attorneys will not charge you for the initial consultation.

# Talks, Lectures, and Workshops. What is the Difference?

I am about to go on a mini speaking tour and in preparation I am skimming Scott Berkun’s “Confession of a Public Speaker.” I like this book, but while reading it I realized that I will be giving two different types of “speeches”. Let’s call them talks and workshops, and even though in both cases the subject will be Stan, the audience’s expectations will be different and my presentation must reflect those differences. In particular, Scott’s book is a lot more relevant to talks than workshops.

Most inexperiences speakers assume that the people who come to their talks want to learn something and some people do have that expectation, but those are usually inexperienced consumers of talks. The truth is that it is very unlikely that you will learn something during a talk. Learning is a hard and active process and it is not going to happen by passively absorbing sound and light waves in a reclining position.  The most realistic goals for a talk is to inspire people to learn more about the subject. This is a difficult task for the presenter, but if you want to know how to do it well, I highly recommend Scott’s book.

A workshop is a different animal. As the name suggests, the participants will be working alongside the presenters and in so doing are hoping to come away with enough initial knowledge to jump start their own exploration. People who attend the workshop have already been inspired to learn more and the bar is therefore higher than during a talk. So what are the important attributes of a good workshop?

To think about that, image you are taking a technical class at a University. You are listening to a lecture. Are you at a talk or at a workshop? The listening part gives it away. Most likely you are at an uninspiring talk that should instead be a workshop. In order for the workshop to go well, here is my short of list of requirements:

1. Participants should have the required background at the right level of abstraction
2. If this is a computing workshop, participants already installed and tested the required software
3. Presenters have designed a series of exercises that gradually guide the audience through a set of hands on tasks each illuminating a different part of the subject
4. Participants have a chance to discuss the problem and their solutions with each other and with the instructors
5. There is a mechanism for the immediate feedback that tells the instructors if the majority have mastered the task

As a presenter, I can not control 1 and 2, but I must make it easy for people to assess their level of knowledge and software installation instructions must be clear.

Creating exercises is very time consuming, but I believe necessary for workshop style learning. Time for discussions can be weaved into the exercises and the output of the exercises can be shared with the rest of the class. Which brings me to the feedback mechanism, which is perhaps the most often overlooked aspect of the workshop.

I don’t have much experience with a feedback system during workshops, but I have used live surveys during talks and they work really well. For computing workshops, I would like to experiment with live code editors, where participants have a chance to post their code, their questions, and the error messages to the shared workspace. This would only work for moderate size groups, but I it seems to me that workshops should only be conducted in relatively small groups (say 50 people or fewer).

If you have any pointers on how to make the workshop experience better, feel free to post them in the comments.

# Thoreau: Thoughts on his Indictment and Defense

I never read Walden, not in its entirety anyway. I read most of the first chapter. It was dreadful. I still remember struggling to keep up with the narrative and wondering why is this such a big deal. Overall, I love the message of the simple life, civil disobedience, and living as one with nature. I do not love the apparently hypocritical obsession with seclusion and the disdain for all humanity. But this, of course, is a very shallow view of Thoreau. But then again, I do not have the patience to study him deeply. Fortunately, Kathryn Schultz and Jedediah Purdy do and offer an indictment of the man and somewhat halfhearted defense.

I really enjoyed reading both of these, but perhaps not surprisingly I found the indictment more convincing. The defence goes something like this. Sure, Thoreau was a hypocrite and an asshole, but we should not blame the message for the messenger (i.e. ad hominem or an opposite of blaming the messenger) even though in this case it happens to be the same person. I can get behind this argument. In science and in business there were and surely are lots of arrogant assholes, who nevertheless made important contributions. John Nash, despite a very favorable portrayal in the movie Beautiful Mind (the book is much less flattering), was not a very nice man. Steve Jobs was no sweetheart either. And so on. So, is Thoreau’s message important enough to stand on its own? That I am not qualified to answer, but a contrarian and anti-authoritarian in me wants to believe it that it is.

Thanks to Bryan Lewis for pointing me to these articles on his web page.

In 2012, I wrote a post about how to learn applied statistics without going to grad school. I still think that one does not have to spend a large amount of money to acquire the skills necessary for data analysis. What has changed for me personally is that am finding traditional statistical methods, call them classical or frequentist, or evolved classical based on the Stanford Statistical Learning school or whatever, somewhat unsatisfying.

These generally rely on maximum likelihood estimation (MLE) to generate point estimates and asymptotic properties of estimators to come up with confidence intervals. One of the main issues I have with this approach has nothing to do with MLE versus Bayesian full posterior per se. It has something to do with the fact that the Likelihood function is largely hidden from my view, although there are lots of others issues, some of which I hope to discuss when my understanding sufficiently progresses.  I am getting too comfortable just running glm(), ok, not glm() since there is no regularization there, but say glmnet or Random Forest or even bayesglm in R.  The latter is of course Bayesian, but still a black box.

I am not sure at this point if I am ready to abandon all the mathematical and algorithmic machinery of Lasso, Random Forests, Gradient Boosting Machines, and so on, but I would like to spend more time thinking and expressing models directly rather than running and tuning abstract algorithms. I am also quite certain I don’t want to write my own model fitting, sampling, and optimization procedures.

Since I would like to approach this problem in a Bayesian way, it also means that my goal is to get to the distribution of the parameter vector $\theta$ given data $y$, $p(\theta | y)$, the posterior. In the Bayesian framework, we still work with the likelihood function $p(y | \theta)$, but we are not trying to find some unique set of parameter values for which it is maximum (i.e. under which y are most likely.) Instead we want a complete picture of the uncertainty in our parameters that is supported by the data $y$, our choice of the model (i.e. likelihood, which as Andrew Gelman likes to point out is a form of prior knowledge) and knowledge about the parameters (prior distribution) without relying on asymptotic properties of estimators. In short:

Getting from prior to posterior is hard work unless they happen to be in the same family, which is rarely the case in the wild. The natural question then is where to start. Short of coding everything from scratch, which would be a very long project, even if I knew how to do it, two types of tools are in order: a probabilistic language capable of expressing models, parameters, priors, and their relationships and an MCMC sampler that can get us to the posterior distributions numerically. For a while, the best bet was some flavor of the BUGS language which uses Gibbs. But the state of the art has moved away from Gibbs sampling.  All the cool kids these days are playing with Stan which uses a more efficient, Hamiltonian MCMC with NUTS sampler and supports a broader set of models.

To get a jump start on Stan programming, I recently attended a class on Bayesian Inference with Stan taught by Andrew Gelman, Bob Carpenter, and Daniel Lee (thanks to Jared Lander for organizing the class.) I learned a lot and I hope to continue my exploration into Stan and Bayes.

* Thanks to Bob Carpenter for looking over the draft of this post and providing helpful comments.

# Getting There

After a 20 minute ride to the Jamaica Queens from Penn Station on the LIRR I am at the JFK.  LIRR is really the best way to travel to JFK from the city.  My flight leaves at 10:20 pm and the check-in procedure is quick an painless.  I noticed that Lufthansa clerk did not check if I had a Kazakhstan visa.

It is approximately 7 hours to Frankfurt, one hour layover and another 5.5 hours to Astana.  The flight was smooth and the airline food was surprisingly bearable.  On the flight to Astana three Russian woman were sitting next to me talking about a Government congress that they were either organizing or actively participating in; their enthusiasm for navigating the local bureaucracy was noticeable but alas not infectious. Their command of the English language was impressive.

We landed in Astana at 12:30 AM local time, right on schedule.  Anton, a grandson of repressed Polish people living in Russia during the Stalin era, met me at the gate.  20 minutes later we were in downtown Astana.

First day in Astana

A few hours of sleep and I was up and ready to soak in the local culture.  After acquiring a local sim card and telephone for $26 (4,000 ten) we visited the Pyramid and Court of Independence. Astana has an architectural plan, displayed on the third floor of the Castle of Independence spanning 18 years through 2030. The plan is to build a capital of Asia that would rival it seems the Arab Emirates. The Pyramid is a magnificent structure taking the shape according to its name. Several floors displaying (artwork?) culminate with a garden, leading to a small auditorium at the apex. There, the world religious congress is being held every three years and representatives from world religions meet in a friendly atmosphere. This was the first sign of religious tolerance and celebration of cultural diversity that I have encountered in Astana. It was one of many. After visiting the Pyramid Anton and I have taken a short walk across the street to visit the Court of Independence. It is another enormous structure of marble inside and out. The interior looks so polished that the floor ornaments reflect light making me feel like I am walking on an opaque mirror. An excursion organized by the staff takes us along the atrium to a set of stairs leading to the second floor. There, a small art gallery displays the works of local, American, and French painters, none familiar to me. I am particularly fond of the local painters depicting the nomadic lifestyle of the region. # Bayterek Bayterek was the first building that marked the beginning of the massive construction project undertaken by the president of Kazakhstan. Incidentally, his pictures and portraits are splattered all over Astana in museums, road signs, and government buildings. Normally this type of propaganda is a sign of deep political corruption, which may indeed be the case, but in Kazakhstan common people seem to have a lot of respect for the president. When I asked a tour guide at the top of Bayterek what she thought about the planned expansion of the city, she said that it would continue due to the will and ideas of the president, “may god bless his soul”. Her opinion was seconded by Anton, albeit not as enthusiastically. He thinks that even though Nazarbaev is the king of Kazakhstan, his policies of pluralism and his support of national diversity enables the peoples from 150 or so religions and nationalities to coexist in peace and mutual respect. This is not a small feat and the president should be applauded for this, irrespective of his motives. # Visit to the Chabad House The tour guide at Bayterek mentioned that among other religious institutions, Astana has a large synagogue. I was surprised to hear that, since I knew that very few Jews live in Kazakhstan. Naturally, I was excited to see it with my own eyes. The Astana Synagogue is in fact an impressive building of white and blue on the (Left) side of the city (Astana is divided in two informal parts by the river Ishum. The new construction is on the right bank of the river if you are facing (North?)) This Synagogue is fact part of Habad, a Hasidic organization whose mission is to attract more Jews into the religious practice. It is the only branch of orthodox Jewry that is welcoming of secular Jews. The building is guarded by a perimeter fence and a security guard at the front gate. After informing the guard that I would like to visit the Synagogue, he politely motions me inside and leads me up the stairs into the entrance. Anton follows me in. The Synagogue is empty at 2 PM in the afternoon. The guard informs the Rabbi of our arrival and in less than 30 seconds he appears smiling, sporting an obligatory long beard, white shirt, and a Hasidic hat. He is clearly happy to see us, and after inquiring if I were Jewish (he did not ask Anton, who looks very Slavic) hands both of us a yarmulke and invites us into the prayer hall. My guess is that the room sits approximately 50-100 people at the main level and another 20 or so on each balcony dividing the space into separate sections for men and women. Men can not focus on higher thoughts and prayers when women are too close, he explains. I think he is right about that. After he finished explaining the building attributes, I see that his excitement is starting to grow as he informs me that a big mitzvah is about to be performed. He runs to the back and promptly returns holding a black box containing a tefillin. I am a little bit squeamish, but he is so persistent that I submit and let him wrap my left hand with a flat leather rope, while placing a wooden box on my head. I repeat his words of prayer as he performs the procedure. When he is done, he informs me that he knows of 200-300 Jews that live in the city. For a city of 700,000 this is indeed a minority. Regular services usually gather 3-6 people and they only achieve minyan on Saturdays. Despite of a small congregation they publish a woman’s magazine and Jewish calendar. After giving Anton and I a copy of each, and asking us if we want something to eat or drink he politely escorted us back to the entrance. Please, come back whenever you are in town, he said to me. You as well, he said to Anton. # Trip to ALZHIR Before coming to Kazakhstan I knew that if I only could visit one site in Astana it would be ALZHIR. The camp lies 20 km East of the city in a village that was called Molinovka (meaning made from raspberries). It was part of Soviet Gulag system from 1937 until shorty after Stalin’s death in 1955. The system is commonly referred to as Gulag Archipelago due to Aleksandr Solzhenitsyn. ALZHIR’s significance is not that it housed political prisoners convicted under Article 58 of the NKVD code, but that it housed their wives and children under the age of 3 (children that were 3 years old or older were separated from their mothers and their siblings and sent to a ‘det dom’: a children’s house that is roughly equivalent to a penitentiary for juvenile delinquents). The road to ALZHIR (Akmolinki Lager’ Zhen Izmennikov Rodini: Akmolinsky Camp for the Wives of Traitors to the Motherland) is a deteriorating Soviet era asphalt road with enough bumps and cracks to ruin the suspension system of a modern tank. After about 30 minutes of bumping and grinding we arrive at the camp. As we approach the main building, the first item is in the front right – a Soviet rail car used in transporting women from all over the Soviet Union to ALZHIR. These wagons were manufactured in Odessa, Ukraine from 1928 to 1929 and women spent more than two months inside while being transported to ALZHIR. The wagon is approximately 7×3 meters (23×10 feet) and housed over 70 prisoners. Many had to stand and sleep on top of each other. To the left, a monument commemorating the victims. The barracks have been destroyed and the only surviving structure is the guard tower with the museum building in the back. Over 17,000 women were held in the camp, many have died from malnutrition and 18 hour work days. Their bodies were dumped into a mass grave behind the camp. Among the prisoners were noted Russian intellectuals and artists including Rachel Messerer, the mother of a famous Russian jewish ballerina Maya Mikhailovna Plisetskaya. Inside the museum the story of the prisoners unfolds. While still in Moscow, the women were told by the authorities that a rendezvous with their husbands was being arranged and asked to come to the prison building for the meeting. Many were so happy that they wore red dresses and makeup in anticipation. When they arrived, they were escorted to the ‘waiting area’. Many never saw freedom again. They were read their charges, asked to admit that their husbands were traitors, separated from their children, and packed into wagons for transport to ALZHIR. While at the camp they built their own barracks from straw and clay that could be found on the outskirts of the camp. The women also made their own clothes and later made uniforms for soldiers during the Second World War. One day, while collecting straw, a group of local Kazakh men and women came by. They watched them work and then started throwing what appeared to be small bricks and stones at the prisoners. The guards were laughing. Even the locals know that you are traitors, they said. One of the women picked up a small brick. It was made out of bread. When we left the camp, I asked Anton to stop in the middle of the steppe. In March the steppe is still covered with ice as temperatures can fluctuate between -30 (c) and -10 (c). I walked on the other side of the road and stepped onto the brittle snow. Even though it looked solid, my legs went in knee deep. For a while I just stood there breathing cold air and getting lost in the serenity of infinite spaces stretching for hundreds of miles in all directions. The Kazakh flatland is calming and magnetic. How was it, asked Anton when I got back in the car. It was magical, I said. Finally, I felt like I was in Kazakhstan. # First Two Weeks of Writing Jacki and I just submitted the first two chapter to our publisher, so I would like summarize early lessons learned (actually we submitted one chapter, but the editor decided to break the chapters in half; a decision that we fully support.) The chapters includes material on programming style (from R’s point of view), introduction to functions and functional programming, some information on S4 classes mostly from user’s perspective, vectorizing code, debugging and various methods of data access including web scraping and Twitter API. First the obvious. We underestimated the amount time required to produce the content. No surprises there. We spent too much time wrestling with the outline. Outlining seems to work well when I know my own writing style, but not so well otherwise. At some (earlier) point we should have just started writing and figured out the detailed chapter structure as we went along. I suspect this will change as we get deeper into the material, but only time will tell. What does need to be planned is the immediate section. For me it helps to have all the code written and all the visuals produced prior to starting writing. When I tried writing code on the fly, I struggled to make any meaningful progress. Lastly, it would have really helped if we read each other’s sections more carefully both in terms of synchronizing content and writing style. I hope that the final product does not read like the book was written by two people. Onto Chapter 2. # Getting Ready to Write a Book My co-author, Jacki Buros, and I have just signed a contract with Apress to write a book tentatively entitled “Predictive Analytics with R”, which will cover programming best practices, data munging, data exploration, and single and multi-level models with case studies in social media, healthcare, politics, marketing, and the stock market. Why does the world need another R book? We think there is a shortage of books that deal with the complete and programmer centric analysis of real, dirty, and sometimes unstructured data. Our target audience are people who have some familiarity with statistics, but do not have much experience with programming. Why did we not call the book Data Science blah, blah, blah…? Because Rachel and the Mathbabe already grabbed that title! (ok, kidding) The book is projected to be about 300 pages across 8 chapters. This is my first experience with writing a book and everything I heard about the process tells me that this is going to be a long and arduous endeavor lasting anywhere from 6 to 8 months. While undertaking a project of this size, I am sure there will be times when I will feel discouraged, overwhelmed, and emotionally and physically exhausted. What better vehicle for coping with these feelings than writing about them! (this is the last exclamation point in this post, promise.) So this is my first post of what I hope will become my personal diary detailing the writing process. Here is the summary of the events thus far. • A publisher contacted me on LinkedIn and asked if I wanted to write a book. • Jacki and I wrote a proposal describing our target market, competition, and sales estimates based on comparables. We developed an outline and detailed description of each section. • We submitted our proposal (to the original publisher and two other publishers) and received an approval to publish the book from Apress’ editorial board. (Apress was not the original publisher. More on that process after the book is complete.) We set up a tracking project on Trello (thanks Joel and the Trello team), created a task for every chapter, and a included a detailed checklist for each task. We have not completed all of the data analysis required for the book, so this is going to be an exercise in model building as well as in writing. If you have any advice about how to make the writing process better or if you think we are batshit crazy, please, post in the comments. I hope to write a book that we can be proud of. We have a great editorial team and a technical reviewer who is kind of a legend in the R/S world. They will remain anonymous for now, but their identities will be revealed as soon as they give me permission to do so. I am looking forward to learning about the writing process, about statistics, and about myself. Let the journey begin. # Joint Distributions or B Celebrity Sex in London I remember the first time the concept of joint probability distribution was introduced to me I found it completely unintuitive (like so many topics in probability), declared myself too stupid to get it, and considered giving up on statistics. The problem was that they used these silly gambling examples to demonstrate the concept. Flip of the coin this, roll of the die that. Urrg. Once I reset it in the context that I could relate too, everything became easier. So here we go. Suppose you are a B level celebrity in London. Then suppose that the probability of you having sex on any given day is ⅔ (it would be virtually 1, if you were an A level celebrity in LA, but that would not make for interesting example). Also, suppose that the probability of a cloudy day is ⅘. Here is how we write it in the language of probabilities. X is a random variable tracking our sex patterns. In this case, X can take on the values of X = Sex and X = NoSex. Y is a random variable tracking weather in London during the day. In our case Y = Cloudy or Y = Sunny. It should be obvious that P(X=Sex) or P(X=NoSex) = P(Y=Cloudy) or P(Y=Sunny) = 1. The probability of the whole sample space is 1 in both cases (you either have sex or not or it is either sunny or cloudy). So P(X=Sex) + P(X=NoSex) = ⅔ + ⅓ = 1. By the same token P(Y=Cloudy) + P(Y=Sunny) = ⅘ + ⅕ = 1. In this analysis, we assume that the sex is independent of the weather, an elusive assumptions and the one that should not be confused with correlation. I will try to make the differences clear when I discuss conditional independence. OK, here is the punch line. When we say joint probability distribution, we mean the probability of the combination of the events in question. For finite and discrete random variables such as the ones we are talking about, we can summarize the joint distribution using a table.  Sum = 1 P(X=Sex) = 2/3 P(X=NoSex) = 1/3 P(Y=Sunny) = 1/5 P(X=Sex,Y=Sunny) = 2/15 P(X=NoSex,Y=Sunny) = 1/15 P(Y=Cloudy) = 4/5 P(X=Sex,Y=Cloudy) = 8/15 P(X=NoSex,Y=Cloudy) = 4/15 In the above table when I write P(X=Sex, Y=Sunny) I mean the probability of both sex AND cloudy weather. (Do not confuse this with a notation P(X | Y), which means that we are only interested in Sex given a particular Weather outcome had already occurred.) This is why it is called joint probability. The probabilities listed in the margins of the table, are called … marginal. Each value in the cells is the product of two marginals. For instance the probability of having sex while it is sunny is ⅔ * ⅕ = 2/15. The cool thing is that if you observe only the joint probabilities you can easily calculate the marginals by summing across rows or columns (the reverse does not work – you can’t get joints from marginals, but you can get them from a good dealer in the Bronx.) So, if I observe you, the celebrity in question, for 150 days having sex during 20 sunny days and 80 cloudy days (you were not having sex during the other 50 days, sorry), I may conclude that the marginal probability of you having sex in London is 2/15 + 8/15 = 10/15 = ⅔ = P(X=Sex) = P(X=Sex,Y=Sunny)+ P(X=Sex,Y=Cloudy), which is in fact consistent with our assumptions. This can be formalized as follows: $P(X) = \sum_{Y}^{} P(X,Y)$ This rule is quite general (it is called the sum rule of probabilities) and it says that for any joint distribution X,Y to get back the probability of X we have to sum across all possible values of Y. Try summing across other rows columns and you will see that results are consistent there as well. # Updike’s Rabbit, Poincare, and the Art of Honest Writing Cover of Rabbit, Run As I am reading Rabbit, Run, I am slowly recognizing the literary genius of John Updike and I can not help but to draw parallels to the artists of the second kind — mathematicians. Updike does not use the tricks of literary construction that are so prevalent in the popular literature and modern blog writing. There is nothing wrong with clever literary construction of course. It makes the pages turn, it draws you in and leaves you asking for more. If you have read John Grisham’s Time to Kill (his first and best novel, I think), you know what I am talking about. The problem is that this kind of prose gets tiring after a while as you sort of feel like the author is consciously tricking you. Not so with Updike. His storyline is quite ordinary as are his characters. He does not leave you hanging at the end pages and paragraphs. He simply tells. The beauty of his writing, it seems to me, is that the prose itself is so cleverly nuanced, yet so vivid, that it infuses extraordinary qualities into ordinary events and actors. For example, from Rabbit, Run, describing a foreplay with a plump prostitute: As swiftly, he bends his face into a small forest smelling of spice, where he is out of all dimension, and where a tender entire woman seems an inch away, around a kind of corner. When he straightens up on his knees, kneeling as he is by the bed, Ruth under his eyes is an incredible continent, the pushed-up slip a north of snow. When reading Updike, the reading itself is an incredible experience, a total escape into the Updike dimension that is as insightful as it is unique. This kind of prose seems completely out of reach for mere mortals who need to resort to literary tricks. Cover via Amazon I get a similar feeling when reading Henri Poincare’s The Value of Science (in English translation) in that his understanding of mathematics is so deep that it feels almost untouchable, yet he simply tells without the drama of other popularizers of science like say Hawking (a brilliant man) or Mlodinow (also no slouch.) Not to be outdone by the literary types, Poincare’s narration is so beautiful that it makes me want to learn French just to read him in the original. Here is Poincare on the nuances of Number Theory: He is a savant indeed who will not take is as evident that every curve has a tangent; and in fact if we think of a curve and straight line as two narrow bands, we can always arrange them in such a way that they have a common part without intersecting And here he is again on the scientific motivation. The scientist does not study nature because it is useful; he studies it because he delights in it, and he delights in it because it is beautiful. If nature were not beautiful, it would not be worth knowing, and if nature would not be worth knowing, life would not be worth living. It was Poincare who noted that: A scientist worthy of his name, above all a mathematician, experiences in his work the same impression as an artist; his pleasure is as great and of the same nature. The curious intersection of art and science has been noted by many. The fact that science has its own aesthetic beauty is not a byproduct of the scientific method. As Poincare so eloquently points out, it is the reason for its existence. # To plot or to ggplot, that is not the question Producing informative and aesthetically pleasing quantitative visualizations is hard work. Any tool or library that helps me with this task is worth considering. Since I do most of my work in R, I have a choice of using plot, the default plotting library, a more powerful lattice package, and ggplot, which is based on the Grammar of Graphics. There is usually a tradeoff between the expressiveness of the grammer and the learning curve necessary to master it. I have recently invested 3 days of my life learning the ins and outs of ggplot and I have to say that it has been most rewarding. The fundamental difference between plot and ggplot is that in plot you manipulate graphical elements directly using predefined functions, whereas in ggplot you build the plot one layer at a time and can supply your own functions, although you can do quite a bit (but not everything) with a function called qplot, which abstracts the layering from the user and works similar to plot. And therefore qplot is exactly where you want to start when upgrading from plot. To demonstrate, the following R code partly visualizes the famous iris dataset containing Sepal and Petal measurements of three species of Iris flower using the built in plot function. par (mar=c(3,3,2,1), mgp=c(2,.7,0), tck=-.012, las=1) with(iris, plot(Sepal.Length, Sepal.Width, col=as.numeric(Species)+1, pch=20)) lbs = levels(iris$Species)
legend('topright', legend=lbs,
col=2:4, cex=0.7, pch=20, box.lwd=0.5, pt.cex=0.6)

One of the problems with plot is that the default plotting options are poorly chosen, so the first line of code fixed the margins, tick marks, and the orientation of the y axis tick labels.  The parameter col=as.numeric(Species) + 1 fixes the color offset at Red as opposed to the default Black.  Type palette() at the R prompt to see the default color vector.

The last complication is that plot does not draw the legend for you; it must be specified by hand.  And so, if you run the above code in R, you should get the following output.

It took a little bit of work, but the output looks pretty good.  Following is the equivalent task using ggplot’s qplot function.

qplot(Sepal.Length, Sepal.Width, data = iris, colour = Species, xlim=c(4,8))

As you can see, ggplot chooses a lot more sensible defaults and in this particular case, the interface for specifying the intent of the user is very simple and intuitive.

A final word of caution.  Just like a skier who sticks to blue and green slopes is in danger of never making it out of the intermediate hell, so is the qplot user will never truly master the grammar of graphics.  For those who dare to use a much more expressive ggplot(…) function, the rewards are well worth the effort.

Here are some of the ggplot references that I found valuable.