Why online meetings work but not online conferences

Sitting through a “slip fielding meeting” this morning, I had an epiphany – on why office work and meetings have adjusted fairly well to online formats, but not conferences. It has to do with backchannel conversations.

In meetings where everyone is in the same room, there is naturally just one conversation. Everyone is speaking to everyone else at the same time. Unless the meeting is humongously large, it is considered rude for people to “cross talk” in the meeting, and hence there is just one conversation. Of course, in the last decade or so, people have taken to texting at meetings and stuff, but that is still small.

The advantage with moving this kind of a meeting online is that now crosstalk is fully legit, as long as you are doing it using text only. Anyway, everyone is sitting with their computers. All it takes is one simple alt-tab or command-tab, and you can chat away with others present in the meeting. In fact, this makes online meetings MORE efficient by increasing the information flow (since the main channel of large meetings are usually low throughput).

It is the other way round with conferences and events. In conferences and events, the whole point is backchannel conversation. Pretty much nobody is there to listen to the lectures or panel discussions anyways – all that most attendees want to do is to meet other attendees.

And off-line conferences are conveniently structured to enable such interaction. By having multiple parallel sessions, for example, it becomes legit to just stay out and talk to others. There is always a buzz in the corridors (one conference which was single-session-at-a-time only turned out to be bloody boring).

The other thing is that most backchannel and side channel conversations at conferences are between people who don’t yet know each other, and who are there for discovery. So you need to physically bump into someone to talk to them – you can’t randomly start a conversation with someone.

And this translates horribly to online. Online is great for backchannel and side channel conversations with people you already know well – like colleagues. When you don’t know most other people, side channel conversation is awkward. And the main channel content in conferences is largely useless anyway.

This is why it is important that conferences and seminars and other such events move to an offline format asap. For large work meetings we can continue online even after we’re all back at office.

PS: I’m firmly in the DJ D-Sol camp in terms of calling people back to work, at least to make them live in or close to the “home locations”. This way, you have the optionality to meet at short notice without planning, something that fully remote work makes it really hard.

Modelling for accuracy

Recently I’ve been remembering the first assignment of my “quantitative methods 2” course at IIMB back in 2004. In the first part of that course, we were learning regression. And so this assignment involved a regression problem. Not too hard at first sight – maybe 3 explanatory variables.

We had been randomly divided into teams of four. I remember working on it in the Computer Centre, in close proximity to some other teams. I remember trying to “do gymnastics” – combining variables, transforming them, all in the hope of trying to get the “best possible R square”. From what I remember, most of the groups went “R square hunting” that day. The assignment had been cleverly chosen such that for an academic exercise, the R Square wasn’t very high.

As an aside – one thing a lot of people take a long time to come to terms with is that in “real life” (industry problems) R squares aren’t usually that high. Forecast accuracy isn’t that high. And that the elegant methods they had learnt back in school / academia may not be as elegant any more in industry. I think I’ve written about this, but I can’t find the link now.

Anyway, back to QM2. I remember the professor telling us that three groups would be chosen at random on the day of the assignment submission, and from each of these three groups one person would be chosen at random who would have to present the group’s solution to the class. I remember that the other three people in my group all decided to bunk class that day! In any case, our group wasn’t called to present.

The whole point of this massive build up is – our approach (and the approach of most other groups) had been all wrong. We had just gone in a mad hunt for R square, not bothering to figure out whether the wild transformations and combinations that we were making made any business sense. Moreover, in our mad hunt for R square, we had all forgotten to consider whether a particular variable was significant, and if the regression itself was significant.

What we learnt was that while R square matters, it is not everything. The “model needs to be good”. The variables need to make sense. In statistics you can’t just go about optimising for one metric – there are several others. And this lesson has stuck with me. And guides how I approach all kinds of data modelling work. And I realise that is in conflict with the way data science is widely practiced nowadays.

The way data science is largely practiced in the wild nowadays is precisely a mad hunt for R Square (or area under ROC curve, if you’re doing a classification problem). Whether the variables used make sense doesn’t matter. Whether the transformations are sound doesn’t matter. It doesn’t matter at all whether the model is “good”, or appropriate – the only measure of goodness of the model seems to be the R square!

In a way, contests such as Kaggle have exacerbated this trend. In contests, typically, there is a precise metric (such as R Square) that you are supposed to maximise. With contests being evaluated algorithmically, it is difficult to evaluate on multiple parameters – especially not whether “the model is good”. And since nowadays a lot of data scientists hone their skills by participating in contests such as on Kaggle, they are tuned to simply go R square hunting.

Also, the big difference between Kaggle and real life is that in Kaggle, the model that you build doesn’t matter. It’s just a combination. You get the best R square. You win. You take the prize. You go home.

You don’t need to worry about how the data for the model was collected. The model doesn’t have to be implemented. No business decisions need to be made based on the model. Contest done, model done.

Obviously that is not how things work in real life. Building the model is only one in a long series of steps in solving the business problem. And when you focus too much on just one thing – the model’s accuracy in the data that you have been given, a lot can be lost in the rest of the chain (including application of the model in future situations).

And in this way, by focussing on just a small portion of the entire data science process (model building), I think Kaggle (and other similar competition platforms) has actually done a massive disservice to data science itself.

Tailpiece

This is completely unrelated to the rest of the post, but too small to merit a post of its own.

Suppose you ask a software engineer to sort a few datasets. He goes about applying bubble sort, heap sort, quick sort, insertion sort and a whole host of other techniques. And then picks the one that sorted the given datasets fastest.

That’s precisely how it seems “data science” is practiced nowadays

Junior Data Scientists

Since this is a work related post, I need to emphasise that all opinions in this are my own, and don’t reflect that of any organisation / organisations I might be affiliated with

The last-released episode of my Data Chatter podcast is with Abdul Majed Raja, a data scientist at Atlassian. We mostly spoke about R and Python, the two programming languages / packages most used for data science, and spoke about their relative merits and demerits.

While we mostly spoke about R and Python, Abdul’s most insightful comment, in my opinion, had to do with neither. While talking about online tutorials and training, he spoke about how most tutorials related to data science are aimed at the entry level, for people wanting to become data scientists, and that there was very little readymade material to help people become better data scientists.

And from my vantage point, as someone who has been heavily trying to recruit data scientists through the course of this year, this is spot on. A lot of profiles I get (most candidates who apply to my team get put through an open ended assignment) seem uncorrelated with the stated years of experience on their CVs. Essentially, a lot of them just appear “very junior”.

This “juniority”, in most cases, comes through in the way that people have done their assignments. A telltale sign, for example, is an excessive focus on necessary but nowhere sufficient things such as data cleaning, variable transformation, etc. Another telltale sign is the simple application of methods without bothering to explain why the method was chosen in the first place.

Apart from the lack of tutorials around, one reason why the quality of data science profiles continues to remain “junior” could be the organisation of teams themselves. To become better at your job, you need interact with people who are better than you at your job. Unfortunately, the rapid rise in demand for data scientists in the last decade has meant that this peer learning is not always there.

Yes – if you are a bunch of data scientists working together, you can pull each other up. However, if many of you have come in through the same process, it is that much more difficult – there is no benchmark for you.

The other thing is the structure of the teams (I’m saying this with very little data, so call me out if I’m bullshitting) – unlike software engineers, data scientists seldom work in large teams. Sometimes they are scattered across the organisation, largely working with tech or business teams. In any case, companies don’t need that many data scientists. So the number is low to start off with as well.

Another reason is the structure of the market – for the last decade the demand for data scientists has far exceeded the available supply. So that has meant that there is no real reason to upskill – you’ll get a job anyway.

Abdul’s solution, in the absence of tutorials, is for data scientists to look at other people’s code. The R community, for example, has a weekly Tidy Tuesday data challenge, and a lot of people who take that challenge put up their code online. I’m pretty certain similar resources exist for Python (on Kaggle, if not anywhere else).

So for someone who wants to see how other data scientists work and learn from them, there is plenty of resources around.

PS: I want to record a podcast episode on the “pile stirring” epidemic in machine learning (where people simply throw methods at a dataset without really understanding why that should work, or understanding the basic math of different methods). So far I’ve been unable to find a suitable guest. Recommendations welcome.

Formal interactions

Over the last couple of years, as the covid-19 pandemic has hit us and people have been asked to work from home, there has been a raging debate on the utility of office, especially for “knowledge work” (where the only “tool” you need is a computer).

Some companies such as Twitter have announced a “remote work in perpetuity”. Others such as Goldman Sachs have declared that remote work is inefficient and people need to return to offices asap. I probably was closer to the twitter position not so long ago, but now I think I’m firmly in the GS camp.

If you look at all the articles on remote work (I think Derek Thompson of The Atlantic has written some interesting pieces on this), one of the main arguments in favour of getting people to office is “informal interactions”, “bumping into colleagues”, “water cooler conversations”, etc. These kind of unstructured interactions can lead to new thoughts, which lead to innovation which lead to growth, goes the saying.

And in response to this, some companies have been trying to replicate these informal interactions in the zoom world. Instead of bumping into a colleague, you are forced to do a random “coffee chat” with a random colleague. There are online events. The hope here is that they will stand in for offline informal interactions.

Whether these events actually work or not, I don’t know. However, as I come close to a year in my job, it is not the informal interactions that I care about when I think of office vs remote. It’s “formal interactions”.

The lightbulb moment occurred earlier this week. I’m working on a fairly challenging problem with two others in my team. Two of the three of us were in office, and started talking about this problem. We drew some stuff on the whiteboard. Did some handwaving. And soon we had a new idea on how to approach this problem.

Now the task at hand was to explain this to the third guy, who is in another city. We opened Google Meet. We opened a “JamBoard” in that. I tried to replicate the whiteboard drawing, but he couldn’t see my handwaving (you realise that in video calls, video and screen share are two disjoint things!). It took a whole lot of effort to get the idea across.

This is not an isolated incident. In terms of collaborative work, I’ve found on multiple occasions that simply sitting together for a short duration of time can achieve so much more than what you can do in online meetings.

Another thing is that I’ve found myself to get exhausted faster in online meetings. Maybe I speak louder. Maybe having to look in one particular direction for the duration of the meeting is stressful. Offline meetings I can keep going and going and going (especially when on methylphenidate). Online, 2-3 meetings and I’m exhausted.

And then you have new colleagues and onboarding. Employees at an early stage require an extremely high degree of collaborative work. You need to “show stuff” to your new colleagues. Sometimes you might just take over their laptop. There are times when they need interventions that in the off-line world take 2 minutes, but online you need to schedule a meeting for.

Notice that none of the stuff I’ve mentioned so far is “informal”. Maybe it’s the nature of the work – involving deep thinking and complicated ideas. Remote work is absolutely brilliant in terms of the ability to shut yourself off without distractions and do deep work. The moment you need to collaborate, though, you need to be in the same physical space as your collaborators.

It’s unlikely I’ll ever want to go back to office full time (as I said, working from home is brilliant for deep work). However, I do look forward to a permanent hybrid model, meeting in office at least once a week. Hopefully the pandemic will allow us to get to this sooner rather than later.

Oh, and informal interactions are only a bonus.

ADHD and the Bhagavad Gita

A couple of weeks back, I stumbled upon an article I had written for Huffington Post India a few years back about what it is like to live with ADHD.  Until HuffPost India shut down, if you googled my name, one of the first links that you would find was this article. Now, the public version of the article is lost for posterity.

In any case, the draft lives on in my email outbox, and I have since forwarded it to a few people. This is how I begin that article:

There is a self-referential episode in the Mahabharata where sage Vyasa tries to get Ganesha to scribe the Mahabharata. Ganesha accepts the task, but imposes the condition that if Vyasa stopped dictating, he will stop writing and the epic will remain unfinished for ever.

If you have Attention Deficit Hyperactivity Disorder (ADHD), you would ideally want to work like Ganesha writing the Mahabharata – in long bursts where you are so constantly stimulated that there is no room for distraction. ADHD makes you a bad finisher, and makes you liable to abandon projects. You could be so distracted that it takes incredible effort to get back to the task. Once you are distracted, you might even forget that you were doing this task, and thus leave it unfinished. Moreover, ADHD makes it incredibly hard to do grunt-work, which is essential in finishing tasks or projects.

And earlier today, during on of my random distractions at work, I started thinking that this is not the only instance in the Mahabharata where ADHD makes an appearance. If you look at the Mahabharata in its fullest form, which includes the Bhagavad Gita (which, it appears, is a retrospective addition), ADHD makes yet another appearance.

If you distill the Bhagavad Gita to its bare essentials, the “principal component” will be this shloka:

??????????????????? ?? ????? ??????
?? ?????????????????? ?? ?????????????????? ?-??

In Roman scripts—

Karmanye vadhikaraste Ma Phaleshu Kadachana,
Ma Karmaphalaheturbhurma Te Sangostvakarmani

Googling threw up this translation (same site as the above quote):

The meaning of the verse is—

You have the right to work only but never to its fruits.
Let not the fruits of action be your motive, nor let your attachment be to inaction.

And I was thinking about it in the context of some work recently – for those of us with ADHD, this is a truism. Because unless we hyper focus on something (and the essence of ADHD is that you can’t choose what you want to hyper focus on), we have no attachments. It is like that “Zen email”.

Assume that there is a gap between the completion of the work and the observation of the “fruits” (results) of the work. By the time the fruits of the work are known, it is highly likely that you have completely forgotten about the work itself and moved on to hyper focus on something else.

In this case, whatever is the result of the work, that you have moved on means that you have become disattached from the work that you did, and so don’t really care about the result. And that makes it easier for you to appreciate the result in a cold, rational and logical manner – if you happen to care about it at all, that is.

The only exception is if you had continued to hyperfocus on the work even after it was completed. In this kind of a situation, you become excessively attached to the work that you have done (and to an unhealthy level). And in this case you care about the flowers, fruits, seeds and subsequent plants of your work. Not a good state to be in, of course, but it doesn’t happen very often so it’s fine.

The other thing about ADHD and “moving on” is that you don’t get possessive of your past work, and you are more willing to tear down something you had built in the past (which doesn’t make sense any more) and start rebuilding it. Again, this can both be a negative (reinventing your own wheel / wasting time) and a positive (ability to improve).

Random line I just came up with – on average, people with ADHD are exactly the same as people without ADHD. Just that their distributions are different.

Letters To My Berry #60

Yes. I’m messing with mumma’s numbers. The last one she wrote was #33. However, since we used to write one every month when you were little, I decided this should be called #60. 12 times 5. There are 12 months in a year.

On that note, you know how to multiply now. And divide. And add and subtract, of course. You’ve also learnt fractions, and prime numbers and square numbers, most of them from school but some of them because I try my experiments on you.

And you are an amazing and eager learner.

One of your and my high points in the last 3-4 months has been the quizzes. In March or April, mumma started taking you for this “Qshala family quiz”. While you would know the answers to most questions there, you would never get a chance to speak out the answers. And that would make you unhappy, and you would cry.

So we decided you needed your own quiz. I’ve had a blast setting them. At the young age of not-yet-5, you have been introduced to the concepts of “list it” and “stage 2”. Don’t be surprised to see a long visual connect before you are 6.

The kind of stuff you are interested in is incredible. I had randomly found a nice periodic table map on Amazon, and got it for you. And it turned out that you not only know all the Noble Gases, but you know it all in ORDER. One day you and I were doing a Sporcle Periodic Table quiz together, and you surprised me with how much you knew.

You are also amazing at recognising countries from their football shirts (basically mapping to their flags), from their shapes, flags and all such. Some day I was watching some random football video, and you recognised the flag of North Macedonia! Mumma was flabbergasted.

The time since the last time we wrote a letter to you coincided with another big wave of covid and lockdown. You had been happily going to offline school, even if only on two days a week, when we wrote the last letter, but then everything shut again.

However, the difference between this lockdown and the previous ones was that by now you had learnt to read. And you devoured books. During a family zoom call during this period, someone asked you what kind of books you like, and you replied saying “I only read non fiction”.

Barbie sent you a book on the human body and you demolished it in one evening. You surprise us once in every few days based on what you know. And when you speak, or tweet from my account, you can get really profound.

Like today, mumma told me “get a life”, and you asked what “life” means. The other day, you tweeted this:

https://mobile.twitter.com/karthiks/status/1428970068474404864

In terms of profundity, though, I was (positively) amazed at one of your actions when we visited your cousins Mahika and Arhita last month. We had taken along a cake, and all you children cut it. The cake had a piece of chocolate on it, and two other kids were negotiating on who gets that, and what toppings the other child would get. And as they were talking animatedly you calmly put out your hand, picked up the chocolate and ate it off!

You are not afraid at all to ask questions. Now that school has started again, you love going there, and have started taking care of the younger children in school and showing them works.

Oh, and in the last month and a half, your reading pattern has changed considerably. It started with a visit to this wonderful bookshop called “Lightroom” in Cooke Town. I, as usual, bought you a whole bunch of non fiction books. Mumma bought you a whole bunch of fiction books.

And suddenly, after that, you only read fiction. You still don’t read “big people books” with lots of text and no pictures (so no Tinkle yet), but love your little stories. You would read them so often that the other day mumma decided to put away all your fiction books in a shelf, so that you can get back to reading non fiction.

Five year old paaps! You are a big girl now. And literally. You have had a growth spurt in the last month or so, and are now so heavy that mumma can’t carry you.

On most days you sleep by yourself in your room. In fact, now you’ve gotten a much bigger room for yourself as we swapped what was your room with the study. You have SO many things that you need such a big room. You sleep there all by yourself, surrounded by your toys. You wake up in the morning and make your own bed, if you haven’t sneaked across the house to our room in the middle of the night that is.

You know – I’m actually feeling conscious writing this because I know that you are fully capable of reading this now. There might be the odd word here or there that you may not know – but will make sure you ask – but reading this should be a breeze now. And os I’m conscious that I shouldn’t make this too long – else you might put NED to read this.

And since it’s been so long since we wrote this, there is still so much more to say. So I’ll just do this in bullet points:

  • You’ve recently gone back to a “appa do like this” phase. You make weird shapes with your hands and want me to copy them exactly
  • Mamma has gotten you hooked to Jurassic Park, and similar “dinosaur movies”. And you love watching and re-watching them. Of course, you get scared as well! That is just part of the game
  • You have restarted voice training classes with Mads.
  • You can brush your hair and tie it up into a “monkey jutta” all by yourself
  • You are self sufficient enough now that we don’t have to supervise your online school. You open my laptop, find the calendar notification and join the Zooom meeting
  • Thanks to the second wave, there has been no travel, unfortunately in the last 6 months. Hopefully we can correct this soon. Then again – you got your passport renewed in this time
  • You still ask for permission when you want to see cartoons. That said, you don’t see much of cartoons nowadays. Books and Khan Academy are more interesting to you

OK I guess it’s really time to stop now! Happy birthday, sweetheart! Have a great year ahead.

 

Work is a momentum trade

Last evening, I called it a day at work at 4:30 pm. It was similar on Tuesday as well – I had gone to office, but decided to leave at 4, and go home and continue working. On both these days, the reason I shut shop early is that I wasn’t being productive. My mind was in a rut and I was unable to think.

I might compensate for it by working longer today. I might have already compensated for it by working late into the evening on Monday. I don’t really know.

Basically, the way I like to work is to treat it as a “momentum trade” (as they call it in capital markets). On days when work is going well, I just go on for longer and longer. On days when I’m not doing well, unless there are urgent deadlines, I shut shop early.

And for me, “going well” and “going badly” can be very very different. The amount I can achieve per hour of work when I’m in flow is far more than what I can achieve per hour of work when I’m not in flow. Hence, by working for longer on days when I’m doing well, I basically maximise the amount of work I get done per hour of work.

It is not always like this, and not with everyone. Our modern workday came from the industrial revolution, and factories. In factories, work is tightly defined. Also, assembly lines mean it is impossible for people to work unless people around them are also working (this is one supposed reason for the five day workweek developing in the US – with large numbers of both Christian and Jewish employees, it didn’t make sense for the factory to be operational on either Saturday or Sunday).

And our modern office working hours have developed from this factory working hours, because of which we traditionally have everyone working on a fixed shift. We define a start and end of the work day, and shut shop precisely at 6pm (say) irrespective of how work is going.

In my view, while this works for factories or factory-like “procedural” work,  for knowledge work that is a bad trade. You abruptly cut the wins when the going is good, and just keep going on when the going is bad, and end up taking a much longer time (on average) to achieve the same amount of work.

Then again, I have the flexibility to define my own work hours (as long as I attend the meetings I’ve committed to and finish the work I’m supposed to finish), so I’m able to make this “better momentum trade” for myself. If you are in a “thinking” profession, you should try it too.

Chaupat Raja Cooking

While cooking my dinner this evening, I had a realisation, and not a pleasant one. I realised that the way I cook can sometimes be described as “chaupat raja” model of cooking.

The story goes that there was a town called “andher nagari” (dark town), which was ruled by a “chaupat raja”. The raja had fixed the price of all commodities at “1 taka” (not sure if it’s the same as the Bangladeshi currency).

So if you bought onions, you would pay 1 taka per onion, irrespective of the size or quality of it. If you buy a piece of rope, you would again pay 1 taka, irrespective of its length. The story, as told in my 8th Standard Hindi textbook, has a bunch of hilarious examples of the absurdities caused by this regulation.

A wall has fallen and killed a man. The chain of investigation reveals that someone sold a very large bucket for 1 taka, and the latter used that bucket as a measure for water, and thus ends up building a wall that is highly prone to collapsing.

Another story is that someone needs to be hanged, and the hangman can only prepare a loose noose because for 1 taka he ended up getting a long piece of rope that day. And so on.

Anyway, one of my wife’s criticisms about my cooking is that I sometimes “lack proportion”. Now, it doesn’t extend to everything – for my coffee, for example, I have a gram scale in the kitchen which I use to carefully measure out both the quantity of the powder and the amount of water (next in line is to buy a food thermometer so I can use water of the exact same temperature each time).

However, when cooking certain things, I use rough measures. “Throw in all the carrots in the fridge”, for example. Or “use two carrots”, not bothering about the size of the said carrots. I use “number of eggs” as measure without thinking about the size of the eggs (which varies considerably in the shops around where I live).

And that leads to chaupat raja kind of outcomes. One day, my omelette had too much onion because the onion I decided to cut that day was large. Another day, a vegetable stew I’d made turned out too sweet because there were three carrots left in the fridge and I put in all of them, though normally I would’ve only put two.

My habit of throwing in everything without measuring means that my wife has banned me from cooking several dishes for her.

In any case, what I’m trying to illustrate is that using measures in the kitchen based on numbers of something can lead to massively uncertain outcomes, and is an example of “chaupat raja economics”. What we need is better precision (even using something like “1 cup of diced carrots” is inaccurate because the amount of diced carrots a cup can hold can change based on the size of each dice. never mind “cup” is in any case an inexact measure).

Now that I’ve recognised that my style of cooking is like chaupat raja, I’ve decided I need to cooking. There is no reason that coffee is the only thing for which I should pay attention to bring in precision.

Or maybe it will just take too much effort, and the average chaupat raja outcome in the kitchen isn’t bad (the ultimate outcome for the chaupat raja was banned. The story goes that someone needs to be hanged, but it turns out that the noose is too loose (for 1 taka, the hangman got a long piece of rope that day), so the king decides to find someone whose neck fits the  noose. After much searching, someone suggests that the king’s neck is the right size for the noose and he hangs himself.

 

The Science in Data Science

The science in “data science” basically represents the “scientific method”.

It’s a decade since the phrase “data scientist” got coined, though if you go on LinkedIn, you will find people who claim to have more than two years of experience in the subject.

The origins of the phrase itself are unclear, though some sources claim that it came out of this HBR article in 2012 written by Thomas Davenport and DJ Patil (though, in 2009, Hal Varian, formerly Google’s Chief Economist had said that the “sexiest job of the 21st century” will be that of a statistician).

Some of you might recall that in 2018, I had said that “I’m not a data scientist any more“. That was mostly down to my experience working with companies in London, where I found that data science was used as a euphemism for “machine learning” – something I was incredibly uncomfortable with.

With the benefit of hindsight, it seems like I was wrong. My view on data science being a euphemism for machine learning came from interacting with small samples of people (though it could be an English quirk). As I’ve dug around over the years, it seems like the “science” in data science comes not from the maths in machine learning, but elsewhere.

One phenomenon that had always intrigued me was the number of people with PhDs, especially NOT in maths, computer science of statistics, who have made a career in data science. Initially I dismissed it down to “the gap between PhD and tenure track faculty positions in science”. However, the numbers kept growing.

The more perceptive of you might know that I run a podcast now. It is called “Data Chatter“, and is ten episodes old now. The basic aim of the podcast is for me to have some interesting conversations – and then release them for public benefit. Yeah, yeah.

So, there was this thing that intrigued me, and I have a podcast. I did what you would have expected me to do – get on a guest who went from a science background to data science. I got Dhanya, my classmate from school, to talk about how her background with a PhD in neuroscience has helped her become a better data scientist.

It is a fascinating conversation, and served its primary purpose of making me understand what the “science” in data science really is. I had gone into the conversation expecting to talk about some machine learning, and how that gets used in academia or whatever. Instead, we spoke for an hour about designing experiments, collecting data and testing hypotheses.

The science in “data science” basically represents the “scientific method“. What Dhanya told me (you should listen to the conversation) is that a PhD prepares you for thinking in the scientific method, and drills into you years of practice in it. And this is especially true of “experimental” PhDs.

And then, last night, while preparing the notes for the podcast release, I stumbled upon the original HBR article by Thomas Davenport and DJ Patil talking about “data science”. And I found that they talk about the scientific method as well. And I found that I had talked about it in my newsletter as well – only to forget it later. This is what I had written:

Reading Patil and Davenport’s article carefully suggests, however, that companies might be making a deliberate attempt at recruiting pure science PhDs for data scientist roles.

The following excerpts from the article (which possibly shaped the way many organisations think about data science) can help us understand why PhDs are sought after as data scientists.

  • Data scientists’ most basic, universal skill is the ability to write code. This may be less true in five years’ time (Ed: the article was published in late 2012, so we’re almost “five years later” now)
  • Perhaps it’s becoming clear why the word “scientist” fits this emerging role. Experimental physicists, for example, also have to design equipment, gather data, conduct multiple experiments, and communicate their results.
  • Some of the best and brightest data scientists are PhDs in esoteric fields like ecology and systems biology.
  • It’s important to keep that image of the scientist in mind—because the word “data” might easily send a search for talent down the wrong path

Patil and Davenport make it very clear that traditional “data analysts” may not make for great data scientists.

We learn, and we forget, and we re-learn. But learning is precisely what the scientific method, which underpins the “science” in data science, is all about. And it is definitely NOT about machine learning.

The Fragile Charioteer

A few days back, I was thinking of an interesting counterfactual in the Mahabharata. As most people know, the story goes that Arjuna went to battle with his charioteer Krishna, and got jitters looking at all his relatives and elders on the other side, and almost lost the will to fight.

And then Krishna recited to him the Bhagavad Gita, which inspired Arjuna to get back to battle, and with Krishna’s expert charioteering (and occasional advice), Arjuna led the Pandavas to (an ultimately pyrrhic) victory in the war.

A long time back I had introduced my blog readers to the “army of monkeys” framework. In that I had contrasted the war in Ramayana (a seemingly straightforward war fought against a foreign king who had kidnapped the hero’s wife) to the war in the Mahabharata (a more complex war fought between cousins).

Given that the Ramayana war was largely straightforward, with the only trickery being in the form of special weapons, going to war with an army of monkeys was a logical choice. Generals on both sides apart, the army of monkeys helped defeat the Lankan army, and the war (and Sita) was won.

The Mahabharata war was more complex, with lots of “mental trickery” (one of which almost led Arjuna to quit the war) and deception from both sides. While LOTS of soldiers died (the story goes that almost all the Kshatriyas in India died in the war), the war was ultimately won in the mind.

In that sense, the Pandavas’ choice of choosing a clever but non-combatant Krishna rather than his entire army (which fought on the side of the Kauravas) turned out to be prescient.

When I wrote the original post on this topic, I was a consultant, and had gotten mildly annoyed at a prospective client deciding to engage an army rather than my trickery for a problem they were facing. Now, I’m part of a company, and I’m recruiting heavily for my team, and I sometimes look at this question from the other side.

One advantage of an uncorrelated army of monkeys is that not all of them will run away together. Yes, some might run away from time to time, but you keep getting new monkeys, and on a consistent basis you have an army.

On the other hand, if you decide to go with a “clever charioteer”, you run the risk that the charioteer might choose to run away one day. And the problem with clever charioteers is that no two of them are alike, and if one runs away, he is not easy to replace (you might have to buy a new chariot to suit the new charioteer).

Maybe that’s one reason why some companies choose to hire armies of monkeys rather than charioteers?

Then again, I think it depends upon the problem at hand. If the “war” (set of business problems) to be fought is more or less straightforward, an army of monkeys is a superior choice. However, if you are defining the terrain rather than just navigating it, a clever charioteer, however short-lived he might be, might just be a superior choice.

It was this thought of fleeing charioteers that made me think of the counterfactual with which I begin this post. What do you think about this?

PS: I had thought about this post a month or two back, but it is only today that I’m actually getting down to writing it. It is strictly a coincidence that today also happens to be Sri Krishna Janmashtami.

Enjoy your chakli!