musings on music and life

September 7, 2017

Should chemists learn to code?

Filed under: Chemistry, Chemistry Jobs, Coding, education — sankirnam @ 9:25 am

I recently posted this comment on a post in Chemjobber earlier this week, so here it is. This is in response to the question in the title.

My two cents:

It’s not just that “chemistry majors should learn to code”; I feel that all college graduates today should learn to code. Programming is becoming a fundamental type of literacy these days. Just like how all college graduates should be fully literate in English and have some exposure to mathematics (e.g. calculus), all graduates should also have some experience with coding or programming.

As to how to incorporate programming into a typical undergraduate chemistry curriculum – I’m not entirely sure. Like a lot of people here, I took a required course as an undergrad on Matlab programming, after which I promptly forgot everything, since we never used it again. My PhD work in synthetic organic chemistry also involved zero programming, and other organic chemists here will probably also have similar experiences. In organic chemistry, programming is one of those things that is nice to know, but not at all necessary for success, and may even be viewed as somewhat of a distraction – is knowing how to program in Java going to get you better separations in your columns? Not really.

Everything I know about programming came AFTER I finished my PhD – I self-taught programming with online courses, starting with Codecademy, and after I felt I had reached a decent level of competency, I enrolled in a “Data Science” bootcamp last year. Everything I learned was completely orthogonal to chemistry; there’s little overlap between training and running a machine learning model using Python/scikit-learn and being able to do asymmetric oxidations at -78 C. 

If you’re doing computational chemistry, then sure, knowing fundamental programming and CS is incredibly important. In experimental synthetic chemistry…I’m not so sure. My academic experiences have proved that programming has limited utility in chemistry. I think it’s time for this part of chemistry to catch up to the modern age as well. Like Anon 3:15 PM says, if you can type print(‘Hello World!’) into a Python interpreter, then congratulations – you know more programming than 99% of organic chemists. But you also know less programming than 100% of professional developers.

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October 31, 2016

Review of MITx 6.00.1x

Filed under: Coding, education — sankirnam @ 12:53 pm

just finished the above course – I just completed the last problem on the final exam and completed the exit survey a few minutes ago, so I figured I would write my thoughts on the course while they’re still fresh.

My impressions of the course are unanimously positive. I just finished the current iteration of the course (Aug – Nov 2016), and I found it to be excellent. I just finished writing an email to Prof. Grimson (the professor conducting the course), thanking him for all his efforts in preparing such high-quality materials!

Keep in mind that the title of the course is “Introduction to Computer Science and Programming using Python”, and so it is aimed to be an intro CS course of sorts. Nonetheless, it does serve as a very good introduction to the Python language, and covers fundamental CS concepts while teaching the Python language, including the various data structures (lists, tuples, and dictionaries), functions, and classes. The course isn’t intended to teach Python specifically, and so doesn’t cover a lot of the things unique to Python (such as lambda functions, list comprehensions, and other topics).

In retrospect, I wish I had taken this course before taking the “Data Science” bootcamp this summer – I would have been better prepared and would have had at least a rudimentary understanding of the CS fundamentals. Anyway, what’s done is done, and I’m glad that I was able to take this course.

The problem sets were very well crafted. They were appropriately challenging, and I probably did spend around the recommended 15 hours/week or so on them, and they weren’t too difficult where I would have ended up throwing my computer out the window and quitting in frustration. The bonus is that I ended up also learning how to use my computer better – since this course uses Python 3, I ended up using Anaconda to install that (so that I could manage that alongside my existing Python 2 install). I also ended up using Spyder as my IDE of choice for the course, and I’ve come to like that a lot.

As always, if you want to take a look at the problem sets, exercises, and my solutions, I’m posting everything to my github.

Proof of completion (I blacked out my username and email to dodge spambots):mitx6001x

Anyway, onwards to the sequel course, 6.00.2x! I started this course and it’s proving to be MUCH tougher, since the barrier is no longer the Python language, but abstractly developing algorithms before implementing them in Python.

August 14, 2016

Microsoft DAT210x

Filed under: Data Science, education — sankirnam @ 12:15 pm

I recently completed the course Microsoft DAT210x: Programming with Python for Data Science on Edx, so I’ll just take a moment to review it here. I’m still taking the Data Science bootcamp by Logit, and took this as a supplement to get additional exercises with Python, Pandas, Scikit-Learn, and other Data Science-related packages.

The course was just introduced by Microsoft last month as part of their “Online Data Science Degree Program“. As such, I took the course from July-August, and this was the first iteration (the course just ended last Friday). That being said, 6 weeks is not much time to teach something as broad as “Data Science”. The course starts with an introduction to the subjects of Data Science and Machine Learning, and then progresses into an introduction to Pandas, which is a Python package for the manipulation of DataFrames, similar to what you do in R. After also covering a brief survey of 2-D and 3-D visualizations with Pandas and matplotlib, the course then covers data transformations and dimensionality reduction, namely PCA and Isomap (for non-linear dimensionality reduction). After that, the course covers several important algorithms used in supervised and unsupervised Machine Learning, including K-means clustering, K-Nearest Neighbors classification, Ordinary Linear Regression and Multiple Linear Regression, Support Vector Machines, Decision Trees, Random Forest Classifiers, and a final rush through confusion matrices, cross-validation, pipelining, and tuning parameters with GridSearchCV.

Given that this was the first iteration of the course, my experience was pretty good. The course could use a little more polish, both in the presentation of the online materials, quizzes, and programming assignments. There were minor typos all over the place (though they didn’t really impede understanding of the material), and the quiz questions were rather ambiguous from time to time, much to my consternation. The explanations of the concepts themselves were a little wishy-washy, but when you’re trying to address a general audience there’s little else you can do. Links to the literature, textbooks, and further explanations are included, and should be read as well in order to gain a complete understanding of the subject matter.

The programming assignments were great, however. They were quite challenging, and I think I spent more than the recommended 4-8 hrs/week on the assignments. They were all really interesting, and challenging enough where I didn’t get completely frustrated and give up. We used PCA and Isomap to project 3-D images into 2-D space, K-Means to identify people’s residences based on anonymized geolocation data from their cellphones (!), linear regression to reconstruct audio samples (sort of like what they do on TV shows!), SVC to analyze whether or not someone has Parkinson’s based on collected speech quality, and other interesting examples.

I would recommend people taking this course do as I did, and use it as a supplement for other courses. There’s no way you can learn everything there is to learn about a subject as broad as “Data Science” from one course, and it is good to take multiple courses because some of them explain certain concepts better than others. For example, this course covers Isomap, which is something that most other courses do not.

If you’re curious about the programming assignments, I’m posting them in my github.

May 17, 2016

On learning to code

Filed under: Coding, Data Science, education — sankirnam @ 11:05 am

Last week, the following article was published in TechCrunch: Please don’t learn to Code. This was swiftly followed by Quincy Larson’s reply, Please do learn to code.

For those who don’t know, Quincy Larson is the founder and director of FreeCodeCamp, an online programming education website that is disrupting the traditional paradigm of teaching programming/ CS. I’m going through it myself, and highly recommend it for anyone who wants to learn programming – the front-end web development curriculum is very well done, and it walks you through HTML, CSS (including responsive design with Bootstrap), JQuery, and JavaScript. Even if you do not necessarily want to go into webdev, this is a good place to start; it has you make projects to really cement your knowledge. Until I did this program, I had no idea how to make a website from scratch with HTML and CSS!

In any case, with regards to the articles I linked at the beginning, I am siding with Quincy Larson on the issue. Computers and digital devices are ubiquitous in our lives nowadays, and we spend at least 5 hours or more (a very conservative estimate) a day interacting with computers, whether it is in the form of desktop computers, servers, laptops, tablets, or mobile smartphones. Knowing how to use these devices is one thing, but that is the bare minimum; if you want to be truly productive in today’s society, you need to be able to get these devices to work for you, and that is where a knowledge of programming comes into the picture. In addition, with the rise of machine learning and increased automation, we’re beginning to see an increased number of jobs that were traditionally done by humans now being done by computers. This automation is beginning to seep into areas that are considered “high-skill”, such as organic synthesis. Thus, it’s like I say nowadays:

You don’t want to lose your job because someone else automates your position, right? You would rather be in a position where you automate someone else’s job. The only way to ensure that you are in the latter position is to learn programming/computer science.

The beauty of the field of programming/computer science is that it is extremely egalitarian, compared to other fields. In the programming arena, people care only about what you’ve done, what you’ve accomplished, and whether you know your stuff or not; educational pedigree is largely irrelevant. Contrast this to a field like organic chemistry, where if you do not have a degree from MIT/Caltech/Harvard/Stanford/Berkeley your resume will be swiftly thrown in the trash. This is why, in CS, it is now accepted that a GitHub profile is the new resume.

In other news, I have been applying to bootcamps for the last few weeks, in order to have something do this summer given that the job situation in organic chemistry continues to remain abysmal. I know I have been scornful of bootcamps and “data science” in the past, but my reason for applying to these places is simple. I could learn the material on my own for free (or a significantly reduced cost), but it would take a long time – at least a year or two. If I can accelerate the process and learn everything in 12 weeks, then it is worth the extra cash, and after all, time is the most valuable asset we have in our lives. This video explains it pretty well:

After interviewing at several places, I was accepted to Codesmith, Logit Data Science, and Dev Bootcamp. I’ve decided to go with Logit Data Science simply because it makes more sense given my background; going into full-stack web development is orthogonal to my past education. There are pros and cons to all decisions; Logit is cheaper, but I’m going to be in the first cohort, so it remains to be seen how good the program is going to be. Also, given that my CS, math, and statistics backgrounds are very minimal, I’m anticipating that this is going to be extremely challenging. But sometimes, succeeding in life is all about risks and taking that first leap of faith! Codesmith is a little better established; they’ve been around for a year. I visited their campus/office a couple of weeks ago in Playa Vista, and was very impressed. The atmosphere is quite relaxed, but I did feel the “work hard, play hard” spirit there. The CEO, Will Sentance, is one of the main instructors there, and his teaching style is absolutely fantastic. He explains all the concepts thoroughly and clearly, and his enthusiasm for the subject is infectious. If you’re considering joining a full-stack bootcamp, I highly recommend Codesmith – do check them out! They are up there with Hack Reactor in terms of quality of instruction and overall experience.

May 11, 2016

Review of DavidsonX D001x

Filed under: Chemistry, education — sankirnam @ 11:49 am

I recently completed the above course on EdX; the full title is:

DavidsonX: D001x Medicinal Chemistry: The Molecular Basis of Drug Discovery

I have taken biochemistry and medicinal chemistry/drug discovery courses several times in the past during my undergrad and graduate studies, but the quality and coverage of the topics in this course was far, far better than anything I had taken to date. Major kudos to Prof. Erland Stevens (the instructor) for doing a fantastic job. I highly recommend this course to anyone with an interest in medicinal chemistry or drug discovery (even if you’re not interested in doing it for a career).

The course starts from the basics and assumes a basic knowledge of organic chemistry and algebra/arithmetic. The organic background is required just so you know the basic rules of structural organic chemistry; there are no complex synthetic schemes or mechanisms in this course. As far as reactions are concerned, the only reactions really touched upon were those involved in oxidations by the liver.

The math required is tedious but not terribly overcomplicated; the majority of the questions involved calculations with Michaelis-Menten and Lineweaver-Burke plots, or using IC50 values and the Cheng-Prusoff equation. These can all be done with Excel or Google spreadsheets, and Prof. Stevens demonstrates clearly how to do it, but that still doesn’t reduce the tedium of going through the calculations. The main thing to watch out for when doing calculations is that the EdX system only gives you 1 or 2 opportunities to input the answer before closing it off, so you have to make doubly sure to check your math and make sure the answer is correct!

This was my first proper exposure to the concepts of ADME (Absorption, Distribution, Metabolism, Excretion), which is central to pharmaceutical science. Each of these concepts was covered in detail; for absorption, the main methods of delivery that were covered were the IV bolus and oral ingestion, although one should keep in mind that these are only 2 out of the many ways of getting a compound into the body (others include suppositories, inhalation, transdermal diffusion, etc.). Distribution covered the basic “one-compartment” and “two-compartment” models, different ways of thinking about how a compound gets around the body. In this case, the bloodstream can be thought of as a “compartment”, and the fatty tissues as another compartment, both with different volumes, and so the concentration of the compound in each will be different. The blood-brain barrier was not touched upon in this course; this is a topic of personal interest to me, and it can be thought of as a barrier that divides one of the compartments.

In the context of drug discovery, Prof. Stevens gave an overview of the major approaches used in the industry today, such as combinatorial chemistry, HTS, peptidomimetics, fragment-based and phenotype-based screening, and natural products. Functional group replacements and isosteres were also discussed, common ones being replacement of a -CH3 group by a -Cl and tetrazole for a carboxylic acid. As an organofluorine chemist, I know that it is common to use -F substitution in this regard, but this was not really touched upon. The lecture on SOSA (Selective Optimization of Side Activities) was a little confusing; Prof. Stevens used the classic example of the discovery of Viagra’s ability to treat erectile dysfunction to illustrate this, but I remember being misled by the associated questions. This could be improved in future iterations of the course.

The cool thing about the course was the Virtual Lab component, and this really brings to life the concepts taught in the course, allowing the students to see the challenges that medicinal chemists face. Using the “bioactivity predictor” feature of Molinspiration, one can input the structures of small organic compounds and conduct a rudimentary screen against some receptors, and further details can be interrogated with the admetSAR website. The challenge in these labs was to design molecules with an affinity for a certain receptor (as predicted by Molinspiration), but still having good ADME properties. This is really fun to play with, as it gives a sense to the interplay between organic structure and function (which is what structure-activity relationships (SARs) are all about).

This course is a great introduction to medicinal chemistry, and I highly recommend taking it in the future, if it is offered again. After this, one can dive into the medicinal chemistry literature (e.g. Journal of Medicinal Chemistry or BMCL) and gain a deeper understanding of the topics covered in the course and how they are actually being used right now.

March 5, 2016

PhD Job Prospects

Filed under: Chemistry Jobs, Data Science, education — sankirnam @ 3:44 pm

A friend of mine sent these two articles and asked for my comments on them.

  1. A bridge to business
  2. Enterprising science

The first article talks about how valuable PhDs, postdocs, and PhD candidates are to management consulting firms. It goes into detail about the training that a lot of PhDs receive while working towards their degree, and that their training is just as valuable as what MBA’s receive.

Now, it all sounds nice on paper, but my experience has been the polar opposite. I applied to several consulting firms last year and was either soundly rejected or received no response (which is quite common when applying for jobs online), and this is in spite of being one of those “[valuable] Science-PhD holders” the author talks about. So I really have no idea what management consulting firms are looking for.

The author also states:

“The broad set of valuable transferable skills that you developed while in graduate school go largely unrecognized and unarticulated within the academy. Most PhD graduates restrict their job searches to what they feel qualified to do, rather than exploring what they are capable of doing.”

Again, this trope sounds nice on paper, but my experience with applying for jobs has been quite the opposite. The whole idea of “transferable skills” only really holds in the tech industry, and that too for a small set of subjects (more on this in a moment).

The second article mentions that early-stage scientists (such as assistant professors, post-doctoral fellows, and PhD students) should also look into commercializing their successful ideas and forming start-up companies. This is solid advice. The article also mentions that professors are also not the best people to be running start-up companies, due to the many demands on their time. That is better left to younger people. Of course, this comes with a caveat.

Applied sciences, engineering, and computer science are by their nature easier to commercialize, as opposed to theoretical or more “pure” fields. Problems that are academically interesting are not necessarily ones that will lend themselves to commercialization once investigated. Another issue is that startups are rarely founded off of PhD research because the interests of the advisor and the student are opposed at that point. The advisor will want the successful student to continue working, generating results and writing papers, while the student will want to leave to start the company. In any case, as the author mentions, it never hurts to allow PhD students opportunities to network with successful people in their field; this will help later when they apply for jobs! Sadly, most schools do a piss-poor job in this regard. In most universities, PhD career services are virtually nonexistent, as are networking events for graduate students.

In any case, back to the subject of transferable skills. From what I have seen, transferable skills are those secondary skills that you might pick up on the course of your degree that are not necessary for success in that field, but can be used somewhere else. For example, most PhD holders would have given talks at conferences at some point. Based on that, “making and giving presentations” can be listed as a skill, even though this something that no self-respecting person would be caught putting on his or her resume. This skill is transferable to other fields where giving presentations is important, such as consulting. I’m not sure if this is a good example or not, but it is what I could think of.

Now, one transferable skill that is being thrown around a lot lately is “data analysis”. The author even refers to it in the first article I linked to above:

“If you have earned a PhD, you know, for example, how to analyse data. You also understand how to examine those results to gain insights.”

The term “data analysis” is beginning to seriously annoy me, because it is incredibly vague. A five-year-old putting his hand on a hot stove, screaming in pain, and then learning not to touch the stove again is doing data analysis! Yet would people call the five-year-old a “data scientist”? Even if others wouldn’t, I would – the kid has used evidence (even if it is a single datum) to draw a conclusion! So yes, in the broad sense, we are all “data scientists” and we all go about our day doing “data analysis” all the time, even if we do it unknowingly!

But the crux of the matter is that the type of data you will encounter varies from field to field, and the types of conclusions you can draw – the analysis, in other words – is domain-specific. In other words, “data analysis” is not a transferable skill. This is a seemingly simple fact that unfortunately is being overlooked by recruiters, employers, and tech workers. For example, I can readily interpret NMR spectra, GC-MS data, and other types of spectra that are commonly encountered in a chemistry lab. However, I would be laughed at if I claimed to be doing “data analysis” in the sense that is used in the tech industry today! What the tech industry calls “data science” or “data analysis” is the statistical interpretation, most often using methods derived from computer science, of large sets of facts or figures that have been compiled. Case in point: Thanks to a friend, I got an interview a few days ago for a “data analytics” position. The HR recruiter who called me was thoroughly confused by my resume, and I had to clarify that even though I had a PhD in science, I had zero skills that they were looking for. She told me “oh yea, we regularly hire people from a variety of backgrounds for this position…we have computer scientists, math majors, statisticians, and even physicists!”. Now, as far as transferable skills go, they probably have a very good command over computer science and programming, as well as a strong mathematics background. These skills are not generalizable to all scientists (just like how I would not expect a PhD computer scientist or statistician to be able to go into a chemistry lab and synthesize small molecules)!

As one of my friends told me,”…well, looks like you have a PhD in an inferior science”.

March 3, 2016

SciHub

Filed under: education, Internet craziness — sankirnam @ 3:17 pm

Well, this just made my day:

https://sci-hub.io/

Thanks to this, I now have journal access again! I cannot overstate the importance of this in my life; just like how most people read the newspaper or news websites regularly to know what is going on around them, so too do practicing, serious scientists read journals in their field in order to keep up with the latest developments. Not having journal access is like being disconnected from the scientific world or community at large, and that is no fun at all. In my case, it’s especially important, as interviewers can sometimes ask, “So, what have you read lately that caught your eye, and why is it significant or interesting?”. Being able to answer with references to the primary literature sends a positive signal that you do take the initiative on your own time to stay abreast of the field.

February 22, 2016

Why no postdoc?

Filed under: Chemistry Jobs, education — Tags: — sankirnam @ 1:21 pm

As I am still in the job hunt, one of the questions I am frequently asked nowadays is “Why aren’t you doing a postdoc?”, as if that is the only logical step after completing a PhD in the sciences. This is second to “So, you’re not looking to go into academia, then?”. Unfortunately, most people do not realize that the two questions are interconnected, due to the incredible competition for academic positions. Most universities these days will only look at your CV if you have received your PhD or completed a postdoctoral stay at a top-5 or top-10 university. This has been the norm in academia for a while now. Interestingly, even people in industry are recommending doing a postdoc in order to differentiate yourself from other PhD graduates. This only means one thing: There is a glut of people looking for jobs at the PhD level, NOT a shortage!

I made a conscious decision a long time ago not to do a postdoc after I finished my PhD. This was driven in part by several factors. One is due to my perception of scientific research. I have mentioned before that a lot of progress in scientific research is actually due to luck, which, especially when it results in a significant breakthrough, is retroactively misattributed to “hard work”, “intelligence”, “genius”, or “insight”. Some of the smartest, most intelligent, and most hard-working people I knew during the course of my PhD actually ended up with zero publications*. Of course, anecdotal evidence is not representative of the larger sample, but seeing the struggles of those friends of mine made me realize that ultimately research is nothing more than gambling. And this is a riskier type of gambling than what you do in Vegas, to boot. In Vegas, you gamble with money. As most people are wont to say, “money comes and goes, and losses are temporary”. I agree. Gambling with money is not such a big deal, because you can always earn it back. On the other hand, when you do research, you are gambling with time, that is, years of your life, which you can never get back! That is the major reason why I decided not to do a postdoc. When conducting research in graduate school, even if your stuff does not work out (which is extremely likely), you can still get the degree (either a MS, MA, or PhD) depending on when you decide to cash in your chips. During a postdoc, you have no such consolation prize or cushion to fall back on, and the outcome of your life is seemingly cast to the vagaries of the goddess Fortuna. I have known several postdoctoral scholars, and their future careers were seemingly independent of the success of their work during their postdoctoral appointment.

As an aside, I am always amused when people, especially the orthodox members of the Tamil Brahmin community to which I belong, strongly hint that I consider doing a postdoc. My amusement stems from the fact that these same people will never consider gambling in Vegas because it is “immoral”, but will have no qualms with recommending that I take on a far riskier gamble with years of my life.

Another reason is that it took 7 years to get my PhD, from start to finish. This was due in part to very unclear expectations from my advisor, and this is something that a lot of PhD students encounter. Towards the end of the degree, there is often a conflict of interest between the student and the advisor. The student wants to wrap up the degree and get out, while the advisor is reluctant to let the student do so, having invested a lot of time and (comparatively, not so much) money in the student. Plus, it is usually the case that the student is most productive towards the end of the PhD, as it takes time to learn the ropes – getting to know the field well, including what problems are worth going after (in terms of the effort:reward ratio), as well as gaining expertise in that particular field’s research methodology (whether experimental or theoretical). In any case, that friction of interest between the two parties can sometimes lead to undesired consequences, such as an unnecessary extension of the time to degree. I still vividly recall that after my thesis defense, in the private discussion with my thesis committee afterwards, the professors remarked that my work was very impressive not just in quality, but also in quantity, with the volume of work I had written up suitable for two theses!

I am still utterly nonplussed to this day.

I will admit that I am burned out from academia after having been in a PhD program for 7 years. If I had been able to finish my PhD in 5 years or less, then yes, I would have the energy and motivation to go to a postdoctoral appointment. But apparently, these feelings are not appropriate, and I should still be rearing to go back and work 80-90+ hour weeks as a postdoctoral fellow for a take-home salary less than what I made as a PhD student.

And finally, as far as the availability of jobs in the chemical and related industries in the US goes…they’re shrinking, the chemical industry is contracting here, and the writing has been on the wall for the last couple of years. I haven’t quite lost hope yet that I may be able to get a job in chemistry, but if it comes down it it, I am willing to retrain and leave the field. Doing a postdoc will cause me to lose flexibility should I need to leave chemistry in the future (which is looking increasingly likely).

So there you go. I hope this answers the question “Why aren’t you doing a postdoc?!?” to everyone’s satisfaction.

*On the flip side, I knew a member of our chemistry department who published 0 papers during his/her PhD and gave an extremely mediocre thesis defense (I say this because another professor who attended the defense and was not on the student’s committee, was tearing it apart for a good half an hour afterwards). Nonetheless, the student was allowed to graduate, and to everyone’s surprise, was able to secure an extremely competitive postdoctoral appointment at a Nobel Prize winner’s lab! A year later, another friend of mine, who was praised unanimously by everyone in our department as being a “rock star of organic chemistry”, was turned down by the very same professor when he/she applied for a postdoctoral position there, even though this student had published 10+ papers in excellent journals, including a couple of reviews and book chapters. Witnessing these events caused me to lose faith in the belief that academia is a meritocracy.

October 8, 2015

Coursera Business Foundations Specialization

Filed under: education — Tags: , — sankirnam @ 2:17 am

So I’m currently taking the aforementioned series of courses on Coursera. They are offered by the Wharton School of Business, at the University of Pennsylvania. I finished the first course, Introduction to Marketing, in August, and am currently taking Introduction to Operations Management. I’m paying for Verified Certificates for all these courses, so that I can hopefully get something tangible out of this; hopefully this will help when I apply to MBA programs in the future.

The Marketing course was very good overall. The quality of the video lectures was excellent, and the topics covered were enthusiastically discussed by the professors in the course. I was under the impression that this was a “softer’ subject, in the sense that there is a lot less quantitative data research in these fields, but I was pleasantly surprised. The lecture did touch on the concept of the “Long Tail”, which was first discussed by Prof. Chris Anderson (UCLA). I’m also re-reading Nassim Taleb’s The Black Swan now, and he also mentions the long tail concept and discusses it in length there.

I do have a few gripes, however. The course covered the market segmentation concept “PRIZM”, and one of the quiz questions asked how PRIZM segments markets. The way PRIZM works is not through “geographic” segments, and yet that was the answer! The really correct answer, which was actually discussed in detail in the video lectures, is “demographic” segmentation!

Also, check out these spectacular gaffes from the final exam:Screenshot 2015-08-27 15.23.58

I guess the course admins and the professors were feeling generous, so they improved the odds of getting that question correct from 1/4 to 1/3!

Screenshot 2015-08-27 15.25.04

I remember reading a lot of confused and angry posts from students on the course discussion forums about question 40. The correct answer to the question is there, but it is still hugely misleading to see “Correct option 3” and “Correct option 2”. Hopefully Coursera has fixed this in the future iterations of the course.

December 11, 2014

well, I am done!

Filed under: education — Tags: — sankirnam @ 8:18 am

Just a few hours ago I successfully defended my PhD thesis! While the defense itself did not go as smoothly as I had planned (I ended up including too much stuff and going a little overtime), it still went well. I didn’t practice at all since at that point I honestly didn’t really care either. But anyway, what’s done is done, and it is time to move forward with life.

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