Online Course Review: Coursera and Stanford University's Mining Massive Datasets

It's been quite a while since I last posted—and eight months since I finished the class I'm reviewing. As it happens, I finally got a job as a data scientist in late January, and work has kept me busy. That job will be the subject of my next post, but right now, we're talking about Mining Massive Datasets, offered by Coursera and three professors from Stanford University, Jure Leskovec, Anand Rajaraman, and Jeff Ullman.

The seven-week course covers the same ground as the trio's book Mining of Massive Datasets, though in much less detail. (That link, incidentally, is to the e-book; if you really want the hardcover, you're welcome to follow this sponsored link to Amazon.) It's now been offered twice on Coursera, with a third iteration set to start on September 15th. Oddly, I never intended to take this class: a friend of mine was interested in it, and I signed up so that we could take it together, but initially I had passed on it. I had taken a look at the schedule, seen a few topics that I had covered before in other courses, and decided that I wouldn't get a lot out of it.

What I missed, in my hasty scan of the course description, was that Mining Massive Datasets is not the typical data science course that shows students how to put useful algorithms into practice through code. Instead, this course is about the algorithms themselves: how they work, why they work at scale, and how they've been modified to improve performance or cover different situations. There is a lot of math: this is not a course for someone without a solid background in calculus and linear algebra (it's not like you need to remember how to integrate esoteric functions—but you do need to understand the basics). There are not, on the other hand, any programs to write: many of the exercises absolutely can't be done without writing short scripts or using a statistical language from the command line, but the professors don't require any specific language, and the code isn't graded, only the answers. The point is not to create functioning implementations of the algorithms in question, but rather to understand the nuts and bolts of how they work. The exercises are challenging, and sometimes require consulting the e-book, especially when the complexity of a topic makes it hard to grasp in a short lecture.

Although the bulk of the course is devoted to algorithms, Week 1 provides an excellent description of how HDFS and MapReduce work—without ever giving details on Hadoop or the various languages used to write mappers and reducers. In fact, I came away from Mining Massive Datasets with a far better conceptual grasp of distributed file systems than I got from the Udacity course devoted entirely to the subject. (See my review of that course here.)

It should be said that Jeff Ullman is an excruciatingly monotonic lecturer, who sounds like he's reading everything directly from notes—and you likely wouldn't be at all surprised if he suddenly called out, "Bueller...Bueller...." In addition, his explanations are not as clear as those of Leskovec and Rajaraman. In fairness, though, Ullman tends to cover the most complex topics in the course, and I was always able to figure things out by consulting the book (which covers the material in greater depth, anyway).

In summary, I strongly recommend Mining Massive Datasets for anyone who wants to understand the nitty-gritty of algorithm design for big data. The course is not, however, for the faint of heart. You could make a very successful career in data science without ever taking or it taking anything like it—but taking it will certainly make you better at the profession.

Online Course Review: Udacity's Intro to Hadoop and MapReduce

For my first course on Udacity, I decided to take Intro to Hadoop and MapReduce, a course created in conjunction with Cloudera, a company whose business model is based on the open-source Apache Hadoop. To sum up my asseessment, the course was useful, but could have been done much better.

The four-lesson course (short by Udacity standards) is supposed to take about a month to complete—like all Udacity course, and unlike those of Coursera, this is not a true MOOC, taken alongside other students in real time, but rather an interactive tutorial. However, Udacity's model does feature student discussion forums; customers who pay (at the rate of $150/month) also get help from live coaches, feedback on their final projects, and the opportunity to earn a "verified certificate", similar to Coursera's Signature Track, with the difference that Udacity, unlike Coursera, no longer offers certificates for non-paying students. (As I've mentioned before, a verified certificate and two dollars may buy you a cup of coffee, but I wouldn't count on its having any greater worth.)

Before I delve into the specifics of this course, let me say that I'm not a real fan of the Udacity interface. While both providers break each lesson up into a series of short videos, Coursera labels each of those videos with a topic, making it relatively easy to go back and find the material you need; by contrast, Udacity strings all the videos for a particular lesson together under a single heading, and so you have to hunt through all of them to find something (you can click on individual videos, and each one has its own label, but you have to click on or hover over a video to see the label). In addition, whenever the video stops for a quiz, it drops out of fullscreen (assuming you're in fullscreen, of course). Moreover, Udacity's discussion forum (note the singular there) has no organization whatsoever, aside from keyword tags—making a search for specific information rather laborious.

Thr first three lessons of this particular course, which features two instructors from Cloudera, are structured in a manner that the director of a music video would appreciate: many of the videos are very short, and switch jarringly from one instructor to the other. Nonetheless, the instructors are engaging, and there's a nice interview with Doug Cutting about how he helped to create Hadoop, and named it after his toddler son's stuffed elephant. The first two lessons, which explain the basics of how Hadoop and HDFS work, can best be described as "lite"—unchallenging nearly to the point of tedium.

Lesson 3 marks an abrupt change: this is where the programming exercises began. The class requires previous experience with Python, which I lacked, and so the exercises took more time for me than they should have, but I managed. One student in the forum questioned whether this was a course on Hadoop, or a course on Python regular expressions, but doing the exercises helped me learn some Python, and, much as I hate the language, it does have a very powerful vocabulary of regular expressions. Unfortunately, the instructor blew by the concept of Hadoop streaming so fast (in Lesson 2) that I wasn't entirely sure for a while what exactly I was doing, though I was managing to get it to work—and once I looked up Hadoop streaming on my own (it is, for the record, an API that allows Hadoop mappers and reducers to be written to be written in any language), I realized that the interface would work just as well for R.

Although the simpler exercises use an online Python compiler, for the exercises that require large datasets, the course's creators deserve kudos for having students install a virtual UNIX box on which a virtual two-machine Hadoop cluster has already been set up, and then manipulate data and write code in this realistic environment. Unfortunately, the exercises that require this virtual machine seem half-baked.

First off, the instructors haven't actually detailed how to write and execute Python scripts on the UNIX machine (the class discussion forum was very helpful here). Second, the syntax needed to make the scripts work is different from the syntax presented in the video lectures (though, fortunately, there are working sample scripts saved on the virtual machine). Third, and most seriously, one particularly tricky exercise requires knowledge that students could not possibly get from the instructions, or, in all probability, the data itself, but could only get from the hints that emerge from a trial-and-error process of submitting answers to the automated grader—it was an interesting little mystery to solve, but there are no automated graders in real life, and so I'm not sure what I gained from the effort.

Yes, figuring out ambiguous instructions does have some pedagogical value, and in the end, completing the exercises was very satisfying, but, especially in the case of the problem that was insoluble without the automated grader, I got the feeling that the difficultes I faced were the result, not of a pedagological choice, but of a simple lack of effort on the part of the instructors—and I felt like I had wasted part of my time.

According to posts in the forum, Lesson 4 was not part of the original class, though I'm not sure if it was planned all along, or tacked on later. To paraphrase Monty Python and the Holy Grail, the course was completed in an entirely different style at great expense and at the last minute. The lectures feature a different intstructor, a Udacity employee, in place of the Cloudera instructors. This lesson covers design patterns, specifically filtering patterns (more regular expressions), summarization patterns (minimums, maximums, and means, for example), and structural patterns (combining data sets); one lecture also deals with combiners, scripts inserted between mappers and reducers to make things more efficient by doing some of the reduction locally on each machine in the cluster.

I found these lectures better than the previous ones, and the exercises better prepared. I will say, though, that I eventually got bored with writing new and different regular expressions in Python, and didn't finish the last few exercises (or the final project, which isn't graded for non-paying students in any case), though I did watch all of the lectures.

In the end, this half-baked pastiche of a course at least gave me a decent idea of how Hadoop works, and removed the mystique of manipulating data stored on a Hadoop cluster. I wouldn't know how to set up a cluster myself (that wasn't the intent of the class, though I don't think it would be all that hard to do), but I do know how to use Hadoop streaming—and I've realized it's not exactly rocket science.

Online Course Review: Exploratory Data Analysis, from Coursera's Data Science Specialization

Back in May, I reviewed two of the short courses that make up Coursera's Data Science specialization. Although the four-week format greatly limits the content of any one course, I was generally impressed by the scientific approach of the specialization (something all too often lacking in data "science"), and, in the case of Getting and Cleaning Data, by the many pointers provided to R packages and sources of information for further study: the course may not have gone into a lot of depth, but it provided a good overview of what you can do with R.

I recently completed a third course in the specialization, Exploratory Data Analysis, taught by Roger D. Peng (the previous courses I took were taught by Jeff Leek). While I enjoy Peng's lecture style (unlike Leek, he engages the audience by showing his face at the beginnings of lectures), and I learned a lot, the course suffers greatly from the short format.

I initially overlooked this class: from the name (more on this in a minute), I never would have guessed that 3/4 of the lectures would cover graphics in R. Peng teaches the basics of the language's three major graphics packages, the base graphics, lattice, and ggplot2. As is the case for Getting and Cleaning Data, the lectures manage only to skim the surface, particularly for ggplot2, but they do give the student a decent idea of what's possible in R. I do though think that for ggplot2 Peng could do a better job of outling the advanced features than simply pointing students to the book written by the package's author, Hadley Wickham (thankfully, it's possible to find free PDF's of the book online, but I'm not sure it's where I'd want to start for solving a discrete problem, rather than studying ggplot2 in a methodical way).

So what's with the name of the course? Peng presents visualization in R as a way of conducting initial exploration of data, but it's obviously useful for more than that, since R can create decent visualizations of the results of analysis. I suspect that the course name was chosen so that one week of lectures on clustering and dimensionality reduction could be shoehorned into the syllabus. This material probably belongs instead in the Pratical Machine Learning course, but something had to be cut to limit that course to four weeks (cf. the nine-week Machine Learning, also offered by Coursera, and which I've reviewed previously—twice, actually). The fact that clustering and dimensionality reduction can be used for exploratory analysis and visualization is the only thing that ties the entire course together.

What's particular disturbing is the way that all of this combines with the specialization's unique approach to exercises and evaluation. Each course includes a hands-on project, and, because open-ended projects in a MOOC must, for logistical reasons, be graded using a peer grading system, the final project for Exploratory Data Analysis only ends up covering material from the first two weeks of the course, since students need the third week to work on the project, followed by the fourth week to grade it; therefore, half the content of the class doesn't play any role in the project. On top of this—I suppose to avoid overloading students—there's no quiz, homework, or any other form of practice or evaluation covering the material on clustering and dimensionality reduction, which makes it hard for a student to know if he or she really understands those topics.

To sum up, I did find the information on data visualization in R useful, but I would have appreciated a full four weeks on the subject. The coverage of clustering and dimensionality reduction was out of place in the course; nonetheless, many will find it valuable (I had already seen most if not all of it in Machine Learning and another Coursera course, Social Network Analysis, which I've also reviewed).

I do have one more comment, though this applies to the Data Science specialization in general, and to Coursera, rather than solely to this course. Normally, after completing a Coursera course, a student can go back and look at the course archives at any later time; I've found this valuable when I suddenly find myself needing to refresh my memory or find out where I can learn more about a topic. Coursera has apparently disabled this feature for the Data Science courses: their archives are no longer accessible after the grading period is over (about a week after the finish of a course). I say "apparently" because, when I contacted Coursera a few months ago to ask why I could no longer access the archives of Getting and Cleaning Data, I never got a response—this is becoming something of a theme with Coursera, which, as I noted in my second review of Machine Learning, ignores most bug reports for that class. I suppose that paying customers might get better service, but I'm not going to pay just to find out if that's true.

Of course, you can always sign up for the current iteration of a class, since they're offered continuously, but it's annoying to have to do that each month. Fortunately, all of the class materials are also available in a GitHub repository, but it's not as easy to display documents on GitHub as in Coursera's web interface. For a set of courses that only skim the surface, and whose major value is in providing links to deeper information, this is a major failing.

Online Course Review: Coursera's Machine Learning, Part 2

Back in October, I reviewed Coursera's Machine Learning course, taught by Stanford professor and Coursera co-founder Andrew Ng. As I mentioned when I first reviewed the course, I wasn't able to finish it, because I was starting a new job and moving halfway across the country. I've just been able to complete the most recent iteration of Machine Learning (which was nearly identical to earlier versions), and I'd like to add a few more thoughts to my original review.

My last time through the course (the session that began on April 22nd, 2013), I completed almost all of the lessons on supervised machine learning methods, such as regression, logistic regression, and neural networks. In this session (which began on March 3rd, 2014), I repeated those lessons, and also finished the rest, most of which covered unsupervised techniques, such as clustering and recommender systems. I won't repeat the contents of my earlier review, except to note that what I said then remains true: Andrew Ng is a clear and charismatic lecturer, he covers advanced techniques, and he provides a number of practical tips, but the programming exercises are a bit canned, and may not fully prepare students to write their own scripts in Octave.

My new comments mostly reflect comparisons to other MOOC's, particularly the two courses from Coursera's Data Science specialization that I took recently. First of all, I think that Machine Learning could do more with the online format. In fact, most MOOC's consist largely of video-recorded lectures, with the addition of a sprinkling of interactive content, but Machine Learning falls short even by comparison with other online courses. The class does feature a very effective automatic grader, but it lacks any links to additional resources, or, very importantly, notes or slides from the lectures. While the latter omission may seem trivial (I didn't notice it the first time I took the course), a lack of lecture notes makes it difficult to go back later and review material from a lecture, except by watching the whole thing again. It's true that the programming exercises include detailed instructions, but not all of the course's topics are covered by these exercises, and at any rate the organization of the instructions can make it difficult to locate information on a specific subject.

I might also amplify my comment from the earlier review that the programming exercises involve mostly copying and pasting, rather than writing entire scripts. There's a reason for this: the focus of the course is on algorithms, not on other parts of solving machine learning problems. Nonetheless, my experiences taking other courses, especially those from the Data Science specialization, have demonstrated the practical value of forcing students to think about the nuts and bolts of a research project. Machine Learning's lack of a big final project also arguably deprives students of valuable practical experience, especially since these projects usually require students to explore the course material in greater depth than do short exercises; on the other hand, the fact that a final project can only cover a single topic from the course—or at most a handful of them—calls the value of such projects into question.

My final concern is that Machine Learning seems to have gone on autopilot at this point, with little or no attention from Ng or anyone else who helped him prepare the course materials. Questions in the discussion forum are answered instead by "Community TA's", that is, volunteers who took earlier sessions of the course. Most disturbingly, the majority of reports of errors in the course materials go unanswered, and those that are answered are answered by Community TA's, who lack the ability to fix the errors. For example, a month ago I discovered that the automatic grader accepted one version of my code and rejected another, even though the two versions were algebraically equivalent. My report of this apparent bug still hasn't been answered.

Despite these concerns, I still heartily recommend Machine Learning as a valuable starting point for anyone interested in data science. While the course was offered twice in 2013, the start date of the next iteration, on June 16th, 2014, suggests that Coursera may be planning to offer sessions of the 10-week course almost back-to-back, meaning several sessions each year.

What's next for me? I'll soon be posting a review of Udacity's short Intro to Hadoop and MapReduce. After that, I'm considering taking two more courses from the Data Science specialization, first Exploratory Data Analysis, which will give me some practical experience with graphics programing in R, and then Practical Machine Learning, which will provide experience using R for machine learning, as well as a basis for comparing the machine learning course reviewed above (though the course for the Data Science specialization, at four weeks, is much shorter, and can't possibly cover the same ground).

In the meantime, while I'm still looking for work as a data scientist, I've had a number of interviews, and some of the potential employers have read and commented positively on this blog. I hope that provides an example for other social scientists out there that, yes, you can become a data scientist.

Online Course Reviews: The Data Scientist's Toolbox, and Getting and Cleaning Data, from Coursera's Data Science Specialization

I recently completed Coursera's The Data Scientist's Toolbox and Getting and Cleaning Data, two courses that form part of the online learning provider's new Data Science specialization, taught by Brian Caffo, Jeffrey Leek, and Roger D. Peng, biostatistics professors at Johns Hopkins University, and, in the cases of Leek and Peng, authors of the Simply Statistics blog. Both of the courses I took were taught by Jeff Leek (referred to in my earlier post today). I found Getting and Cleaning Data to be an especially useful course, teaching some practical skills that are quite essential to the real-world practice of data science. However, I probably wouldn't recommend the entire specialization to anyone coming from the world of quantitative research in academia, since a big focus of the program is teaching the scientific method and the logic of statistical inference—that is, things a quantitative social scientist should know already. First, however, a little background on Coursera's specializations....

Coursera has recently introduced a handful of "specializations", each consisting of a series of short courses followed by a capstone project. The specializations continue Coursera's effort to monetize its offerings through the Signature Track, which offers a "Verified Certificate" for those who pay a fee (typically about $50-$100) to take the course.

The Signature Track in itself has dubious value. Allegedly, its purpose is to provide a more useful credential than the certificates Coursera has traditionally offered for its free classes. To make the Verified Certificate more useful (that is, more impressive to potential employers), Coursera takes measures to guarantee you did the work yourself, but these measures seem fairly easy to circumvent. Specializations add an additional sweetener: if you take every class in the specialization on the Signature Track, you can then take the capstone project (offered as an additional class), which is not available to students who take the courses for free (or even, for that matter, to students who pay for only some of the courses). Students completing the specialization also receive a specialization certificate.

The Data Science specialization includes 10 short (four-week) classes, including the capstone, each priced at $49 for the Signature Track. If you stump up the whole $490 at once, you can take any of the courses as many times as you like over the next two years (in case you don't pass the first time); if you pay for the courses one at a time, you can only retake each one once (which is probably enough—honestly, if you can't pass one of these classes, you probably don't belong in the profession, but sometimes life gets busy, and you can't finish the work for a class). Each of the first nine courses will be offered once a month; the first six are available already, and the remaining three will be offered for the first time in June. For a couple of the classes, there's also an option to substitute an alternate course on Coursera. The capstone has yet to be schedule (word in the forums has it that it'll be offered in fall), and I'm not sure how often Coursera plans to offer it.

The course that really interested me was Getting and Cleaning Data, but I signed up for The Data Scientist's Toolbox because it's required for the rest of the specialization; R Programming is also required, but I already had some experience with R, and I had no intention of completing the entire specialization, and so I skipped this one. Taking one of the later courses at the same time as the required intro course didn't pose any difficulties for me, but I think that someone who has no experience with R would probably want to complete that class before tackling any of the others.

Much of The Data Scientist's Toolbox is devoted to introducing the topics of the specialization's other eight courses; frankly, you can skip this if you don't intend to take those courses (or possibly, even if you do intend to take them—you will, after all, cover that information later, though if you're taking the whole specialization, you may need to watch the video lectures in question in order to complete the quiz for Week 1). For me, the most useful content of this class was its introductions to Git, GitHub, and RStudio (I had been using the plain old R Console, and RStudio makes things considerably easier). RStudio is required for the programming necessary to complete the assignments in the later courses, and Git and GitHub are necessary to complete the projects at the end of each course (you have to upload your work to GitHub so that other students can peform peer assessments on it). For the sake of full disclosure, let me say that I skipped the introductory lectures in Week 1 of this course (though I did pass all the quizzes), and did not complete the course project, which consisted of taking screenshots to prove that you'd installed Git, GitHub, and RStudio (I installed all three, but I wasn't really concerned with getting the course certificate).

I found Getting and Cleaning Data invaluable. I took the course because I wanted to learn how to get data off the web. For example, in the project I did for Coursera's Social Network Analysis last year, I ended up saving data from several hundred web pages by hand, which is not a particularly efficient way of doing things. Getting and Cleaning Data promises to teach students how to extract data from common data storage formats (including databases, specifically SQL, XML, JSON, and HDF5), and from the web using API's and web scraping. The syllabus also includes tips on using R to clean and recode data, and, in the last lecture, a long list of links to sources of data. It's also worth noting that the style of the video lectures is a bit different from those of other classses I've taken: there's never any video of the instructor, just the instructor's voice over the lecture notes.

Initially, I was skeptical, because most of the lectures amount to little more than a list of R packages, functions (with a few short examples), and links for further information. The information blows past you so fast that there's no hope of remembering much of it. However, the lecture notes (in both HTML5 and PDF—the HMTL5 is a little awkward to navigate, but the links work, unlike in the PDF) provide a wonderful resource that you'll find yourself referring to again and again. I've often found that the hardest part of a project is knowing where to start, and the lectures in Getting and Cleaning Data point you in the right direction; in fact, I'm using information from the lectures on web-scraping and JSON right now to do an updated version of my project for Social Network Analysis, a statistically informed visualization of which cards in the game Android: Netrunner appear together in the decks designed by players. Look for that to be posted here soon!

Among the data science courses that I've taken online, Getting and Cleaning Data is the first one that taught me how to go out and get data and then put it in a form that's usable for analysis. By contrast, Coursera's Machine Learning, taught by Stanford's Andrew Ng, provides highly practical advice on selecting and using algorithms, but does so uses very much canned programming exercises, in which the data has already been collected and processed. In fact, the two course are highly complementary, at least inasmuch as they give you ideas about how to handle different stages of a data science project. It should though be noted that Machine Learning uses Octave (essentially the open-source version of MATLAB) rather than R; the Data Science specialization includes its own (much shorter) Practical Machine Learning course, as well as an earlier course on Regression Models that delves far more deeply into that topic than does Machine Learning.

I should add that, for this class too, I never completed the final project: it looks like a highly practical exercise, but I was short on time, and more interested in my own project; again, I didn't care much about earning a certificate, with my main concern being to learn the nuts and bolts of getting data from the web.

Finally, let me offer a few comments on the Data Science specialization as a whole. I would not recommend completing the entire specialization for anyone who's well-versed in statistics and the scientfic method: if you're a competent social scientist (as opposed to someone who took one stats course as an undergraduate), you already understand important issues like sampling, causal inference, and reproducibility (though, admittedly, I've read more than a few articles by social scientists who evidently had shaky grasps on these concepts). For a specialization that labels itself as "Data Science", there's also scant coverage of databases. That being said, anyone interested in data science might find Getting and Cleaning Data, R Programming, and Practical Machine Learning useful, and for someone who doesn't have a background as a quantitative researcher, I can't recommend this specialization's focus on the scientific method and applied statistics highly enough.

Online Course Reviews: Coursera's Machine Learning and Probabilistic Graphical Models

Whoops, I haven't posted in a while.

In May, I started a new job. It has nothing to do with data science, but it has given me experience in supervising other writers, and it's also kept me quite busy. The fact that work kept me busy explains why I haven't posted recently. It also explains the one caveat I have to add to the reviews I'm about to give you: I was never able to finish all the material for either course. I got busy with the new job and moving my family into a new house, and by the time I came up for air, it was too late to finish.

As I mentioned in my April post, I signed up for Coursera's Probabilistic Graphical Models, Machine Learning, and An Introduction to Interactive Programming in Python. I dropped the An Introduction to Interactive Programming in Python almost immediately, after realizing that the course's focus on programming video games made it not as useful for my purposes as I had hoped.

Probabilistic Graphical Models was taught by Stanford Professor and Coursera co-founder Daphne Koller. Coursera hasn't yet listed a new iteration of it, but if the previous pattern holds up, it should be offered again next year. As I mentioned before, I took this course because it includes Bayesian and Markov models, both of which show up in many job ads for data scientists. I decided not to take the optional programming track, figuring that it probably wasn't a good idea to be writing programs for two different courses in a language I was just learning (both this course and Machine Learning use MATLAB and/or the very similar Octave).

Machine Learning was taught by Andrew Ng, also a Stanford professor and Coursera co-founder, and is one of Coursera's best-known and most popular courses. It's also been taught by the University of Washington's Pedro Domingos, but Ng's version will be offered again starting October 14th. I signed up for the course because machine learning is one of the basic skills of data science, but I also wanted the chance to learn one of the most commonly used statistical programming languages, MATLAB/Octave.

As I said in my last post, Daphne Koller is not the most charismatic lecturer, and her explanations can be confusing. What I didn't say last time is that I don't think Koller entirely understands the medium in which she's working. In the classroom, asking questions of the professor can make up for a confusing lecture; Koller seems to be giving the same lecture she would give in the classroom, but without the opportunity to stop her and ask questions about each topic before moving on to the next, that same lecture doesn't work very well.

While the lectures are less than ideal, the quizzes are particularly troubling: rather than presenting a simple test of the material covered in the lecture, the quizzes ask students to move beyond the lecture material, drawing out implications on their own. Asking students to do this is a great pedagogical technique, especially in a graduate-level class. However, it works a lot better when the students have discussed the material in class, giving them the opportunity to start down that path together, with the professor's guidance. None of this is possible in an online class, and, even with discussion forums, rules that prevent students from providing answers to one another prevent full exploration of the quiz topics; part of the problem is that students can see the quiz questions before beginning their discussion, rather than receiving a quiz or homework assignment only after the classroom discussion is over.

It might help to begin each quiz with more straightforward questions, giving students a little practice, before moving on the ones that require additional thinking. Far from adopting this model, Koller actually exacerbates the problem by adopting an unusually strict rule (by MOOC standards) for retaking quizzes: any attempt after the second is penalized. Because of this, I found myself taking quizzes I had no way to prepare for, because they introduced concepts for the first time, and I had no way to practice applying those concepts beforehand.

I want to stress here that I'm not simply some idiot who was in over my head. I'm trained in statistics, and I have experience using structural equation and time series models, both of which share similarities with probabilistic graphical models—and I was really interested in the course material. Koller acknowledges in her lectures that the course is challenging, and even seems to take pride in that fact. However, while the material is indeed challenging, the course is hard partly because it's badly taught. It's also possible that Koller is trying to cover too much material for the online format—the lack of classroom discussion not only makes individual topics more difficult, but increases the time required to cover each topic, since the teacher has to provide a much more detailed lecture, rather than relying on student questions to fill in holes.

While I didn't pursue the programming track, other reviewers have complained that they spent more time trying to figure out how to read in the data than they did conducting the analysis. Mind you, this is a problem that data scientists face in the real world, and so the criticism might not be completely fair.

Andrew Ng's Machine Learning is another beast altogether. Ng is in fact a charismatic, and very clear, lecturer; indeed, Koller uses a couple of his lectures in areas where the material in the two courses overlaps. Not only does Ng convey his topics clearly, but he stresses the practical aspects of the methods he's teaching, and provides useful tips about how to apply them in the real world. While Ng pulls students along at pace much gentler than Koller's, he's still able to teach methods that, he insists, are advanced enough to be unfamiliar to many practicing data scientists. I should add that the automated system used to grade programming assignments works quite well. If I do have one criticism, it's that the programming assignments probably involve a little more copying and pasting than might be ideal for learning Octave, but then, copying and pasting isn't uncommon in real programming.

In short, this is a very good course, and I strongly recommend signing up for the session that starts October 14th. Now that things have calmed down a bit for me, I might even sign up for it myself.

Online Course Reviews: Coursera's Social Network Analysis and Foundations of Business Strategy—Plus New Courses to Check Out

I've recently completed Foundations of Business Strategy, taught by the University of Virginia's Michael J. Lenox, and I've submitted the final project for Social Network Analysis, taught by the University of Michigan's Lada Adamic, and I'd like to share some comments on both of these oferrings from Coursera, as well as give readers a heads-up to other courses that have just started.

Social Network Analysis provided a good survey of the methods and applications in the field, covering random networks, measures of centrality, small world networks (and other topics related to the question of optimization), and the dynamic aspects of networks, such as contagion and opinion formation. Adamic's explanations were usually clear, and even a student with little knowledge of probability could have gotten the gist of most of the course material (and made use of Gephi to perform basic analysis), but equations were presented for those who wanted them, and the readings gave further detail. In fact, this is the only course I've had so far that made extensive use of academic journal articles (and a few written for a wider audience), some of them required and some recommended—they gave a much better impression of the history of social network analysis and the current state of the art than Professor Adamic could have given by herself. From a personal perspective, this topic particularly interests me because I can see how social network analysis might be applied to the study of ethnic politics, my previous area of research.

The course's only weakness lay in the (optional) programming track: the first three programming assignments, two in R and one in NetLogo, were largely exercises in copy-and-paste, rather than posing full-fledged coding tasks; they were, however, enough to give students basic familiarity with the two programming languages, and with the igraph package for R. In contrast to these "canned" assignments, the final project was almost completely unstructured, and while this provided welcome freedom to explore whatever topic a student wished, it also meant a steep learning curve for someone whose experience with R or NetLogo was limited to the earlier exercises.

Compared to the other courses I've taken, Foundations of Business Strategy proved much less time-consuming, with required readings limited to very short chapters from a forthcoming book by Lenox (and when I say "short", I mean it's more a pamphlet than a book, with chapters only a few pages long), and a business case each week. The professor encouraged discussion of each case, both in small groups and in the discussion forum, and each week recorded a debriefing that made reference to students' comments in the forum; however, the only assignments that needed to ber turned in before the final project were (relatively easy) weekly quizzes that covered the lecture topics. Quizzes, hence the lectures the quizzes covered, could be completed at any time during the course, though completing the lectures late meant that a student had no chance to participate in the discussios of the associated cases. The final project was a 1500-word "executive summary" of a strategic analysis of an organization of the each student's choice.

Despite the sparseness of the course material, the class provided a useful framework for business strategy, a framework—and this is the part that surprised and impressed me, after all the scurrilous rumors I've heard about business schools, and the weak business students I've taught in my own classes—that was solidly grounded in microeconomics, with no mention at all made of the latest management fads. No, someone who took this course isn't guaranteed to become a strategic genius, or even, necessarily, an effective strategic thinker, but that's because strategy requires making decisions in an environment that's inherently complex and ambiguous, the upshot of which is that giving students a good framework for organizing thought—and a chance to practice strategic thinking on real cases—is about the best that a teacher can do.

My one serious concern with the course was the rubric used for peer review of the final project: the assignment presented a set of criteria for grading that focused largely on the quality of analysis and writing, and specifically warned against trying to use all the methods of analysis that had been covered in the course; by contrast, the rubric that was actually used for peer grading of the assignment amounted to a checklist of topics in the course, and penalized students for not covering each one, while leaving little room for judging the quality of the report. As a former professor, I certainly understand that detailed rubrics, while beloved by students, tend to push attention in grading towards mechanical aspects of the assignment, and this probably goes double for peer assessment, but it is possible to create rubrics that give more or less clear guidance for making qualitative judgments. More importantly, whatever rubric is used needs to match the criteria that are spelled out in the original assignment.

New Courses to Check Out

I've signed up for three courses that have just started, though odds are I'll need to withdraw from one of them, due to time constraints. I stumbled upon Probabilistic Graphical Models—the term "graphical" didn't suggest anything I was interested in, but a look at the course description revealed that probabalistic graphical models (PGM's) includes Bayesian and Markov networks, both of which feature in decision-making and machine learning, and both of which show up repatedly in job ads for data scientists. Coursera co-founder Daphne Koller, of Stanford University, lacks the charisma and clear explanations of the three MOOC professors I've previously learned from; she also comes off (if I may be subjective here) as a bit pretentious, an impression that makes her inclusion of the Simpsons' family tree as an example of a genealogical network more cringeworthy than cool. I've found most of the material so far to be readily understandable, but I'm guessing it wouldn't be for someone without my background in statistics (especially since I've used by structural equation and time series models in my research, and these feature many of the same concepts found in PGM's). This is a graduate-level course, and Koller herself describes it as challenging even by those standards. Needless to say, the other side of that coin is that anyone who gets through the course will have a solid foundation in PGM's, and also, for those taking the programming track, knowledge of Octave and/or MATLAB (the two are close relatives), especially given weekly programming assignments in a 11-week course.

I've also just started An Introduction to Interactive Programming in Python, taught by multiple instructors from Rice University, and Machine Learning, this iteration taught by Coursera co-founder Andrew Ng of Stanford, one of two professors on Coursera to teach this course; like Probabilistic Graphical Models, Machine Learning makes use of Octave. I doubt however that I'll have time for all three courses, meaning that I'll likely have to withdraw from one of them, which will probably be Probabilistic Graphical Models or Machine Learning, given their overlap in programming language, and, to a lesser extent, subject matter.

Online Courses to Check Out

Having nearly completed Stanford Professor Jennifer Widom's Introduction to Databases, I've recently begun two more massive open online courses (MOOC's) that you, the reader, might want to take a look at, both of them offered by Coursera.

The first course is Social Network Analysis, taught by Lada Adamic of the University of Michigan. This methodology, which can be applied to topics as divergent as infrastructure and epedemiology (as well as the more obvious targets such as Facebook), obviously plays a prominent role in data science, which is one reason to take the course. A second reason is that the course features an optional programming track with four assignments (including a peer-graded final project), some using NetLogo and some using R, and in my case I'm taking the course in part as a way to learn R. The course also makes use of Gephi for basic network analysis. In the second week, there are two versions of the lectures, with an advanced version for students with a background in probability distributions and differential equations; it's not clear if this will be the case in later weeks. This is a nine-week class, and if you're reading this soon after I've posted it, you can still sign up and get full credit, since the first assignment isn't due until Friday night (March 15th).

Taking the advice of one of my contacts to learn something about business, I've also signed up for a non-technical course, Foundations of Business Strategy, taught by Michael J. Lenox of the Unviersity of Virginia. This six-week class features a textbook that Lenox is currently developing, as well as the case method typical of business-school education (Lenox recommends small-group discussion to get the full impact of this method). The most interesting feature of the course is a peer-graded final assignment in which each student writes a short but well-researched strategy memo for a the CEO of a company of his or her choice; more interesting still, Lenox has invited organizations that would like their strategy assessed to join the course and offer themselves as cases for the students' final projecdts. Though we're already about 25% of the way through the course, the assignments all have the same deadline of April 14th, and so it's easy to catch up.

You might also keep a lookout for two courses starting the latter half of April, An Introduction to Interactive Programming in Python, taught by a team from Rice University, and the perennially popular Machine Learning, taught by Coursera co-founder Andrew Ng of Stanford (this one uses Octave, a close relative of MATLAB, for those keeping track of programming languages).

Stanford's Introduction to Databases vs. Big Data University's SQL Fundamentals I: A Comparison and Review of Online Courses

It's been too long since I last posted here. To put things simply, I found that I was spending more time maintaining the blog as a resource for others to learn data science than I was spending actually learning data science myself. Now that I've got more experience with online courses under my belt, I'd like to share my insights.

When I first became interested in data science, I began to take Big Data University's SQL Fundamentals I course. This course uses IBM's free DB2 Express-C platform, and content and links on the DB2 webpages, as well as the branding in some of the older course material, indicate a connection of some sort between Big Data University and IBM itself, though I couldn't find any statement of the nature of this connection. Posts in the Big Data forums mention that SQL Fundamentals I was originally a true course, with a schedule and interaction between teachers and students; it's now a self-paced course that makes use of video lectures by a variety of instructors, exercises (downloaded in PDF form), required reading (from free e-books produced by the DB2 community), and a final exam. It covers not only the basics of SQL (including queries and database modification), but theoretical modules as well, specifically, relational algebra and relational design theory.

Stanford's Stanford University's Class2Go offers only a small number of courses (three, at present), but these include a 10-week Introduction to Databases, taught by Professor Jennifer Widom, which was originally offered in Fall Quarter of 2011. It's being offered again now, in Winter Quarter of 2013. Rather than partnering with a third-party provider of massive open online courses (MOOC's), such as Coursera or edX, Stanford has opted to go it alone, hosting the course on its recently established Class2Go (interestingly, Stanford professor Andrew Ng's popular Machine Learning course is still on Coursera, which Ng co-founded). Introduction to Databases uses video lectures, interactive online quizzes and exercises, and exams; supplemental readings are suggsted but not required. This review will address the parts of the course that cover the same subjects as the Big Data University course: parts of the introduction, relationsal algebra (most of week 2), SQL (week 3), and relational design theory (week 4); the 10-week course also covers XML, UML, OLAP, NoSQL, and some advanced SQL topics, such as triggers, views, and authorizations.

First of all, full disclosure: I didn't get past "Getting Started" and "Lesson 1" in the Big Data University course before I started taking the Stanford one. That may not sound like a lot, but it includes most of the reading (seven of the eight chapters) in the course—and the most difficult reading—and I got bogged down with that, though I did finish all of it. That means, ironically enough, that I never completeed the actual SQL portions of the Big Data course, though I've since examined some of the lectures and exercises for those portions. I've been through only the first four weeks of the Stanford course, but I wanted to publish this review in time for readers to join the course late and still be able to get something out of it.

On the whole, I think the Stanford course is the better one, mostly because the lectures contain more material, and the exercises are more demanding. Specifically, the Stanford course features lectures that go into greather depth, and exercises in relational algebra and writing SQL queries that require a lot more thought than the Big Data exercises on the same subjects; there's more emphasis on the logic being applied, rather than merely learning rules and syntax, but, as is often the case, struggling with difficult problems helps to solidify memory of rules and syntax. It also helps that the Stanford course is more interactive: I haven't made any use of the virtual office hours provided by the course's teaching assistant, and both courses have forums, but the Stanford course has a few nice extra features, such as short quizzes during lectures, and automated online exercsies and quizzes that allow you to check to see if your answers are right or wrong—often many times over—without revealing the correct answers and thereby preventing you from working them out for yourself. The Big Data University course does cover some syntactical nuances of SQL that the Stanford course misses. Moreoever, as someone with more training in statistics than in computers, I already have a decent grasp on the logic used in relational algebra and SQL queries, because it's quite similar to what a statistician uses in recoding variables and filering cases. Nonetheless, I think that the Stanford course, because its exercises ask more of a student, does a better job of teaching rules and syntax.

The Stanford course also handles theoretical subjects better. In the Big Data University course, "Lesson 1" includes relationsal algebra and relational design theory, but the lectures cover only the basics of relational design theory, and skip relational algebra altogether. The rest of the material is relegated to the readings, and while I would normally prefer this, since my reading speed is much faster than the speed of a recorded lecture, these readings, while clearly the result of loving hard work, are not well written. The primary problem seems to be that most if not all of the authors are non-native speakers (one thing you learn grading papers is that native speakers and non-native speakers tend to make entirely different errors), and it's extemely difficult to write coherent text in a language other than your native tongue. Indeed, the reading on relational calculus (which, in fairness, is a subject not covered by the Stanford course) was so difficult to follow that I never did glean even the most basic principles from it—and I'm a person who got 800's on the math and logic sections of the GRE. My criticism, by the way, doesn't apply to the recorded lectures, which are quite understandable even though some of the lecturers are also authors of the written materials, and all of the lecturers appear to be non-native speakers. The Stanford course is not without its own weaknesses: judging by both my own experience and posts in the course's forums, the lectures on relational design theory, especially the sections on decomposition and normalization, simply don't go into enough detail to allow students to grasp the subjects in question and complete the exercises—the ideas are all there, but not always spelled out. Nonetheless, the Stanford course covers even this material better than does the Big Data course.

The main drawback of the Stanford course, obviously, is that it's only offered at specific times. As of this writing, the course is presently in week 5, and it looks like you can still register (the registration page is still up, but I didn't create a fake account just to make sure that it works) and try to catch up; given the amount of work each work (several hours a week, with weeks 2-4 being especially tough, this could be difficult for those lacking time or dedication, but Professor Widom does stress that the course is suitable for "a la carte" learning, picking and choosing the topics of interest. The course materials were available online after the close of the last instance (Look, I used database jargon!) of the class, in Fall 2011, but they were taken down and re-used for this one; hopefully, the materials will again be offered online for those who need to learn about databases before the next round of the course is offered.

Finally, one strong point of the Big Data University course that bears mentioning is that the lectures cover downloading and installing a specific SQL platform, DB2 Express-C. By contrast, the Stanford course relies on a web front-end superimposed on SQLite; that means that students don't have to worry about installing any software (they're invited to install software and download the exercise databases if they like, but there's no requirement to do so, and I've been quite successful in doing everything online), but it is nice that I learned the basics of DB2 in the Big Data Course. The flip side of that, of course, is that DB2 is only one of many SQL platforms, though it's admittedly one of the most popular.

UPDATE: Widom has announced that after the current iteration of Introduction to Databases concludes, all of the class materials (including interactive exercises) will remain online.