In fact, after breezing through most of Statistical Inference, I found significant portions of this class to be more challenging. After the first week of Regression Models, I didn’t have much prior knowledge to rely on, which automatically made the class more challenging.

The basics of regression such as a line of best fit, least squares, residuals and the like were all familiar enough. Multiple regression was something I’d played around with a bit too, but we went much deeper than I’d gone with that in the past. I also learned a great deal about topics such as anova testing, variance inflation, and hat values, topics that were completely new to me. In week 4, we covered generalized linear models, and a week after the class ended, I’m still trying to wrap my head around the most advanced aspects of glm’s.

Dr. Caffo does spend a significant amount of time on proofs in the class, even though the proofs aren’t really assessed at all. As a high school mathematics teacher, I can appreciate that because you don’t want to short change your students by giving them a lot of formulas and examples without the rationale and theory behind the problems. That said, given time limitations and the fact that they weren’t being assessed, I did skip some of the proofs in the lectures since I was more interested in application. Perhaps I short changed myself, but to stay on schedule, sometimes trade-offs have to be made. Much like with the Statistical Inference course, Caffo makes heavy use of the manipulate package, and that’s a good thing since it helps to visualize the concepts under discussion.

When I took the class in July, the grade was made up of 4 quizzes and a project, due at the end of week 3. The project, an analysis of the mtcars data set, felt very open ended for a statistics course, and I found myself stressing quite a bit over it, particularly since there was a very tight length limit (2 pages) that I had to justify myself. It seemed impossible to apply the concepts I had learned in Reproducible Research with such a short length of paper.

I found this to be a more challenging course than Statistical Inference because the material went beyond the typical beginning statistics course. You may want to look at the supplemental resources I suggested in my review of Statistical Inference because they are relevant here as well.

]]>In R Programming, the members of the class without significant programming experience had to fight and scrap to keep up. In Statistical Inference, it seemed to be the members of the class that had been out of the mathematics too long that struggled.

I should preface the rest of my comments by admitting that as an AP Statistics teacher, even of just one year, I had a significant advantage in this class. Probably 75% of the material in Statistical Inference is covered in AP Statistics curriculum, and while Dr. Caffo pushed a little deeper than the average high school senior would go, many quiz questions could have come straight from the an AP Stats exam review book. Obviously, that inspired a lot of confidence, and for me, this was the easiest course in the sequence other than The Data Scientist’s Toolbox.

Dr. Caffo is at his best when he encourages his students to think about the effects of potential changes to a data set. He does this a couple of different ways. First, he occasionally uses visual diagrams of data sets that he’s plotted ahead of time. Second, my favorite method, he uses the “manipulate” package in R. This package allows the teacher or student to use slider bars to make changes to various parameters and have the graph in R react in real time. It almost lets me pretend I’m working with the TI-Nspire again. Lest you miss him announce it the first time, all of the code for the manipulate demonstrations is available on the course Github repo, so you can copy and paste right into RStudio and do the demos along with him.

In Statistical Inference, you will find a lot of the basic concepts of inference such as confidence intervals, p-values, and hypothesis tests. There’s also some basic probability covered. Topics that I had less familiarity with included Poisson distributions (hadn’t used them since an actuary test years ago), resampling techniques (the jackknife and the bootstrap), and multiple testing.

At the time I took Statistical Inference, which was the June session, the grading was entirely made up of 4 quizzes. There were also optional homework assignments, which I found to be very helpful. If you don’t have a deep statistics background, be prepared to spend some time supplementing with outside resources for this class. It is simply too much to expect to pick up everything you need in a short series of lectures. This class covers almost as much material as would be covered a semester at a university, which could be a problem if it is all brand new to you, as it was to some students in the class.

A couple of resources I would suggest are Datacamp, which offers R training in your browser. Take the Data Analysis and Statistical Inference track, which overlaps a lot with this course. A couple of very high quality free eBooks that are popular with people in the Data Science Specialization are An Introduction to Statistical Learning and Open Intro. Open Intro is actually so cheap on Amazon that I picked up a physical copy there.

Summarizing, Statistical Inference is a very challenging course for those that have not got a statistics background. Expect to spend time studying and Googling. You will need to supplement the lectures. If, on the other hand, you already have a firm grasp of introductory level statistics, you should only expect to pick up a few new concepts along the way.

]]>Reproducible Research is the course among the first five in the specialization (except The Data Scientist’s Toolbox), where I spent the least time learning new R code. Instead, the emphasis of this course was more philosophical in nature. Here the emphasis was on writing your research findings up in a way that they could be shared with others in such a way that they were considered to be reproducible, though not necessarily replicable. For more on the definition of reproducible research, check out this post from Dr. Peng.

That’s not to say there isn’t much R coding in Reproducible Research, or even less coding. Just like the other classes in the sequence, I still spent a fair amount of time cleaning data and programming R for data analysis. It’s just that the emphasis of the class was on communicating those results in a manner that anyone who was well versed in R could follow my analysis from the very first step to the very last step and reproduce those results.

One of the niftiest features of RStudio that we explored in this class was its ability to easily use Knitr. Using Knitr, we created single documents that combined markdown and R code into one, simple to read document. The output of the code is contained right in the document and the code itself can be revealed or hidden. The document can be outputted as say, a pdf or html file. It’s a really handy tool.

Throughout the course, Dr. Peng emphasized the importance of making your research reproducible. It reminded me a bit of being back in high school and being told I needed to “show my work.” Very compelling examples were shared with the class of the importance of reproducible research. Without a doubt, the most compelling example was the case of the fraudulent cancer research at Duke University, which eventually made its way onto 60 Minutes.

While I do hope the Data Science Specialization leads me to a new career opportunity, I don’t suppose it’s very likely that I’ll end up as a cancer researcher. Will reproducible research be as important to me as those cutting edge medical researchers? Perhaps not, but I can certainly understand why this course was included in the sequence, and even if I only end up sharing my code with a few coworkers down the road, I’ve learned a thing or two about the proper way to share my results with them.

]]>This course could just about as well be titled “Visualizing Data,” since most everything in the class emphasized methods of presenting data visually in R. The bulk of the time in the class was spent on the 3 most popular methods of graphing in R: the base plotting system, lattice plot, and ggplot2.

Each of these methods of plotting has their own unique syntax. While I got pretty comfortable with base plotting, I’m still gaining a comfort level with lattice and ggplot2. I’m glad we dove in with them because it’s pretty clear from poking around Stackoverflow and other forums that these packages are very widely used. Since I completed the course, I recently attended a webinar taught by R guru Hadley Wickham, where he explained the newer package ggvis. Since Hadley made it pretty clear that ggivs is going to someday replace ggplot2, I wish we had at least touched on it in the course.

Unlike most of the later classes, this one had 2 projects, not 1, and one of the projects was due at the end of week 1. I was not doubled up, but trippled up when taking Exploratory Data Analysis, since I was also taking Reproducible Research and Statistical Inference simultaneously. That meant I really had to jump in and get going on that first quiz and project immediately.

I found the second project to be extremely challenging. Only in retrospect did I realize that I’d made a few foolish mistakes by trying to accomplish things with for loops that could have been done much more easily with apply functions. If you are signing up for the sequence now, learn from my mistakes and master those apply functions early.

In week 3, significant time was devoted to hierarchical clustering, dendrograms, k-means clustering, and heatmaps. However, these topics weren’t assessed in either project, so I don’t feel like I mastered them as well as I wish I had. That is a bit of a weakness of these courses being each a month long. Some topics are going to have to be for exposure rather than mastery.

Ultimately, this was another course that taught absolutely critical skills in the Data Science Specialization. I can’t imagine moving forward without having learned these visualization techniques in R.

]]>Once again, it’s time to update my calculator recommendations for the 2014-15 school year. Each year around back to school time, I try to update this post to reflect the changes that have happened with new models and new operating systems. It’s amazing to see how this list has changed in just the 4 years since I started Tech Powered Math. Back then, there were no calculators with full color screens. Today, I don’t have any calculators on my list without a full color screen.

As always, I suggest you first decide whether you need a CAS or non-CAS graphing calculator. Before I give you my 2014 calculator recommendations, let’s review the differences between these two.

A CAS is a computer algebra system. CAS calculators can solve equations, manipulate variables, factor, and more. Basically, these calculators are capable of solving problems with x and y, like x + x = 2x. Once you get into sophisticated calculations involving variables, this is a lot of power. They are welcomed in some circles, such as AP calculus, the SAT, and many high school and college classrooms. However, they are banned by the ACT and some teachers who feel they can do a little too much. Consider your college testing plans and your school’s math department policies before deciding on a CAS vs. non-CAS calculator.

Without further delay, here are my picks for the best calculators for the 2014 school year.

For me, the calculator of this decade is the TI-Nspire CX, and it’s easy to see why. Texas Instruments brought graphing calculators into the 21st century with this one. It has computer like features including drop down menus, point and click interface, and file/folder features. Graphing features were tremendously simplified over most other graphing calculators, and the resolution is high, making it easy to see the math operations that look exactly like they do in your textbook. As a teacher, I feel like the TI-Nspire OS 3.2 brought this calculator to another level, giving it the ability to graph equations written in “x=” form from simple lines to advanced conic sections. Texas Instruments has continued to evolve the platform, releasing regular updates to the operating system as recently as June of 2014 (3.9). I recommend buying your TI-Nspire CX on Amazon to get the best price and free shipping.

TI-84 Plus C Silver Edition (REVIEW)

A year ago, Texas Instruments updated their most popular graphing calculator of all time, the TI-84 Plus. They gave it a new high resolution, full color screen with a backlit display and a rechargeable battery. The new TI-84 Plus C is can now graph on images as well. While I don’t put it on par with the TI-Nspire CX, it is a big step up from older, black and white versions of the TI-84 Plus, and it doesn’t cost much more. If you are going to buy a TI-84, I’d strongly recommend going ahead and paying the extra $10 or so to get the color edition. You can get the best price on an 84 C here at Amazon.

The Casio Prizm continues to be the most underrated graphing calculator on the market today. I consider it the easiest graphing calculator to use. This non-CAS calculator offers a lot of easy to use features that you won’t find in most other non-CAS graphing calculators. It simplifies radicals, finds exact trig values, and uses textbook format for it’s math symbols, meaning you don’t waste a lot of time learning calculator syntax. It’s graphing features are also very cool, as the Prizm will find y-intercepts, solve for x values given a y value, even integrate between two curves. Much like the TI-Nspire CX, the Prizm has a full color screen and the ability to load images. Casio has also been good about issuing OS updates, including a recent one that gave the Prizm the ability to do the periodic table of elements. Since it doesn’t have a CAS, it’s also a terrific calculator for the ACT. It’s also affordable, and you can often buy it on Amazon for $30 or so less than the TI-Nspire CX.

Interested in a touch screen calculator that is legal on College Board’s SAT exam? Then you’ve basically got a single choice, the HP Prime. This CAS calculator is about to celebrate its first birthday by being widely available during back to school shopping for the first time ever in 2014. It offers a variety of powerful graphing features, and while it’s not quite as intuitive as the others on this list, is far more user friendly than HP models of old. It will certainly appeal to HP’s hard core fan base. Click here to get the HP Prime on Amazon.

]]>**Please buy your TI-Nspire CX on Amazon for the best price and FREE shipping.** If you buy through this link, a small percentage of your purchase will help support my work at Tech Powered Math.

Since I work with it more than any other calculator, I like to do an annual refresh of my TI-Nspire CX review, so it’s time for 2014 review. The last year has brought a couple of new operating system updates, 3.6 and 3.9.This post is both a TI-Nspire CX review and TI-Nspire CX CAS review, although you’ll see while reading it, the two models have more in common than they do differences.

First, it’s important to understand that Texas Instruments has continued to support the older TI-Nspire “grayscale” models. So when a new operating system like OS 3.9 was released this June (2014) for the CX models, it was also made available for the TI-Nspire graycale models like the clickpad and touchpad. That means that from a software standpoint, anything you can do on on the TI-Nspire CX, you can do on a TI-Nspire grayscale. I don’t see this changing any time soon, and hopefully ever. Texas Instruments has been really great about supporting schools and students that adopted the TI-Nspire platform early on. That said, I’d still steer you toward the newer TI-Nspire CX if you can afford it. It’s true that you could pick up a used grayscale model on eBay at half the cost if you really need to and simply update the software. If you are in a serious financial bind, it’s not the worst option ever. However, there are definitely some advantages to having a TI-Nspire CX over an older model that I’ll go into a little further down in this article.

The platform only continues to charge ahead. Almost two years ago, we saw the release of the TI-Nspire iPad app (review). I was very impressed with this app and do recommend it even at close to $30. However, you should keep in mind that it’s simply not a replacement for a TI-Nspire CX handheld since it’s not allowed on standardized tests like the SAT or ACT.

I will say that even before the updates of the last couple of years, I had felt that from an educational standpoint, the TI-Nspire operating system was superior to any other TI product (such as the TI-84 or TI-89). I like the fact that it uses the type of interface that kids who have grown up in the internet era are familiar with. That means it has drop down menus, the ability to open multiple tabs like a browser, a drag and drop interface, and a file and folder system that’s just like using a computer. Graphing calculations have been much simpler, not requiring much button pressing beyond the trace feature because the Nspire automatically detects max and min values and intercepts while using trace. It’s also easy to type in your calculations using mathematical symbols so that they appear just like a student would see them in a book or write them in their homework, so there’s pretty much no code to learn. What really sets the Nspire apart has always been its dynamic properties. On one screen, you could have a graph, an equation, and a table of values. Grab the graph and drag it into a different shape, and all three adjust simultaneously.

With the OS 3.2 update a couple of years back, Texas Instruments gave teachers and students the features that they have been asking for. There are some really awesome new features for the TI-Nspire CX, but as I explained before, these features are now available on the older TI-Nspire models with a free software update from Texas Instruments. The biggest upgrades with OS 3.2 were graphing features. Owners of any up-to-date Nspire now have the ability to graphing conic sections and any equation written in “x=” form. This is something Texas Instruments users have wanted for years. You can see this and more below in the TI-Nspire CX video review I recently put together.

The updates that came in versions 3.6 and 3.9 were more modest in nature, and would probably be more noticeable to users of the the teacher software than users of the handheld itself. However, TI did make some improvements to the way certain graphics are displayed in graphing mode, spreadsheet mode, and data and statistics mode that are a nice improvement.

Also new for 2014, the latest TI-Nspire CX model has an improved battery design. Previously, if you were having battery issues with your TI-Nspire CX, the battery was wired into the Nspire, and there was always a “hold your breath” moment of disconnecting that wire. The new battery is smaller and connected by a compression contacts like a cell phone battery. It pops in and out very easily. This isn’t an issue that comes up very often since the CX has a rechargeable battery, but I’m still glad to see it addressed.

The TI-Nspire CX will always be known as Texas Instruments’ first color graphing calculator, and an improved design came with it. It’s a much thinner, lighter design that we’ve seen from TI in the past–more like the thickness of a standard scientific calculator than previous graphing calculators. The odd “wings” of previous TI-Nspire models are gone. The screen is gorgeous. The resolution is good, the colors are bright, and the display is easy to read. You won’t mistake it for an iPhone, but it’s better than any display I’ve ever seen on a graphing calculator, including the Casio Prizm (review), despite my love for the Prizm. As I type this, the backlighting is bright enough to read easily from my deck in the shade, although not in direct sunlight. However, I don’t know anyone who would uses their graphing calculator in direct sunlight.

I’ve tried the image features on both the TI-Nspire CX models and the TI-Nspire graycale. You have the capability to load any JPG, BMP, or PNG file onto the device and curve fit graphs right on top. It certainly works on both color and grayscale models; I’ve even posted pictures of the grayscale pictures on the Nspire already. However, I think the screen captures are a little deceiving. While the pictures look great on the computer screen in both color and grayscale, in some cases they’re actually kind of hard to make out the the Nspire grayscale. No worries if you are on a TI-Nspire CX; images look great on the handheld.

I can also tell you that the buttons are “snappier” and more responsive than previous TI-Nspire models. Back when I first asked him about this, Dale Philbrick, Texas Instruments Marketing Segment Manager for Mathematics, told me that this was accomplished by a combination of hardware and software upgrades.

Along with this change, there are no more interchangeable keypads. That means you can’t use the TI-Nspire CX as a TI-84 like older Nspire models. When Philbrick and I talked about this back at the time of the Nspire CX release, he said, “We felt like it was time for people to start using the Nspire as an Nspire.” No argument here. In the early Nspire days, I used to be frustrated with the occasional student who refuses to give up their Nspire’s TI-84 keypad but then makes mistakes on problems that are so much easier with the Nspire, such as secant, cosecant, and cotangent (which can’t be found directly on the TI-84). With each new TI-Nspire OS, the TI-84 just feels more and more outdated.

Judging based on polls I’ve run in the past on Tech Powered Math, I’ve found that there’s actually more interest among TPM readers in the TI-Nspire CX CAS than there is in the TI-Nspire CX. If you’re unaware of what a CAS calculator is, it is a Computer Algebra System calculator. This means the calculator has the ability to do algebraic manipulations on variables, such as x + x = 2x. This is a great thing if you plan to take the SAT or AP Calculus, where this type of calculator is encouraged, but not so great if you are taking the ACT, where it is banned.

The TI-Nspire CX and TI-Nspire CX CAS have most of their hardware and functions in common. They’re so similar, it’d be easy to mistake one for the other, even after playing with it for a bit. However, the CAS version has a number of menus and submenus tucked away that you won’t find in the non-CAS version. In the past, the questions I’ve received about the Nspire CAS tend to be from TI-89 users wary of the the advanced calculus capabilities of the Nspire CAS. Since OS 3.0, differential equations and a host of advanced calculus features have been included for all TI-Nspire models.

While I’ve taken math courses all the way to a graduate level, my teaching experience only goes through second semester college calculus, so I can really only speak to classes at that level and below. In those classes, I’d much prefer the TI-Nspire CX CAS to a TI-89 Titanium. Even though the TI-89 Titanium (review) is an excellent calculator, the Nspire CX CAS is easier to work with on conics, has a much nicer UI, is easier for graphing, and has 3D graphing for a couple years now, a drawback that was a problem when the series was first launched, but no more.

If you find that you need a little help learning how to use your TI-Nspire, I recently started a line of books designed to help students and teachers learn how to use the calculator. TI-Nspire Tutorials Volume 1 is the TI-Nspire for Beginners, that helps you navigate all of the basic features of the calculator. TI-Nspire Tutorials Volume 2 is Using CAS Features Like a Champion, and will get CAS users started with those features only found on the CAS. Both books are currently available in Kindle format for the iPad, iPhone, Android, PC, and Mac. I’ve recently updated them for TI-Nspire OS 3.9.

At around $130, the TI-Nspire CX and TI-Nspire CX CAS are among the more expensive calculators available today, but they are well worth the price, only a small increase over the TI-Nspire grayscale models. When Texas Instruments debuted the TI-Nspire line a few years ago, it had a few warts, but with each year, they’ve continued to make massive improvements to both the hardware and software. Even at the beginning, I was impressed with how much faster kids caught on to features like fractions, graphing, and tables on the TI-Nspire than they did on the TI-84. From remedial Algebra through AP Calculus and my 2012 Illinois state championship math team, the TI-Nspire is my top choice for my students.

With the Nspire series, my students spend less time learning calculator code and more time learning math. Since the release of the TI-Nspire CX and continued OS upgrades, this is a fully mature platform. No longer can Texas Instruments users cling to the good old days of the TI-84 and TI-89 and feel like they honestly have the best the market has to offer. The future has arrived. The TI-Nspire CX is TI’s platform of the next decade.

**Please buy your TI-Nspire CX on Amazon for the best price and FREE shipping.** If you buy through this link, a small percentage of your purchase will help support my work at Tech Powered Math.

The breadth of material covered in this course was spectacular. Dr. Leek spent the majority of the first two weeks of the course explaining who to read a variety of data sources into R, some of which I was pretty familiar with, but others I was learning about for the first time. Among the included data sources were Excel files, XML, JSON, HDF5, and MySQL. I wish we had spent a little more time on SQL since this seems to be about the only place the Data Science Specialization touches on it, but this class had so many topics that it was impossible to spend too much time on any one.

I also found the subject of reading API’s into R fascinating. I’ve heard the term API thrown around so much over the last 5 years or so, but beyond knowing that it stood for Application Programming Interface, all I really knew was that it allowed programmers to tap into existing web services somehow to create their own apps. We practiced with accessing Twitter from R using the Twitter API, and it was so interesting that I spent some time later experimenting with the LinkedIn API in R on my own time.

There was time spent learning how to subset and sort data as well, much of it a review of material from the R Programming class, but my guess is that most people will need that review unless they took the classes out of sequence like me. There’s also significant time in week 4 devoted to editing text variables, working with dates, and plain-text search functions such as “grep.”

If I have one beef with this class, it is not the class itself, but the course dependency chart. On the official chart, there are no courses that list Getting and Cleaning Data as even a soft dependency. Consequently, I took courses 4 and 5 (Exploratory Data Analysis and Reproducible Research) prior to taking this course. Cleaning the data sets for analysis was probably the most challenging part of those courses for me. In retrospect, I can see it is because I had not yet taken Getting and Cleaning Data. The material from week 3 and especially week 4 does come in handy in those later classes, so I would not recommend taking the route I did for future students of the Data Science Specialization.

Like many other classes in the sequence, this one had 4 quizzes and a peer graded project due at the end of week 3. The project involved preparing a tidy data set, and it did require information from week 4 of the class, so if you are thinking of taking this class, plan ahead on the project.

Some students on the class forums have called it among the most challenging in the Data Science Specialization. Although I learned a lot and was challenged, I didn’t find it to be the toughest, but that could be because I had already taken 5 classes by the time I got to this one and had gotten through some of through some of the hardest initial part of the learning curve.

]]>R Programming is taught by Roger Peng, who, based on forum feedback, seems to be a student favorite in the data science sequence. I have to concur that Dr. Peng did a good job explaining the highlights of introductory R functions as well as providing examples throughout the course. He lays things out in a way that’s clear and concise.

That said, if you are thinking of signing up for R Programming with zero programming experience (and it was clear that some people had done this), it’s going to be a rough go. I hadn’t done any significant programming in nearly over 15 years, but I did have some algorithmic knowledge to draw on from my days in the 90′s working with GW-Basic, Fortran, and Scheme. That experience was invaluable, but I still felt like parts of my brain that hadn’t been worked out in a long time were really being stretched.

I’ve completed 7 of the 9 courses in the sequence (minus the capstone) at this point, and it’s become increasingly clear that there are a couple of bottlenecks in the sequence. R Programming is a stumbling block for people without significant programming experience. I’ll talk about the other, Statistical Inference, in a later post.

If you lack that programming knowledge, you might consider spending some time working through some exercises at Codecademy before enrolling in R Programming. It’s a free way to get some experience with algorithmic thinking. I’ve been using their Python track to get additional experience in another language and am finding it very helpful. R Programming consists of quizzes as well as programming assignments. When I took the class in May, a couple of the assignments were automatically evaluated by computer, and another was peer evaluated. The real challenge was the second programming assignment. Many people on the course forum said they dropped out in frustration during this assignment. Be sure to set aside sufficient time to complete it, particularly if you are a programming beginner.

I would also advise anyone taking R Programming to pay special attention to R’s apply functions. Each of the courses in the Data Science Specialization will throw a lot of material at you, and you probably won’t master all of it. Sometimes I find myself having to make decisions about where to spend my study time, but those apply functions become critical as you progress through the sequence. Do yourself a favor and lock yourself in a room practicing them until you understand them.

In summary, R Programming is quite a challenge for those without significant programming experience in another language or who have been out of the loop for a while. Those that already have significant programming experience seemed to adapt a lot more quickly. We were pushed to do some independent thinking that went beyond the lectures, and it is a good thing that we were in an early course in the sequence because that is an expectation for the rest of the Data Science Specialization.

]]>I’ve spent some time making changes to the screen captures and directions in both volume 1 and volume 2 to create second editions of the books. Texas Instruments made the changes from 3.2 to 3.9 very smooth, so none of the changes I had to make to the books were earth shattering. However, if you have already purchased a copy of one of my books, you should be able to get your updated “second edition” with the up to date graphics and directions for OS 3.9 by going to the manage your Kindle page at Amazon.com.

If you have never purchased a copy of my books, or if you have friends that would like to get in on a great deal, this Thursday is the day to do it. To promote the new editions, I will be running a one day special, giving away FREE copies of the TI-Nspire Tutorials Vol 1 and TI-84 Plus Tutorials (which is unchanged), and selling TI-Nspire Tutorials Vol 2 for $.99. Here are the links to the books that will be available for free or cheap on Thursday, August 14:

TI-Nspire Tutorials Volume 1

TI-Nspire Tutorials Volume 2

TI-84 Plus Tutorials

Remember, you don’t need to own a Kindle device to read the books. The Kindle app for your phone or tablet will work just as well. Please let your friends know about the promotion by liking, tweeting, and sharing this page with them. As always, thanks for your support, and if you’ve found the books helpful, please consider writing a review on Amazon.

]]>While I was under the impression that the general public is now pretty well informed about MOOC’s, it’s been pretty obvious from speaking with my college educated peers that that is not the case. I’ve had to do a lot of explaining to my friends over the summer about the classes that I’ve been taking–how anyone can enroll, that the instructor’s videos are available on demand, that quizzes and projects are submitted online, and that for a small fee, Coursera will use a few identity verification features to issue you a special “verified” certificate at the end of the course that can even be posted to LinkedIn automatically.

I took the Data Scientist’s Toolbox concurrently with course 2, “R Programming.” I disproportionately spent most of my study time in May on R Programming, not this course. Professor Jeff Leek did a good job with motivating information to begin the Data Science Specialization, both giving interesting examples of data science in the news and explaining how it is growing as a career path. I found Leek’s ability to keep me engaged with relevant examples and stories has been one of his greatest teaching strengths throughout the 3 courses he teaches in the specialization.

The bulk of the course was spent on setting up the software that is used throughout the remainder of the specialization. That is, anyone taking this class will spend most of their time installing RStudio and Git Bash on their computer and setting up a GitHub account. If you are already “good with Git,” you’ll find this class to be extremely easy. If you are not, expect an hour or two of struggles with Git as you complete the course project that involves Git. Version control with Git is not the most exciting way to begin your data science studies, but having completed most of the rest of the sequence, I would urge anyone who plans to complete the sequence not to overlook this project since it is a tool the professors use in most of the remaining classes.

There’s not much else to say. This class is much, much easier than any of the other classes in the Data Science Specialization. The course dependency chart lists this course as a “soft dependency” for R Programming. I would agree that these two classes can certainly be doubled up.

]]>