Author Archives: Lucas Allen

Regression Models Coursera Review

RegressionModelsThe seventh course in Johns Hopkins Data Science Specialization on Coursera is Regression Models. This is the second course in the sequence taught by Brian Caffo, after Statistical Inference. Much like that course, the emphasis here is on mathematics, and people who have been out of the mathematical loop for a while will probably find this class to be a struggle.

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.

Statistical Inference Coursera Review

The sixth course in Johns Hopkins Data Science Specialization on Coursera is Statistical Inference. This is the first course in the specialization taught by Brian Caffo. In my review of the R Programming course, I mentioned that there were two places in the sequence that seemed (based solely on my observations of forum comments) to be boggingContinue Reading

Reproducible Research Coursera Review

The fifth course in Johns Hopkins Data Science Specialization on Coursera is Reproducible Research. This is the third and final course in the sequence taught by Roger Peng. 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, theContinue Reading

Exploratory Data Analysis Coursera Review

The fourth course in Johns Hopkins Data Science Specialization on Coursera is Exploratory Data Analysis. This is the second class in the sequence taught by Roger Peng, after R programming. 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 ofContinue Reading

Getting and Cleaning Data Coursera Review

The third course in Johns Hopkins Data Science specialization on Coursera is Getting and Cleaning Data. The purpose of this class is to get students familiar with the process of creating a “tidy” data set from a variety of different sources. Like The Data Scientist’s Toolbox, this class is taught by Jeff Leek. The breadth of material coveredContinue Reading

R Programming Coursera Review

The second course in Johns Hopkins Data Science Specialization on Coursera is R Programming. I took this class concurrently with The Data Scientist’s Toolbox, which was more of a “warm up” class. If you don’t have much of a programming background, you’d better get warm quickly, because this class gets hot in a hurry for theContinue Reading

Second Editions of TI-Nspire Tutorials

Second Editions of TI-Nspire Tutorials

With my long silence here on Tech Powered Math, I’ve hardly mentioned the fact that there have been two upgrades to the TI-Nspire operating system, OS 3.6 and OS 3.9. These upgrades have been incremental, not earth shattering, but they have pleasantly changed some aesthetics throughout the TI-Nspire environment. There have also been some minorContinue Reading