The eighth course in Johns Hopkins Data Science Specialization on Coursera is Practical Machine Learning This is the third and final course in the sequence taught by Jeff Leek. Probably more than any other course in the JHU series of classes, this is the one that feels like it brought the whole sequence together. Students of Practical Machine Learning need the skills developed throughout the rest of the sequence to be successful in this course, from basic R Programming (course 2) through Regression Models (course 7).
The 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.
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 bogging students down. R Programming was obviously the first. Statistical Inference is the second.
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 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.
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 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.
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 the uninitiated. R Programming is substantially more challenging than The Data Scientist’s Toolbox. R Programming is taught by Roger Peng, who, based on forum feedback, seems to be a student favorite in the data science sequence.
The Data Scientist’s Toolbox is the first course in the nine course sequence (plus capstone) that Johns Hopkins is offering via Coursera towards a Data Science Specialization. This course was not only my first course in that sequence, it was my first class on Coursera. In fact, it was my first MOOC (massive open online course). 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.
For nearly six months, this blog has gone quiet. Considering I’ve had 3+ years of posting an average of two times a week, that’s quite a stretch without writing. There have been a variety of reasons for my silence, including increasing work and family commitments, but as I alluded to last fall a couple of times on this blog, I’ve also been investigating new career options. The biggest reason for my silence is the significant amount of time that I’ve devoted to career questions over the first half of this year.