Getting and Cleaning Data

Getting and cleaning data is the third course in the first wave of John Hopkins’s data science specialization track on Coursera. It is recommended that you take this course after the data scientist's toolkit and R programming courses.

The title of the course pretty well sums up the content: the entire class is about loading data into R and cleaning it up so that it can be used of data analysis. You'll learn how to load various data formats into R, such as json, xml, csv, excel files and get data from other sources like MySQL and web APIs. The course also discusses subsetting data, adding variables, merging data, regular expressions and working with dates.

This course is a good summary of many of the things that are useful to know when trying to access and prepare data for analysis. Similar to R programming, it suffers from overuse of static slides with voice-overs, a lack of instructor face time and a lack of interactive content or in-lecture quizzes to help you learn and retain as you go along. You'll be introduced to many R packages and syntax that you probably won't remember after a week or two, but you'll be exposed to many common data formats so that you can refer back to the course materials or other web resources to deal with them in the future.

I'm a fresh beginner to R and my only experience with it is from the previous 2 courses in this specialization.

The lectures aren't so bad... they're a little bit boring and not engaging since they rarely are more than just a voiceover and slides. If that's important to you, don't take this class. However, I do think the instructors explain the lecture topics well and there is some value in their short walkthroughs.

Unfortunately... this only applies to the lecture topics... which are often only a small part of the quizzes and programming assignments. The previous course in the specialization, R Programming, was MUCH worse in this regard. That said, if your R background is minimal and you utilize outside sources (Stack Exchange, forums, etc.), you WILL learn a LOT. But at times you may feel a bit that the course itself didn't play a large role in the learning process, aside from giving you assignments.

Overall, the material is quite difficult for someone with no background (aside from the other courses in the specialization). I have to give a lot of props to the people in the discussion forums and the CTAs: these people really help close the gap and it's because of them that I keep pushing through!

This is the third course in the series, and it's taken me this long to realize that everything I learn comes from external sources and not the course itself. If you do this, you'll learn something. If you don't, you'll lose your mind and waste a ton of time in the process.

I started out by watching the videos, taking copious notes and then realizing that I didn't have the information I needed to complete the assignments. I was very stressed about it until my friend -- who uses R programming regularly for work -- shrugged and said, "That's how it works in the real world. You search Stack Overflow or Github to find others who have already solved your problem. Don't go reinventing the wheel." This took a huge amount of pressure off.

I find that I look at others' solutions, work through them line by line to figure out the how and why of it, test to see if they're correct (about 40% of the time they don't appear to be), and learn by doing. I listen to the course videos in the background, and try to tie what they're talking about to actual code that I'm seeing, which makes a big difference.

This is the third course in the Data Science specialization. The course is all about how to read data of different formats into R and how to create tidy datasets (one variable per column, one observation per row, one observational unit type per table). There are brief introductions to reading datasets from online resources such as XML files, website APIs, and MySQL, and the quizzes for weeks 1 and 2 require you to work with these tools. Week 3 introduces subsetting and reshaping data and tools like dplyr, and week 4 introduces working with text strings and regular expressions.

I found this course to be quite a bit easier than the prerequisite R Programming. The quoted time commitment of 4-9 hours/week seems pretty reasonable, and I was probably at the lower end of that even though I front-loaded everything and finished by the middle of the second week of the course. The course project was fairly straightforward but also open-ended, and there was some concern on the discussion boards about how certain aspects of the project (e.g. descriptive variable names) would be evaluated.

All of the quizzes required a fair bit of programming, but nothing was too difficult. There were some technical hurdles in several of the quizzes that caused people problems. For example, R cannot read XML files over an https connection, and that caused some problems for quiz 1, although several solutions were quickly posted. Quiz 2 contained probably the most difficult programming task in the course, which required reading information from the instructor's Github account using the Github API. With some searching I found solutions online, but if you're having trouble and can't find good answers elsewhere, the forum will eventually help once a sufficient number of people get around to taking the quiz.

Overall, four stars. The course was fairly straightforward, and the information here may or may not be valuable to you depending on the type of data analyses you plan to perform and where/how your data are stored. I had previous experience with dplyr from the first course in the EdX PH525 series, so the most valuable portion of the course for me was the section on working with regular expressions.