John Hopkins is offering a 9 course data science specialization through Coursera in batches of 3 courses per month for 3 months. The first three courses, The Data Scientist’s Toolbox, R Programming and Getting and Cleaning Data are just wrapping up so I'm going to give a quick review of each of these three courses and my thoughts on the data science specialization as a whole thus far.
The Data Scientist’s Toolbox
The Data Scientist’s Toolbox is essentially just an overview of the data science specialization track. It introduces the very basics of R and R studio, Git and Github and a few other things that will be used in the data science specialization track. It is basically a bunch of introductory and supplementary material that shouldn't be a standalone course. You can complete all the lecture videos in the entire course in about 2 hours. It's almost embarrassing that John Hopkins has a paid verified certificate option for this course and it is required to complete the data science specialization track.
Rating: 1/5 stars: Terrible.
R Programming is a remake of Computing for Data Analysis, another course offered on Coursera by the same instructor, Roger Peng. This course covers R basics such as R data types and objects, reading and writing data, control flow, functions, scoping, dates, loops, debugging tools, simulation and code profiling. The slides and lectures are a bit smoother than Computing for Data Analysis but the content is mostly the same.
This course has good information but suffers from a lack of instructor face time and heavy use of static slides with voiceovers, which are less engaging than videos of instructors actually running the commands they are talking about. Additionally, there are no in-lecture quizzes or interactive exercises to help you absorb the material as you go along. If you want to get as much out of the course as you can, I recommend that you follow along with R Studio open on a second screen or window and try out commands discussed as you watch the videos. Overall, it is a decent intro to R, but it is not particularly engaging.
Rating: 3/5 stars: Satisfactory
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.
Rating: 3/5 stars: Satisfactory
The first month of the data science specialization track is disappointing mainly due to dull presentation and a lack of content in The Data Scientist’s Toolbox course. R Programming and Getting and Cleaning Data contain a lot of good information that could help students form a foundation to build on in the coming months, but a lack of interactive exercises and in-lecture quizzes means students may have a hard time retaining the information. Despite the lackluster beginning, I'm looking forward to the next wave of 3 courses starting up in a few days. These courses were mainly review for me; it's always more fun to learn new things.
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