Coursera review: The Data Scientist’s Toolbox

Facebook can be a wonderful thing.  I have liked the BBC, CNN, and many other news outlets on my Facebook account.  I don’t watch the news on TV, I get upset about it on Facebook.  Really, it is much more efficient and upsetting. 🙂  But you can’t get too upset as the next post is some funny cat video.

Advertising is moving to Facebook and in talking about Data Science, Facebook and their advertisers must have some really good algorithms at work.

I remember when we all used to laugh about the inappropriate ads that would show up on the web and Facebook.  Now, these ads are targeted and they know us well.

An add for Coursera’s Facebook page is how I came to know about the Data Science certificate.  Since then I’ve read articles about the course and the enticing low cost of the course gathered my interest. ($29) [NOTE: all other courses are $49 each]

The Data Scientist’s Toolbox course contains an overview of the Data Scientist’s job, takes you through installing R, creating and using a Github account with Git GUI and Git Bash, and the steps in Data Analysis.

I went through the course very quickly; although, I had to wait for the official course end to get my certificate. (Coursera datascitoolbox 2015)

The course is composed of video lectures, dedicated forums where many classmates post questions and answers, quizzes, grading other student’s work, and projects.

The fairly simple course projects was to show that you had successfully installed R, R Studio, and established a Github account.

The lecturer is very good, easy to understand and while not vocal about it you can tell he enjoys the field and teaching.  Enjoying teaching is something I wish all my college professors had when I was in college.

When I was finished with the course I decided I really wanted more.  I enjoyed it a lot.  The amount of time to complete the course was far less than their estimates (4-9 hours a week).  So I went on to the R Programming course immediately afterward starting that course mid-course.

While I thought the course was easy and the project was easy, I graded 4 other student’s work and 1 of them was not able to properly use Github.  So, what you will get out of this course depends greatly on what you take in with you.  Even someone with advanced programming knowledge will learn at least a little something from this course.

There was only one thing that was lacking in an overview course like this one – and that was to show off R Programming languages capabilities and say why choose R over other technologies.

I’ve finished the second course “R Programming” and started “Getting and Cleaning Data” and “Exploratory Data Analysis“.  I have to say that it isn’t until the fourth course that you really see why you would choose R for data analysis over using Excel, SQL Server Analysis Services or other options out there.

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