# MOOC: The Data Science Specialization

I’ve long wanted to take continuing education classes through a massive open online course (MOOC) provider. I decided that I’d attempt a programming class for for my first venture into the world of MOOCs.

I’ll let you in on a little secret: prior to starting the Coursera Data Science Specialization last autumn, I had never done any programming of any kind. This put me far behind the class curve. Further compounding that mistake was the fact that I chose to start my programming education the hard way, with R, a programming language often used in statistical analysis, particularly in healthcare.

So why R, of all things? Because I’m often handed a clinician’s conclusions regarding a dataset, and I wanted to be able to go back and understand how they came to those conclusions, and whether or not they were accurate. After all, the closer I can get to the raw information, the more effectively I can communicate the outcomes.

The course is run by three professors from the department of biostatistics at Johns Hopkins Bloomburg School of Public Health at Johns Hopkins University. When I registered, the specialization said that no experience was necessary. This was comically untrue. It has since been updated to say that *“Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).”* This, in my opinion, is a necessary update. While I had no problems understanding the course concepts, or even the mathematics involved, I struggled endlessly with things that would have been so incredibly basic to anyone who had programmed before, such as *How do you load a dataset into R Studio*?

Courses included:

- The Data Scientist’s Toolbox: an introduction to the world of data science
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Capstone: a final project intended to pull together the lessons learned in the nine courses

The courses were good, and gave me insight that I wouldn’t have otherwise had. However, there were some challenges with the class structure. For example, I would watch the Week 1 lectures, try to complete the Week 1 project, and find myself thoroughly baffled by what needed to be done. Yet I’d later discover that the Week 3 lectures covered the topic that I needed to know to successfully complete the Week 1 project.

After a few courses, I learned that the only way to approach the classes was to clear my schedule, binge-watch all four weeks of lectures at once, and then proceed with the course work. It made the process dramatically easier.

I also learned that even all these years after school, I still stress about grades. I finished the course with eight 100% scores, a 98.3% in Reproducible Research, and a 97.5% in the capstone; and yes, the capstone grade still rankles a bit.

Would I do it again? Yes, I’ll consider it, though I think I’ll select topics where I have at least a basic level of understanding about the subject. I don’t think it’s an ideal learning environment for someone who is entirely new to the topic.

Have you used MOOCs to expand your knowledge? How did it work for you?