One more note about my programming studies:

I found out there isn't a way to "study" programming. No amount of detailed note taking really improved my ability to do anything with the R programming language. Passive viewing of lectures, writing down very basic rules, and hands-on practice are the only ways I've been able to gather precious nuggets of programming knowledge. It's weird and feels very different from typical study methods for academia. I remember memorizing over half of the content that made up my college degree. History, writing, basic finance and economics formulas, trig identities, etc. were all memorized and hardly understood at anything remotely close to first principles.

Anyway, back to work. For real this time.

The Road to Data Science Mastery: Part 2, Programming (aka going nuts)

This is what programming feels like to someone who isn't well versed in the field:

1. You learn about objects, vectors, functions, and for loops.
2. You begin an assignment feeling like a god.
3. You find out that your first lessons in programming didn't truly prepare you for true programming work. It's like being given a few lessons in karate and then being sent off to fight Bruce Lee. (Exaggerating, obviously)

It's a roller coaster of emotions and hissyfits. I thought programming was just pure logic. And you know what? It really is about logic. But there's a creative streak to programming in that you can accomplish a task through multiple methods. So, in a way, programming feels kind of artistic and can be difficult when you're not feeling creative.

It's all worth the effort though. I've never felt such accomplishment from any other subject I studied and practiced. 

Whelp, time to go back into isolation and finish my programming assignments!

P.S.

I should've made a post titled, "The Road to Data Science Mastery: Part 1"  and written about why I want to pursue data science. Oops. 

Oops, I did it again. Repeated a college mistake.

I got sidetracked from my Coursera studies and now I'm paying the price by burning the midnight oil to make up for lost time. I spent two days of very valuable time doing research on which $400ish laptop to purchase. I made mistakes such as this back in college and paid dearly when it came to coming to quizzes without any proper preparation. 

Thankfully, Coursera courses offer a few "late days" that serve as due date extensions in case the student had an unavoidable schedule conflict. I didn't want to use them so early on in the courses, but it looks like I'll have to burn them if I want to get full credit on my assignments and quizzes.

By the way, the courses I'm currently working on are:

1. America's Written Constitution 
2. Surveillance Law
3. Statistics (not the actual course name)
4. R Programming <---- fun class so far, but it can be a bit challenging when it comes to understanding everything. I'm trying to force myself to sort of "skim" over material and trust that the hands-on assignments and tutorials will solidify my understanding of basic R programming concepts. 

If you're curious to see what the coursework involved and what a verified certificate looks like:

You can view my course record here:

https://www.coursera.org/records/RxMP2uVCFbKvaqTY

And here is my certificate:

https://www.coursera.org/maestro/api/certificate/get_certificate?verify-code=6FE95U4VCA


It wasn't the most difficult course. Anyone can pass this class with distinction by simply following the instructions. But you know what? It feels good to be done with the first class of the Data Science Specialization.

Onward to learning R Programming and sharpening my knowledge of statistics!

Got some grades back from Coursera...

I passed my Data Scientist's Toolbox course with no issues whatsoever! Well, except for the first quiz where I missed one point on a question and forgot to retake the quiz. Uploading the Coursera certificate to my linkedin and other online profiles may not affect my life much, but it'll feel so very satisfying.

On another note, I'd like to mention one neat feature for work verification. Each student had to grade four other students' projects on both content and legitimacy. If the graded work looks suspicious, then students can report the work and add comments about any funny details they noticed. I'm guessing a minimum of these submissions are required to alert Coursera's staff. I might write a little more about this grading feature when I learn more about it.