R PROGRAMMING

Life is precious and fleeting. You never know when it will end, so don’t take it for granted. This is something I have done for far too long.

I’ve never liked the saying, “Live each day as if it’s your last.” If that were the case, I would drink four milkshakes a day. That’s too many shakes for one person.

Living that way means you’ll never work for tomorrow, which is incredibly important and something I am finally doing.

I have taken the bull by its horns (also not a good literal thing to do) and stopped making excuses for why I’m not doing things. And I have to say, it feels good. I am more motivated than ever.

It’s not easy, but it’s not supposed to be. And that’s okay.

Now that I have completed my awkward pep talk, you’re probably wondering what I have done since last week on my quest to become a data scientist.

I took two days off from coursework this week, so I didn’t get as much done as I normally do. One day was spent fixing a bunch of problems on my website. The other day was devoted to working on a digital art design for a Photoshop contest with Phlearn.

I know, the latter isn’t all that important, but I really enjoy creating images with Photoshop and I haven’t done so in quite a while. It was fun to make, but I did screw up some of the shadowing. Alas, I did not win, but it is good to break up some of the monotony to prevent getting burnt out.

So what have I done with the remaining part of the week?

I am 11% of the way through geometry. I think the last four days straight have consisted of nothing but geometric proofs (i.e.: proving that the orthocenter, circumcenter, and centroid of a triangle all sit on the same line). They are important, but I kind of despise proofs. Geometry is more vital to everyday life for most people, but I have always preferred algebra. I find it more enjoyable, anyhow.

Sal from Khan Academy explains everything really well and the lectures are awesome, but again, I am not fond of proofs and look forward to getting back into calculations.

My data science coursework with Coursera is moving along. I completed my first introductory course, in addition to the first week of my R Programming class.

So far, the course has involved the history of R and learning commands and arguments to use in R for different functions, such as assigning variables, creating a list, vectors, and data tables. I still don’t know what I’m doing, but we have only touched on it so far.

At the end of the week, there were a series of optional courses provided. They are Statistics with Interactive R Learning, or SWIRL for short.

I’m not sure why they are optional, because I think they are an extremely valuable learning tool, especially for someone without prior programming knowledge. I was a bit leery at first because each module says it takes 3 hours, but they weren’t nearly that long.

SWIRL is a system created by Nick Carchedi, a student at Johns Hopkins University. It runs through the R console and allows you to learn R in a practical manner. It explains what needs to be done, then provides the command to accomplish that task. As you go along, it tells you what to do and you need to utilize the information you have learned in order to carry out the task. If you continue to make mistakes on a given question, it provides you with feedback on exactly what needs to be done (I also appreciate the encouraging responses when you do something correctly).

I think it’s fantastic and I wish there were more modules. There were 7 optional ones for this week that I completed and 8 more for future weeks, which I haven’t attempted.

That is where I left off.

By next week, I’ll be out of geometric proofs (hopefully) and I should be finished with at least week 2 of R Programming.

I’m still feeling really good about my decisions and I can’t wait to learn more!