One-Third of the Track

coursera-third

Prior to my decision to temporarily suspend all simultaneous and unrealistic activities for 2015, I got to reap a bit from my hard work through this certificate. It really means a lot to me that I managed to crawl through this course during January. It was super challenging to balance it with other things but somehow I knew that this is really something that I want to accomplish.

Tidying data is not exactly a glamorous course. It had this rigorous coursework that I had to spend hours and hours trying to make sense of. I already have some basic ideas on where to use what I learned from this course. Just the potential of the knowledge learned to mix with the other things I know from my other intellectual pursuits fills me with excitement beyond words. It is a lifetime of exploration. I may not necessarily leave my computer for a long time for this, but it’s worth it.

Somehow, the pursuits I have laid out for myself for the last two years are taking shape slowly but surely. I know that I started from a completely different industry professionally, but recent events have shown me that this actually might work for me and it’s worth giving a second look. Heck, I really gave it more than a second look! I am transitioning into whole new realms altogether, a joint output of circumstances and my personal preferences. A lot of people look down on my decisions for work. I met one of them before Christmas and I swore not to subject myself to that kind of company again. Since the year kicked in, I have relieved myself of the need to “CONFORM” to what people call as acceptable or normal work. I am carving out something new and if it doesn’t work, I can just wing it til I make it.

There are so many things I realized about life since last year and one of them is that time is a non-renewable resource. I keep writing about this because it can never be emphasized too much anywhere.

So with three subjects down in this signature track course of John Hopkins, there are six remaining demanding subjects that will claim for my attention in the latter part of the year. I am psyched! I am really looking forward to doing more of my adventures in Python and R this 2015. I cannot see myself thriving in any other way. 🙂

 

 

 

Matrix Mania

From matrix-averse, I am now officially a convert to the cult of the matrix. One of my deepest regrets is not giving matrix mathematics the love that it sorely deserves. Now, I am working with them all the time and I felt so much regret that I did not take those linear algebra lectures in college seriously! Ohhh, the pain of missed chanecs!

Now I needed the refresher more than ever. I can’t turn back time but I can speedread my way to it. I am almost done with the my refresher literature so that I can finally go work on coding with TENSORS in full blast using R.

After getting hooked on all these technical explorations, I had a ‘Where have you been all my life?’ thought bubble in my head! It’s just crazy how late I began in this! It’s just amazing, picking things that Einstein in his genius picked from scratch in his time. It’s exciting and I feel sad that there are few people who appreciate this. Photo files make use of pixel matrices. Cryptography makes use of bit matrices. R programming handles gazillions of big data matrices. LiDAR data uses matrices extensively in automation and machine learning. It’s just great and it is in everything, even in things that people casually just disregard. And there is more than 2 dimensions in a matrix. It can run infinitely and converge somewhere in time and space.

Things move around in a matrix like with infinite possibilities of breaking it down and solving it. It’s a typical “there are many ways to skin a cat” problem. You are not restricted to a single method. You can reduce a huge matrix into blocks, or lower and upper triangular matrix breakdowns, or employ the use of another vector space. I am really genuinely enjoying this.

Resources are everywhere. (I actually need help trimming down my reading list. I have enough books to last me two lifetimes, digitally and non-digitally.) The challenge is really in just making time to access these resources and maximize them. A project-based learning is more apt because you can solve things one at a time and increase the complexity of the problems progressively through it.

 

Fo-R-Sight

A very successful friend who is now the regional director of one of the biggest freelancing websites in the world encouraged me to try R and Hadoop in addition to my Python and Drupal pursuits. He says that with the boom of big data, these skills will become like wine over time. This is what has been eating up all of my time and my life lately. This thing he recommended is probably the best advice of 2014 for Helena.

I was deeply inspired by how he (friend) managed to make things work on his end, so I took his advice and I embarked on a different course of intellectual exploration. I do it during my free time (FYI, I don’t really have one so I am hacking my life to fit it all in! Good luck to my optimization efforts!)

Now, I shall name this post Fo-R-sight, because my attempts to learn R programming is exactly that: a foresight into the future with the data of today.

Forgive the pun; I have been rushing all weekend to finish my basic data science course work.  My eyes are all bleary from the information overload and I am just sitting here, thinking about the marvels of learning from an international university from the comforts of my home.

This month, I believe I hit a personal development milestone when I started learning the basics of R while learning everything else. It’s not yet at par with the world-shaking levels of data that the world’s awesome data scientists are able to manage now. I’m just a tiny mole in the computing universe, and I will always be so. But I’ll be the most awesome  mole I can be, for sure. 

It’s just good enough to make me happy and I find myself very willing to sacrifice a lot to do this. I just know how much I wanted it when I fought to make time for it despite the fragile nature of my schedule.

And now I have new people I want to emulate, career-wise. Years ago, people would ask me who I would like to peg myself as becoming. I have no answer. Now, I have people I look up to, blogs I am subscribed to almost religiously, and things that I want to do with my time. I now view time differently, not as something to be used for lounging around, but something that I should maximize so that I can do as much positive change to the world as humanely and digitally possible.

I used to be so lost. I went to different jobs, swam in office politic shark-infested waters, waded in the worst of my personal issues about myself… And finally, I got to this peaceful and stable point where I can say that I know my game plan for the next five to ten years. I am actually happy at where I am now, professionally. It’s not even about the position; it’s more on the research problems that I am assigned to solving.

Additionally, I thought I was already smart enough with math from engineering, only to find out in my current job that I know nothing and I am such a huge and dumb ape all this time.

LiDAR data makes use of higher-than-vector math and I need to up my game, sharpen my saw, and hit those books. I love it. I love the rush, that adrenaline-boosting intellectual rush that comes when I discover new things to develop from my computer… I hate the tiny things that take me away from my exploration like the usual work politics and inefficiencies, but it’s a painful part of the package and I have to accept that, nay, embrace that on a daily basis so that I can keep doing what I am really doing.

Another prolific friend once told me: “we have our day jobs, and we have the real second job that goes beneath what people see.” I totally agree.

In my job, I had to handle a great amount of high-level matrices to automate certain processes. So learning R for my pet project is like a cherry topping to a wide cake of learning.

R is quite adept at handling matrices. It can do so many things. It’s so powerful that sometimes it scares me. Python also has its own suite of awesome things like Geopandas and other data science-related libraries and I have more than enough to play with at this point in time.

I missed writing so much and there is an overflow of things I wanted to write about. I just could not consolidate them as neatly as those R data frames so it’s a giant dump of things on a blog post before the work grind eats me up again on weekdays.

There’s a bright data-driven future up ahead. 🙂