Hello all my dedicated readers. All three of you. You know who you are. (Hi mom!) So maybe it has been a year and a half since I have posted. So maybe the last time someone visited this site was in February (and I think it was me). So maybe I’m guilty of letting this blog languish like my high-school diary, and any crochet project I’ve ever attempted. But I have good news! I’m back and ready to begin again.
I feel like I owe an explanation for my abandonment, so here it is: I decided to double-down on my MBA. As an employee at Rockhurst University, I was able to take Rockhurst up on it’s incredibly generous tuition remission program in order to work on an MBA with an emphasis in Data Science, and that decision was awesome, but it took on a life of its own. A new degree program, the Master of Science in Business Intelligence and Data Analytics opened up and I took the opportunity to go the dual degree route. Because why get one degree when you can get two! In order to graduate within a reasonable time frame, I had to take more hours than I was planning. And quite frankly, some of the classes were extremely challenging and took an inordinate amount of time.
Google the term data science and one of the first things you find out is that is one of the fastest growing fields everywhere. This actually includes libraries. And it’s super cool, and apparently one of the sexiest jobs for three years running. Who doesn’t want a little more sexy?
A Data science librarian is an actual position. (I know, right?) Does a person have to go to school to learn this stuff? Actually no- there is a lot of information out there on the web. Did I have to go to school to learn this stuff? Yes. Oh yes. One thing that I have learned about myself is that I’m deadline-driven, so if there is not some kind of external accountability structure, it doesn’t get done. Cough. <this blog> Cough.
So in a nutshell, I was busy. I was beyond busy. As I’m growing in my R and Python coding and learning about predictive models, text analysis, etc, I’ve been able to apply that to my job. This is beyond amazing, and I hope to discuss these projects at a later date, but working on them was (and is) time-consuming. Not only was I learning when and how to use different algorithms, but also how to translate what I did into plain English. Predictive modeling, text mining, and Hadoop, oh my!
I have learned so many things that I feel like my head is going to explode. I’m afraid to talk about all the ideas about working with data that I have, because if what if I talk and the ideas start piling up and I start talking faster and faster and as I get excited my voice gets higher and higher and all the sudden there is a whoosh, some glitter and I turn into a giant chipmunk? And more importantly, will I be a sexy data science chipmunk? But I’m almost at the end of this crazy journey with my sanity (almost) intact. I am graduating in May, and I am starting to look ahead into what is next. It is going to be a wild ride.
A note: There are also some personal reasons that occurred over the summer and in the fall of 2016. I hesitate to get to personal online, but this was something that I needed to hear, and so I will pass it along in hopes that it will help someone else: If you have to deal with the deaths of close loved ones within a couple of months of each other, the second one will be exponentially worse than the first. It has to do with your grief and the grieving process, NOT whether you loved one person more. There is nothing wrong with you.