Over the last year, I realized I wanted this blog to act as a pointer for Me circa 2017. The 21-year-old Jaj who was desperate to find a way amalgamating the tech side with everything she found interesting in the arts, humanities, and sciences. If there was me, there must have been others like me, although the evidence seems to be quite to the contrary. People in my classes, work, general acquaintances have been nothing short of amazed in my investment in the STEAM area. But there must be someone out there.
So here it is. This is what it feels like working towards a Computational Sciences graduate degree.
Let me preface by gushing about the Computational and Data Sciences department in the College of Science. I love walking around the building with its dinosaur bones frozen mid-flight, human skulls adorning forensic department, little cubic tables of the periodic table, and colorful rocks from the geology department …
Which brings me back to my one Computational Social Science class. This was the building where I was taking up Dr. Andrew Crooks’ Introduction to Computational Social Sciences CSS 600 class in hopes of eventually obtaining the Computational Social Sciences Graduate certificate. When I had to register for courses I was floundering. I had no idea which course would work best for me except to go by gut instinct and the practicalities. Luckily, I fell hook, line, and sinker for this Computational Social science class because it was the closest to Digital Humanities that I could get under the wide umbrella bracket of Computational Sciences.
From an Indian undergrad education background, the readings felt immense, long, and overwhelming. I gradually picked up stuff like the Pareto Principle, Zotero-ing, connecting and comparing the papers’ contents by topic. I was in a room full of journalists, neuro-scientists, economics students; Masters and PhD students, experienced professionals and academicians, etc. My competitive streak drew an edge. Nothing like knowing how much you don’t know.
Indeed, I was asked if I wanted to attend Computer Science or Computational Sciences and I picked the more interdisciplinary of the two. Yes, databases are huge but what can be done about them and with them in a narrower context for applicability? Or at least, that was what I hoped Scientific Databases meant to teach me. And it was that, but also so much more. I was learning about the Machine Learning algorithms for similarity much like the Google Art Selfie project’s clustering of 2,500 paintings from around the Western world. I was also learning RDBMS and Non-RDBMS. Which made sense to me since I saw the practical application of MongoDB in a project on event management interfacing chat-bot, which we named PhenoBot (cute, right?). The cherry on the cake is the project I worked on for it. Since the professor also teaches Bioinformatics, the database is a sciency-weincy proteiny string of gobbledy-gook of amino acids. I spent many hours figuring out the Protein Pairwise Alignment Sequencing.
Here’s the presentation:
Numerical Methods (CSI 690), even as I took it, I knew I’ll be screaming for bloody murder of mathematics. No pressure, after hounding the professor, some former students, and hitting the books all nights long, I seem to have a certain grasp of it. It slips every little bit now. That’s okay.
My GPA now stands at 3.67 which feels exquisitely wondrous to relish.
So, that was one semester of classes. I plan on taking up
- Scientific and Statistical Visualization CSI 703
- Agent-Based Modeling and Simulation CSS 610
- Cognitive Foundations of Computational Social Science CSS635
- Origins of Social Complexity CSS620
- Principles of Knowledge Mining CSI 777
- Computational Learning and Discovery CSI 873 / MATH 689
- Social Network Analysis CSS 692
- High-Performance Computing CSI 702
- Times Series Analysis and Forecasting CSI 678
- Visualization and Modeling of Complex Systems CSI 758
- Statistical Graphics and Data Exploration CSI 773
- So many more…
The biggest, most heartwarming, eye-wide-opening fact of graduate school is – Really, everyone must know this – Kindness. I have asked for help multiple times. It was excruciatingly painful to. SO many times, I wished I could have done it by myself. However, it truly take a village – or well, a campus at the very least. I received the most gracious aide of PhD students, fellow classmates, study buddies, teachers, instructors, bosses, librarians LinkedIn acquaintances, friends, colleagues, department heads, students, and I truly, truly could not have done any of this without them. Thank you Universe.