Areas of study worth exploring

In my final year of university, I have come to the humbling realization that Data Science is huge. There is an ocean of different tools, information, and most definitely, expertise, that we are expected to learn from. It is in this interdisciplinary nature and the open-mindedness with which you approach a problem that makes me think very deeply about what I really want to learn. Now this is not mutually exclusive to Data Science, but I keep wanting to learn about more tools and areas of expertise. This blog will serve to document areas of Data Science I really want to explore.

Parallel Computing

After using PySpark for about a week or so (to early to write a blog, hey?) , I have sort of realized how important it is to understand what happens inside a computer, and how to fully exploit every core of my computer to make a stupendously large project possible.

Stanford CS149: Parallel Computing seems to be a great resource for this

In order to better understand Parallel Computing, I need to know more about

Computer Systems

I need to learn more about how everything works under the hood in a computer. Processes, threads, CPU Scheduling and Memory Management. I think Silberschatz's Operating System Concepts will be a great resource for this.

Database Systems

There are a couple of resources for this one. But the reason I really want to learn Database Systems is because it is so important for Data Analysis. A lot of companies use RDBMS technologies. I really want to learn more about Relational Algebra, Data Normalization and refresh my Query Optimization concepts.

Stanford CS145 is actually insane for this. It covers a lot of breadth on vectors and stuff. It seems super fun, the way the course website is designed. Almost like a certification.