CS 373 Spring 2021: Brinda Prasad
What did you do this past week?
I’ve been meeting with my group a bit less than the last phase, but we had a bit of a head start on this phase since we had implemented sorting during Phase 2. For this phase, I mostly worked on writing unit tests and GUI tests. I’m really happy with how everything turned out on the deployed website. I also finished up doing Perusall for Paper #12 and my partner project for OOP. Other than that, it’s been a pretty relaxed week.
What’s in your way?
I’m feeling really good about this week! If anything’s in my way, it’s probably how hard I’m finding it to pick up the pace and get more things done during the day, but I’m not too worried about it at the moment.
What will you do next week?
My group and I will probably meet early this next week to plan out our issues for Phase 4 of IDB. I’ll also be starting my last partner project for my OOP class this week. Other than that, I’ll be trying to frontload my work for classes before the chaos of finals starts to kick in.
If you read it, what did you think of the More getters and setters?
I’m glad we’re continuing to read about getters and setters. Even though the concept of working around using getters and setters for good object-oriented design can be hard to grasp at first, it makes total sense.
What was your experience of SQL?
I’m really enjoying learning about SQL! It feels very intuitive and the HackerRank wasn’t too bad. I think SQL is super useful and applicable, so I’m glad we’re going over it in class.
What made you happy this week?
I love that Prof. Downing always reminds us to get outside if we can at the end of class, because I usually need that reminder! I’ve definitely been enjoying the outdoors this past week when the weather’s been nice. It finally feels like summer, so I’m looking forward to more and more time in the sun!
What’s your pick-of-the-week or tip-of-the-week?
My pick of the week is Coded Bias, a documentary (it’s on Netflix) about biases in facial recognition algorithms and artificial intelligence. While it’s not directly related to our class, I think it’s important that we recognize these biases and how they might manifest in our code. One thing in particular I think we should keep in mind is how skewed/biased data sets can further perpetuate existing biases/prejudices.
