Group 2 Notes

Notetaker: Haylie
(Callen, Jacob, Haylie)

Mel McGee
We noticed less questions were asked since she talked about Scrum mostly.
We felt it would have been beneficial if she talked more about jobs and real world situations rather than just one topic that is possible within the tech field.
I (Haylie) found the Scrum talk beneficially because this past Monday I came into work and my team was doing agile training so I joined in and actually knew a lot about it already so it helped me better explain terms and ideas.
Overall we both really enjoyed her as a speaker as we felt she gave an engaging presentation. It’s also always interesting to hear about the women in tech topic (being women in tech).

Women in Tech

Women in tech is important because diversity creates a much better environment for work. The different backgrounds and experiences help to create quicker and more creative results for a problem or project.

Women in tech also creates the conversation for all kinds of diversity. Not just women are needed in tech but all kinds of people in regards to race and even economic backgrounds.

Callen touched on how John Carroll is around 87% white and in our CS classes that percentage reflected, at least in the upper classes (Junior and Senior).

Overall diversity is so important in any walk of life. Differences don’t create disagreements, just more options and paths to go down.

Data Science
It’s a very interesting field. It’s a more emerging field. Callen talked about a coworker who is doing data science work and she asked him about how long he’s been working on it and his response was “No we’ve only been using data in this process for about a year or two”

We both wish John Carroll had more data related classes.

Relational data is really cool, just being able to type in some queries and get results. It’s a way to manipulate something in that’s not infringing on people or anything its just playing around with the given data.

Data also has a sketchy side.

My computer and phone are synced up so when I look up something on my laptop, my phone also knows to show specific adds related to my searches.

Face recognition is a weird thing.

Alexa and Google and their AI services going into the whole “the governments watching me” type vibe. It’s hard to find the line between what’s an okay amount of data to collect about people and what is an invasion of privacy.

Questions for Cal (relevant to Data Science):

What is Data Science exactly?
What kind of software and tools does he use?
What is his opinion on Open Data?
What languages are important to use?
If you wanted to pursue a career in data science or just a data related position in general but have no educational background of it, how would you suggest getting into it? Is the topic easy to self-teach?