For last week’s meeting we were fortunate enough to have Professor Rekatsinas (firstname.lastname@example.org) come in to discuss his perspective on Data Science. I’d very much encourage you to check out his slides and potentially reach out to him about research opportunities (provided you have programming experience). As we discussed at our research meeting, don’t be afraid to follow up if you don’t hear back in a week!
Shell scripting and being able to use the command line is a critical skill for anyone doing analyses. Many CS students don’t learn how to navigate a terminal until they’re forced to, so it’s definitely a skill worth having. We’ll be joined this week by Professor Tyler Caraza-Harter, who will be leading a workshop on this integral skill.
[DataSci] BASH: The Command Line
Scheduled: Mar 28, 2019 at 6:30 PM
Location: Room 1441, Genetics-Biotech
Right before spring break we met to go over the Do’s and Don’ts of building your résumé. Tech Specialist Amy Yang gave a presentation and discussed keeping a master résumé and customizing it for each company you apply to. She also discussed whether or not you need a cover letter and some caveats for styling your résumé, as well as opportunities for free professional attire. Please see the attach presentation (I promise it’s useful).
THIS WEEK – Résumé Workshop
Do you have a professional résumé? Are you applying for internships and research labs? This week we welcome tech specialist Amy Yang of UW’s SuccessWorks to do a workshop on presenting yourself to employers. We’ll discuss the Do’s and Don’ts of résumé building.
LAST WEEK – Machine Learning Crash Course
Sorry for the delay. Thanks for coming to the machine learning crash course last Thursday! We had ML PhD Finn Kuusisto discuss a variety of different supervised learning models, specifically Neural Networks, SVMs, Decision Trees, k-Nearest Neighbor, etc. Finn also gave some insights on graduate study and some specifics about deep learning.
Machine Learning Postdoc Finn Kuusisto will be joining us to give an overview of machine learning, specifically focusing on a few common models and their applications! This meeting is meant to acquaint you with some new terminology—it is not meant to go in depth nor discuss the intimate details of implementation.
After last week’s meeting with Dr. Richard Barker, I sent out this interest form. This information has been passed on to the Doc, but if you’d like to continue receiving some additional information, please join the #astrobotany channel of our slack workspace, dotDataGroup.
Thanks for coming to the meeting tonight! Here are the presentation slides on getting into research. The slides have some example/successful emails + general tips about labs. Apologies if tonight’s talk was a bit biology-laden; next week’s lecture should be more computational, as our guest lecturer will be giving an overview of Machine Learning.
Big thank you as well to Dr. Richard Barker (email@example.com), who was kind enough to come in and discuss his research. He largely discussed his work and the context in which it occurs, as well as some exciting information about NASA. He also mentioned NASA does internships, which, due to the government security clearance, requires you to be an American citizen. While it’s too late in this semester to add on research credits to your schedule, next semester there will be an astrobotany course in which you can enroll and do research! (great résumé builder)
BARKER RESEARCH — [interest form!]
The research-credited course that Dr. Barker mentioned does not yet have a course number or departmental designation. I will let you all know when it does. In the meantime, please fill out the form so that he can reach out to you.
Thank you to everyone who came last week to see the Genetic Algorithms and SAS presentations! I hope you all managed to get some pizza. :^)
If you missed the meeting or wanted to see the slides again, here is what we covered.
- Some of you asked about a tutorial
- Tetris Example: code and live example (type in tetris on this page once it loads)
If you have more questions about Genetic Algorithms, email Matthew at: firstname.lastname@example.org.
Some reminders from Rachel:
The registration for Student Symposium is November 16th, teams of 2-4 with a faculty advisory will compete in a data challenge with a data set with SAS software to use. The top 3 teams will be highlighted at the Global Forum in April 2019.
SAS E-Learning – Access code is : G70007601
Free University Edition software www.sas.com/universityedition
Video tutorials – https://video.sas.com/category/videos/sas-studio
Free Book on Data Science
Apply to Internships on Handshake or here
If you have any questions for Rachel regarding SAS, contact her at: email@example.com.
Meeting Tomorrow (10/11/2018)
Reminder that we have our meeting tomorrow on Getting into Research + Preparing a Résumé for Internships (7pm in CS1221).
As always, Adithya and I will stick around afterwords to answer any questions you may have regarding classes, as well as discuss ongoing projects. We can give you some personal feedback on your résumé. There may be cookies.
• Bring your résumé if you have one!
Hi Data Science Club,
Thanks to everyone for coming to the meeting! For anyone who wasn’t able to make it to the meeting, here is the presentation that was given. Sorry if I went a bit fast—I’d be happy to elaborate on any points I skimmed (email me or Adithya). For the next meeting, the topic will be:
Résumé Workshop for Tech Internships & Research
7pm on Thursday 10/11 in CS 1221
While this meeting topic is subject to change, I think it’d be good because it’s internship-hunting season.
Right now we’re looking at meeting every other week… But that said, there’s a lot of topics that people are interested in and a lot of things that I’d personally talking about. I’ll be sending out another email with a poll, listing some workshop topics for a meeting. If you have a topic you feel competent in or project that you did & would like to present, email me.
A recap of topics and resources for this past meeting:
I ended up discussing a lot more than I expected about prospective classes to support an interest in Data Science. Besides the slides in the presentation, here are some of the other resources I mentioned:
- Seeing grade distributions from past years, per professor + plots (not exhaustive)
- Grade distributions for the past, by year and by semester (exhaustive)
- RateMyProfessor (not brought up, but these should all be used in conjunction)
Someone spoke to me about internships for underclassmen, and it made me remember a resource I DEFINITELY wish I’d known about—an advisor mentioned it to me this last spring. They largely open up in December, but it’s definitely worth eyeballing now.
CS LEARNING CENTER (Tutoring)
A resource that I didn’t know about until I became the resource: tutoring in the computer sciences building. The word tutoring can have a stigma to it and feels inaccurate in this case, because anyone can just show up and have a pseudo-TA help them debug their programs and explain concepts. They’re guaranteed* to cover every class up to 400. After that, it’s down to what the tutors have personally taken.
Every Sunday-Wednesday there’s tutoring in the lounge immediately above the east entrance of the Computer Sciences building. It’s from 3-9pm for Mon-Wed, and 2-8pm on Sundays. I’m personally there from 6-9 on Mondays, and often from 5-9ish on Wednesdays. If you’d like to get involved as a tutor, contact Andrew Kuemmel!