Becoming a Data Scientist podcast, Partially Derivative podcast, Adversarial Learning podcast, and some other awesome data people that do elections forecasting for their day jobs joined together for this talk about the US election and the subsequent major questions surrounding the predictions, since basically all of them heavily leaned toward a different overall outcome than we got. If you’re interested at all in data science surrounding political campaigns, this episode is a must-listen!
I just got back from PydataDC, where I learned a lot, had fun, and met a bunch of awesome people! I’ll definitely write about it more later, but I wanted to share my slides here since I told the attendees they could find them on my website. I got good feedback on the talk, and I’m so glad that my message resonated with some people! The talk was recorded and video should be out within a few weeks! Here are the slides: Becoming a Data Scientist – Advice from my Podcast Guests and the slide notes. Update 10/26: Here is the recording of my talk, with a playlist of other talks from PyData...
Here is the first episode of the Becoming a Data Scientist Podcast, which is also available in video form!
(sorry for the poor video quality!)
In this episode, I talk a little about the podcast, I talk about my own background, and I introduce the Data Science Learning Club. Enjoy!
(Note: Episode 1, the first interview episode, comes out Monday 12/21!)
Podcast Video Playlist:
Youtube playlist where I’ll publish future videos
More about the Data Science Learning Club:
An organization based in Puerto Rico called “Broadening Participation in Data Mining” (BPDM) interviewed me over the weekend, and it’s online now! Without further ado…. Thanks to Orlando and Herbierto for having me on! (P.S. I did put up the post about Data Sources on DataSciGuide)
I love finding and sharing good articles about data science related topics on twitter, but I know not everyone is on twitter, and also sometimes tweets get quickly lost in the timeline and they’re easy to miss. So, I’ve started sharing the best articles via a Flipboard magazine as well! Check it out! https://flipboard.com/@becomingdatasci/becoming-a-data-scientist-5ktft1lky
You may have seen me tweeting about some research I did on “Data Visualization for Exploratory Data Analysis” for my Cognitive Systems Engineering course. My presentation went really well! I’m less satisfied with the paper since it was done in a hurry to complete the project deliverables, but i’m including it because it explains some things that aren’t obvious from the powerpoint without my commentary. Principles of Data Visualization for Exploratory Data Analysis [presentation – pdf] Principles of Data Visualization for Exploratory Data Analysis [paper – [pdf] Check out the references in both documents for some good resources. I’ll include some links in the post below, too. I had a lot more material from my research that I wanted to include and just didn’t have time to in a 15-minute presentation! The professor was happy about the topic I picked because she’s teaching a class on Data Visualization next semester, so I think that worked out in my favor :) These two books by Stephen Few covered the very basics of visualization for human perception: Show Me the Numbers: Designing Tables and Graphs to Enlighten Now You See It: Simple Visualization Techniques for Quantitative Analysis Blog posts about related topics: Six Revisions: Gestalt Laws eagereyes: Illustration vs Visualization Detailed visualization of NBA shot selection Publications and articles: IEEE Transactions on Visualization and Computer Graphics Toward a Perceptual Science of Multidimensional Data Visualization: Bertin and Beyond by Marc Green, Ph. D. Scagnostics by Dang and Wilkinson Generalized Plot Matrix (GPLOM) by Im, McGuffin, Leung UpSet: Visualization of Intersecting Sets by Lex, Gehlenborg, Strobelt, et al. …and there are more resources in the paper and presentation files! (and if you’re REALLY interested in this topic, post a comment and I will add even more links I have bookmarked) I also did a project using some data from my day job related to university fundraising and major gift prospects, but unfortunately I can’t share that study here because I don’t have permission to do so. It included some cool visuals like bubble charts, and also an interesting analysis of movement through the prospect pipeline using Markov Chains. I learned a lot doing that one! It was nice to end my final semester of grad school with two data-related projects! (Yes, I’m finally graduating! Masters of Systems Engineering! woo...