In the first episode of the Becoming a Data Scientist podcast recorded in front of a live audience, Renee interviews Andrew Therriault – formerly the Director of Data Science for the Democratic National Committee & Chief Data Officer for the City of Boston, and currently Data Science Manager at Facebook – about how he learned data science, what advice he has for people who want to learn data science and apply for data science jobs, and about his career path as a Data Scientist and leader in the field.
Category: resources
RVATech Summit Slides
I promised the audience at the RVATech Summit yesterday that I’d post the updated slides for my “Can a Machine be Racist or Sexist?” talk, so here they are! Here is the link to the previous post, which has a pdf version of the slides that’s almost identical, and a video from when I gave… Continue reading RVATech Summit Slides
APRA Data Analytics Symposium 2018
Hi, #APRADAS2018 Attendees! I plan to come back and add more info here in the future, but for now, here is a PDF version of my slides: My Journey from Advancement Data Analyst to Data Scientist
Summer of Data Science Goal-Setting
The purpose of the Summer of Data Science is to learn a specific topic or complete a specific project or read a book or finish a course so you can check something off of your long data science “to learn” list, and have fun achieving goals along with other data science learners during a fixed period of time. The deadline should be motivating, to get you to start and finish something before the summer is over.
Week 1 was all about brainstorming ideas and gathering resources – dreaming up what you’d love to learn, and finding content that will help you learn it.
Week 2 (which started yesterday, but don’t worry, jump in any time even if you see this a month from now) is all about goal-setting.
Summer of Data Science 2018 #SoDS18 Launch Day!
The main goal of the Summer of Data Science is to learn something new during a fixed period of time, and share your progress and references to help and inspire others (and to get help from and get inspired by others, too!).
Can a Machine Be Racist or Sexist?
I presented a talk with this title at the Applied Machine Learning Conference at Tom Tom Fest in Charlottesville (which I also helped plan) last Thursday April 12, 2018.
My interest in this topic started long ago, and I partially based this talk off of my blog post “A Challenge to Data Scientists” from 2015. There are a ton of links throughout, and I included the slide notes so you have those along with the presentation…
Podcast Listens Analysis
I’ve been telling everyone that I’d do something “data fun” when I hit 20K Twitter followers, so I posted an analysis of my podcast listeners! I used python and pandas in a Jupyter notebook for the first part, then I did a dashboard in Tableau for the last part.
Hi #DemystifyDS Attendees!
I’m assuming that some people who see my talk at Demystifying Data Science conference will be dropping by here, so I wanted to put up a quick post summarizing some of the resources I have made available to data science learners!
2 Quick Announcements
1. Sorry for the issues loading images and with logins on this site. I’ve had problems ever since I had bluehost set me up with HTTPS certificates, and apparently those certificates have expired or something and are causing issues with the images being able to load, etc. I’ll look into it, but I also have… Continue reading 2 Quick Announcements
Introductory Machine Learning Terminology with Food
I was just pondering some ways to discuss machine learning terminology in a way that would be accessible to beginners, and figured I’d share my semi-thought-out ideas here. I’m sure this has been done before, but here are some common machine learning terms couched in the language of cooking and food. Feedback welcome!
Machine Learning Algorithm
A machine learning algorithm is a list of instructions to guide a computer to analyze some data to find patterns, and works much like a cooking recipe. You put some data in (ingredients), do some stuff to it (preparation and cooking), and then evaluate how the results compare to what you were hoping to accomplish (photo in your cookbook and expectations of taste).