The dreaded technical interview. Does anything else strike such fear into the heart of the job-seeker? When I started my data science job search, I was downright terrified of technical interviews. They seemed like such minefields, packed with opportunities to look like a dunce in front of someone who could give me a job. Now …
Author Archives: JRK
My post-bootcamp learning plan
Earlier this year I completed Flatiron School’s full-time online data science bootcamp. The program took about 40 hours per week for 5 months, and it covered pretty much everything I expected from a data science bootcamp (plus some things I hadn’t even heard of before I started!). It’s impossible, of course, for any bootcamp to …
How to approach a data science take-home project
You’ve made it past the initial phone screen for a data science job (congrats!), and now they’ve given you a take-home project. It’s your chance to blow their minds, knock their socks off, convince them that you’re a slam-dunk hire—but where to start? When tackling a take-home project, it’s important to be strategic so that …
Continue reading “How to approach a data science take-home project”
Pop X Pitchfork: Topic modeling for music reviews
From time to time I read Pitchfork.com to get new music recommendations. Now, I’m no music snob. If it’s on the Top 40, it’s good enough for me. But Pitchfork’s music reviewers tend to be strong writers and extremely knowledgeable, so it can be really enlightening to get their perspective on a certain artist or …
Continue reading “Pop X Pitchfork: Topic modeling for music reviews”
Online networking resources for women in data science (and everybody else)
During the 2008 financial crisis, I was fresh out of college and looking for a job. It was definitely a challenging time to be job-searching. I remember going out in my interview clothes with a stack of résumés and walking door-to-door in shopping centers looking for hourly retail work without much luck. After a few …
Continue reading “Online networking resources for women in data science (and everybody else)”
Customer segmentation using the Instacart dataset
I recently had the opportunity to complete an open-ended data analysis project using a dataset from Instacart (via Kaggle). After a bit of exploration, I decided that I wanted to attempt a customer segmentation. Luckily, I found an article by Tern Poh Lim that provided inspiration for how I could do this and generate some …
Continue reading “Customer segmentation using the Instacart dataset”
How I built it: Pinball Wizardry
Recently, I was looking for a fun project to help me practice a couple of skills that I don’t get to use very often. In particular, I wanted a chance to call an API, scrape some data from a website, and do some cool visualizations in Tableau. What started off as a chance encounter with …
Quick and easy model evaluation with Yellowbrick
Now and then I come across a Python package that has the potential to simplify a task that I do regularly. When this happens, I’m always excited to try it out and, if it’s awesome, share my new knowledge. A couple of months ago, I was browsing Twitter when I saw a tweet about Yellowbrick, …
Continue reading “Quick and easy model evaluation with Yellowbrick”
Bootcamp by the numbers
I recently completed Flatiron School’s Online Data Science Bootcamp. It was intense and fun and challenging and inspiring, and now that I’ve had a few days to recover, I want to share some data about my experience for anyone who may be considering a bootcamp for themselves.
Predicting the "helpfulness" of peer-written product reviews
Some e-commerce sites let customers write reviews of their products, which other customers can then browse when considering buying a product. I know I’ve read product reviews written by my fellow customers to help me figure out if a product would be true to size, last a long time, or contain an ingredient I’m concerned …
Continue reading “Predicting the "helpfulness" of peer-written product reviews”