Strategies for keeping your cool at the whiteboard

Two people fencing.
Two people fencing.
Photo by Micaela Parente on Unsplash

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 that I’ve been through a handful of them (some passed, some failed), I have some perspective on how to approach them with less dread and more positivity.

First of all, I want to thank all the noble souls who have conducted my technical interviews. It can’t be easy to navigate between blurting out solutions and withholding reasonable assistance, or between probing to assess someone’s skill level and just antagonizing them. All of my technical interviews have been respectful and professional, and some have even been friendly and fun! …


Building my own bridge from student to professional

A hand adds a sticky note to a group of sticky notes on a wall.
A hand adds a sticky note to a group of sticky notes on a wall.
Photo by Kelly Sikkema on Unsplash

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 cover everything a data scientist needs to know, and that’s not really the point. Flatiron School’s stated objective for the bootcamp is to introduce students to a wide range of material and teach them how to learn new skills on their own.

After graduating and while starting my job search, I set out to fill some gaps in my preparation. Reading job ads taught me a lot about what employers are looking for and what specific skills were required for the sorts of jobs that appealed to me most. In this post I’ll share some of the resources I have used post-bootcamp to broaden and deepen my data science knowledge. …


Tips for making “homework” work for you

Brown takeout box with wire handle
Brown takeout box with wire handle
Photo by Kelly Sikkema on Unsplash

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 you can deliver your best work in the time allotted.

Not everybody is a fan of take-home projects, especially if you won’t be paid for the time you spend. I attended a panel recently where one presenter said he always declines to do take-home projects and instead offers to discuss over the phone how he would approach the project. How you respond to a request to do a take-home project is up to you, of course. Personally, I have found them to be a good opportunity to show that I really know how to do the things I say I know how to do. …


Detecting subgenres in 1,000+ music reviews

Pink headphones on a pink and green background.
Pink headphones on a pink and green background.
Photo by Icons8 Team on Unsplash

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 album.

Recently, I came across a collection of over 18,000 music reviews scraped from Pitchfork on Kaggle. The possibilities for analyzing how Pitchfork writers write about music were too good to resist. Today I’ll walk you through how I modeled topics on the subset of reviews about music in the “Pop/R&B” genre. …


Networking at a distance for the pandemic job-hunter

Drawing of hands touching
Drawing of hands touching
Photo by 🇨🇭 Claudio Schwarz | @purzlbaum on Unsplash

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 months of surviving on babysitting gigs, tutoring sessions, and credit cards, a friend recommended me to her manager at Starbucks, and I finally got a reliable part-time job. …


Getting creative when the data you want isn’t there

A row of blue shopping carts in front of a yellow wall.
A row of blue shopping carts in front of a yellow wall.
Photo by Fabio Bracht on Unsplash

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 handy visualizations to help me communicate my findings. In this post, I’ll walk through how I adapted RFM (recency, frequency, monetary) analysis for customer segmentation on the Instacart dataset. …


Behind the scenes with a web scraping and data visualization project

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 some data about pinball machines led me on an adventure that ended with an 80s-themed dashboard (below). In this post, I’ll walk you through how I built Pinball Wizardry, which took about 12 hours over the course of a week.

Screenshot of a Tableau dashboard.
Screenshot of a Tableau dashboard.
View the interactive dashboard at Tableau Public.

Inspiration

It all started with the weekly e-mail from Data Is Plural, by Jeremy Singer-Vine. In this newsletter (and in the Google Doc where all the newsletter content ultimately gets compiled), Singer-Vine shares interesting datasets on a dazzling array of topics, all of which are more or less open and free to use. A recent newsletter featured Pinball Map, a “crowdsourced map of public pinball machines” (per the website). …


Review of a handy tool for your machine learning workflow

Image for post
Image for post
Photo by JR Kreiger, 2016.

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, a package for model visualization. I tried it, liked it, and now incorporate it into my machine learning workflow. In this post, I’ll show you a few examples of what it can do (and you can always go check out the documentation for yourself).

Fast facts

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Applying NLP and neural networks to a tricky text-processing problem

Fictitious product review from an enthusiastic cat lover.
Fictitious product review from an enthusiastic cat lover.
Was this helpful? Princess Foofie wants to know.

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 about.

What if a business could predict which reviews its customers would find helpful? Maybe it could put those reviews first on the page so that readers could get the best information sooner. Maybe the business could note which topics come up in those helpful reviews and revise its product descriptions to contain more of that sort of information. …


Is your discount strategy driving sales, or just costing you money?

I bet you know this feeling: an item you need is on sale, so you gleefully add it to your cart and start thinking, “What else can I buy with all this money I just saved?” A few clicks (or turns around the store) later, and you’ve got a lot more in your cart than you came for.

This is such a common phenomenon that some retailers openly exploit it. Amazon has those “add-on” items that are cheaper (or only available) if you add them to an order of a certain size. I’m pretty sure I’ve heard Target ads that crack jokes about the experience of coming to the store for something essential and leaving with a cartful of things you didn’t need but wanted once you saw what a great deal you were getting. …

About

JR Kreiger

Data scientist with a background in archaeology, museums, and libraries. On Twitter @j_re.

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