In early 2018, I gave a few conference talks on “The Lesser Known Stars of the Tidyverse.” I focused on some packages and functions that aren’t as well known as the core parts of ggplot2 and dplyr but are very helpful in exploratory analysis. I walked through an example analysis of Kaggle’s 2017 State of Data Science and Machine Learning Survey to show how I would use these functions in an exploratory analysis.

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When I was working at Etsy, I benefited from a very robust A/B testing system. Etsy had been doing A/B testing for more than 6 years. By the time I left, Etsy’s in-house experimentation system, called Catapult, had more than 5 data engineers working on it full-time. Every morning, I was greeted with a homepage that listed all the experiments that Etsy had run in the prior four years. When you clicked on one, you got a summary of what the experiment was testing (usually written by the product manager).

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When interviewing for any position, you should be evaluating the company just as much as they are evaluating you. While you can research the company beforehand on glassdoor and similar sites, interviews are the best place to get a deeper understanding of the company and ask important questions. Companies will never straight up tell you they are bad to work for, so you have to look for the signs yourself.

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Following Dave Robinson’s sage tweet to write a blog post when you’ve given the same advice three times, this post is a collection of my thoughts and recommendations for people interested in applying to data science jobs in the US. Many of these principles also apply to tech jobs in general. A disclaimer: I have never worked as a recruiter or career coach. This knowledge comes from mainly from my study of Organizational Behavior (including negotiations and women in tech) in graduate school and my own career.

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In my last post, I discussed the importance of building your network and some strategies for effectively reaching out. I closed with emphasizing how helpful your peers or people one step ahead of you can be. But there’s a specific area where people with more resources, status, or experience can help you: sponsorship. What is sponsorship? When people discuss what they’re seeking from a more senior person in their field, they usually talk about “mentorship.

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Emily Robinson

Date Scientist at DataCamp

Data Scientist

New York