Diving Into Data

2 months ago | divingintodata.com | Tessa Xie

Let me explain the subtitle first. This is not one of those feel-good posts where I PROMISE new writers that the success can be replicated; I personally hate those because they ignore the survivorship bias and the luck factor in success and suffer from the “. But before you lose hope and click away, while the tips here CANNOT guarantee views and income on your article, they DO increase your CHANCE of being successful on the platform. So, I have been writing on Medium since the beginning of 2021.

2 months ago | divingintodata.com | Tessa Xie

In the last article in the series (Concepts You Have to Know for Data Science Interviews — Part I: Distribution), I touched on the basics of distributions — the most important distributions and their characteristics that might show up in data science interviews. In this article, I want to continue the tutorial with common probability questions that companies like to ask DS candidates.

2 months ago | divingintodata.com | Tessa Xie

After transitioning out of quant finance, my first experience as a data scientist was in consulting. Most of the feedback I received in my early days at McKinsey was not related to my code or technical skills, but rather consisted of advice like “you need to tie your work to the higher-level priority of the company/organization”, “you should add more crisp insights” or “you need to be more of a thought partner”.

2 months ago | divingintodata.com | Tessa Xie

This is the 4th article in the interview series. I’m hoping this series will function as a centralized starting point for aspiring data scientists in terms of interview preparation. So far, we have talked about the following concepts:Part I: DistributionPart II. ProbabilityPart III. Basic Supervised Learning ModelsIn this article, I want to continue the journey down the ML lane and talk about advanced supervised learning models.

2 months ago | divingintodata.com | Tessa Xie

“We have observed a drop in our core metric, what is going on?”“What are the drivers for churn?”As data scientists, we encounter questions like these every day. A stakeholder comes across something they want to understand better, and they look to us to take an open-ended question like this and make sense of it. However, even seasoned data scientists are sometimes stunned by just how vague or open-ended these questions are.