Statistics

The term “metadata” gets thrown around a lot in the business world. For example, my dad (who works in marketing) recently was in a meeting… — The term “metadata” gets thrown around a lot in the business world. For example, my dad (who works in marketing) recently was in a meeting where someone referenced metadata. After the meeting, he reached out to a co-worker to ask how they would define metadata. The co-worker responded by saying…

Little known outside the community, Jane Street is one of the world’s largest market-maker who trades more than $17 trillion worth of… — Little known outside the community, Jane Street is one of the world’s largest market-maker who trades more than $17 trillion worth of securities in 2020. Yet, the company is also well known for its difficult interview questions. …

How to examine and evaluate models for time-to-event data — Selecting a suitable model for the time-to-event data of interest is an important step in survival analysis. Unfortunately, with the sheer number of models available for survival analysis, it is easy to become inundated by all of the available information. While it might be tempting to meticulously study every modeling…

A step by step guide to forecasting the future — Be sure to SUBSCRIBE here to never miss another article on data science guides, tricks and tips, life lessons, and more! Introduction With the emergence of big data and machine learning, predictive modeling has never been more prevalent in businesses. In fact, it’s almost essential for businesses to leverage predictive modeling…

And other silly thoughts about our work — Saying a data scientist is not like a data analyst is beside the point. Also, it is a bit like saying a car isn’t like a wheel. Read below for more silly things Data Scientists sometimes say. Introduction A statistician, a business analyst, and a coder walk into a bar. The…

Why it’s one of the most important techniques, and how to use it — What is it? Regularization is a type of technique that calibrates machine learning models by making the loss function take into account feature importance. Intuitively, it means that we force our model to give less weight to features that are not as important in predicting the target variable, and more weight to those…