Learn why implementing a metrics layer will make gathering data-driven insights much more robust in your organization! — Metrics layer is a framework that empowers organizations to unlock valuable insights and drive data-informed decision-making by consolidating, analyzing, and visualizing key performance indicators in a unified and intuitive manner. In this article, we’ll explore the significance of the metrics layer, its benefits, key differences as compared to the semantics…

Learn how to improve the fit of your linear models by making them more flexible to trend changes — Modeling time series data can be challenging (and fascinating) due to its inherent complexity and unpredictability. For example, long-term trends in time series can change drastically due to certain events. Recall the beginning of the global pandemic, when businesses such as airlines or brick-and-mortar shops saw a quick decline in…

Learn how to make your linear models more flexible by including interaction terms — Linear regression is a powerful statistical tool used to model the relationship between a dependent variable and one or more independent variables (features). An important, and often forgotten, concept in regression analysis is that of interaction terms. …

The bare minimum guide to get you started with experiment tracking — This article is the third part of a series demonstrating how to utilize DVC and its VS Code extension for ML experimentation. In the first part, I illustrated the entire setup of an ML project and demonstrated how to track and evaluate experiments within VS Code. …

An extensive reference into commonly used regression evaluation metrics and their practical applications across various scenarios — As a data scientist, evaluating the performance of machine learning models is a crucial aspect of your work. To do so effectively, you have a wide range of statistical metrics at your disposal, each with its own unique strengths and weaknesses. …

What are the pros and cons of 7 leading tools — Building machine learning models is a highly iterative process. After building a simple MVP for our project, we will most likely carry out a series of experiments in which we try out different models (along with their hyperparameters), create or add various features, or utilize data preprocessing techniques. …

Part 3 of the series exploring the hidden gems of pandas — I am quite sure that pandas does not need an introduction. In the third article of the series, we will continue to explore some of the useful functionalities of pandas that you might not have heard about. In case you want to check out the previous parts, you can find…

Enhance Your Productivity with These Must-Have Tools! — As the field of data science continues to grow and becomes more and more integral to modern industries, the need for tools facilitating data analysis has grown accordingly. One tool that has gained popularity among data scientists is VS Code, an integrated development environment (IDE) with a wide range of…

Part 2 of the tutorial on how to run and evaluate experiments without leaving your IDE — In the previous article of this series, I demonstrated how to use DVC’s VS Code extension to transform our IDE into an experimentation platform, allowing us to directly run and evaluate ML experiments. I also mentioned that the extension offers useful plotting functionalities, which enable us to visualize and evaluate…