Whether a data scientist is just beginning her professional career, or she is already a seasoned professional, working at a startup offers a number of advantages. Most startups are more hands-on, and usually most employees are involved in many aspects of the company.

At Bigabid, ensemble learning methods are the weapons of choice when it comes to our machine learning (ML) architecture. As ensemble learning methods combine multiple base models, together they have a greater ability to produce a much more accurate ML model.

When you implement the Data QA process we’ve outlined here, you’ll be amazed by the number of bugs that exist in the data writing process that you’ve never even noticed.

The most obvious issues are related to computational efficiency, and the inability to visualize high-dimensional data. In this article, we’ll dive into the technicalities of PCA to help you better understand the model and its uses, benefits, and limitations. We’ll also explore some extensions to PCA. 

Although feature stores play a vital role in data strategy, it’s still difficult to find information about them online. But understanding what feature stores are and why they’re important is crucial, especially in today’s world of increasing data governance, and business problems being increasingly solved by machine learning models.

Are you interested in getting into the field of data science? We don’t blame you. Data science is an exciting field that’s constantly changing and developing, which gives data scientists’ work endless potential.

Imbalanced classes are a common problem in machine learning classification, where there’s a disproportionate ratio of observations in each class. In this article, we offer guidelines for working with imbalanced datasets.

A/B testing is usually the first choice for people aiming to optimize an ad campaign setup. Instead of relying on guesswork, with A/B testing you can make choices based on more scientific data. 

In order to ensure that the work of data scientists is well-managed and delivers impactful results, our approach focuses on defining a set of possible outcomes for each data science task.