Python-bloggers

Data scientists often face a puzzling challenge. Their carefully crafted Principal Component Analysis (PCA) implementation gives different results compared to scikit-learn’s version. These differences create confusion and make debugging harder, even when both implementations appear mathematically sound. PCA is a significant dimensionality reduction technique in machine learning that helps ...

The last five days! On Day 25, we compared the peformance of the adjusted vs. unadjusted strategy for different prediction scenarios: true and false positives and negatives. For true positives and false negatives, the adjusted strategy performed bet...

If you’ve worked with statistical data, you’ve likely encountered z-scores. A z-score measures how far a data point is from the mean, expressed in terms of standard deviations. It helps identify outliers and compare data distributions, making it a vital tool in data science. In this guide, we’...

Developing a Shiny app is relatively straightforward, but deploying it to a production environment is a whole another set of challenges. If you’re unfamiliar, R Shiny is a powerful web application framework for building interactive data visualizations, dashboards, and other data-driven applications. At Appsilon, we’re a big fan ...

If your organization is looking to build dynamic and interactive dashboards with a strong emphasis on performance or large scale, the Observable Framework could be an ideal fit. This article introduces the framework, its core features, and real-world use cases to illustrate how it can help your data science team ...

Quite often, the involvement of a designer in a project is temporary, depending on the budget, deadlines, planning, etc. This approach is mainly used in projects with small budgets or at the POC stages, where a working prototype needs to be built to test hypotheses. From a utilization standpoint, this ...

In this edition of Shiny Gatherings x Pharmaverse, we were thrilled to welcome Nina and Dony, Principal Data Scientists at Genentech, for an insightful session on building clinical data analysis apps using the {teal} framework. Explore how {teal} and {pharmaverseadam} simplify clinical dashboards for faster insights. Learn how these tools ...

On Day 24, we explained in detail how the error correction term led to somewhat unexpected outperformance relative to the original and unadjusted strategies. The reason? We hypothesized that it was due to the the error term adjusting the prediction ...

Top firms accept only 2% of candidates for quantitative research internship positions. Landing these roles is incredibly competitive. Linear regression questions show up in almost every technical interview round. Your dream internship offer depends on how well you understand everything from simple assumptions to complex financial applications of linear regression. Data ...

On Day 23 we dove into the deep end to understand why the error correction we used worked as well as it did. We showed how traditional machine learning uses loss functions and then hypothesized how our use helped improve predictions through its effe...