Key Takeaways -   To understand data science, one needs a lot of technical expertise along with business understanding. Generative AI, MLOps, and clou ...

Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...

A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

In the previous three articles, I explained the mechanism of PCA from scratch. Because you have the experience of manual calculations with NumPy, you understand what the library is doing behind the ...

In the previous article, we learned the basic concept of PCA. Based on the idea of "finding the direction where the data is most spread out," we tried every angle from 0 to 180 degrees in 1-degree ...

Part II: Unsupervised machine learning in R to cluster and identify candidate countries for international expansion, using PCA, K-Means, and DBSCAN.

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

ABSTRACT: This study applies Principal Component Analysis (PCA) to evaluate and understand academic performance among final-year Civil Engineering students at Mbeya University of Science and ...

sispca is a Python package designed to learn linear representations capturing variations associated with factors of interest in high-dimensional data. It extends the Principal Component Analysis (PCA) ...