Exclusive articles:
What is a Database? Introduction for Data Scientists
Learn what a database is and why data scientists need to understand them. Explore RDBMS, NoSQL, data warehouses, keys, indexes, and how databases fit into data science workflows.
Understanding Structured vs Unstructured Data
Learn the difference between structured and unstructured data. Explore semi-structured data, real-world examples, storage systems, and how data scientists work with each type.
Anomaly Detection: Finding Outliers in Your Data
Master anomaly detection from first principles. Learn Isolation Forest, Local Outlier Factor, One-Class SVM, statistical methods, and PCA reconstruction error — with complete Python implementations.
Implementing PCA for Data Visualization
Learn to implement PCA for visualization in Python. Compare PCA with t-SNE and UMAP, create biplots, visualize explained variance, plot reconstruction, and apply to images and text data.
Understanding Eigenvalues and Eigenvectors in PCA
Understand eigenvalues and eigenvectors from scratch with geometric intuition and Python code. Learn how they power PCA, what they mean, and how to compute them — no linear algebra background required.
