Data Analytics

Prescriptive analytics turns data into action. This article explains how this advanced data analytics method uses data to tell you what to do, to change decision making, business processes and customer experiences. Read about its methods, benefits and real world examples.

Statistical modeling uses mathematical techniques to analyze data and predict outcomes. It is widely applied in various fields, including business, healthcare, and engineering. This article explores different types of statistical models, such as regression, clustering, and decision trees, and highlights their applications and challenges.

Data ingestion is the first step in the analytics pipeline. This article explains what data ingestion is and examines its types—batch, real-time, and hybrid—while discussing challenges and key tools for collecting, transforming, and storing raw data from sources like databases, APIs, and IoT devices.

This article explains statistical analysis—the process of organizing and studying data to spot patterns, answer questions, and predict outcomes. Learn about types like descriptive, inferential, and predictive, and why they are key to smarter decisions across fields.

Learn how real-time analytics processes data instantly for immediate insights, enhancing decision-making in areas like fraud detection, customer experience, and operational efficiency. Explore tools like Apache Kafka, Flink, and Spark for stream processing, and understand the real-world applications transforming industries.

The characteristic equation is central to data science, helping solve for eigenvalues and eigenvectors. This article explores its role in dimensionality reduction (PCA), clustering, and machine learning. Learn the computational methods, Python libraries, and real-world applications of this crucial concept.