Nominal data is a type of categorical data that represents labels or names without any inherent order. It is used for classification rather than numerical analysis, meaning the values cannot be ranked or measured. In data analysis, nominal data is essential for organizing and labeling information, making it useful for fields like market research, healthcare, ...
Data ingestion is the process of importing, transferring, loading, and processing data from multiple sources into a storage system for further analysis. It serves as the first step in the data pipeline, ensuring that data from various structured and unstructured sources is collected and made available for analytics, reporting, and decision-making. Unlike ETL (Extract, Transform, ...
Data integration in data mining is the process of combining data from multiple sources into a unified view for efficient analysis. It plays a crucial role in merging structured and unstructured data, allowing organizations to derive meaningful insights. Ensuring data consistency, accuracy, and accessibility is essential for maintaining high-quality datasets. Without integration, businesses face challenges ...
The rapid growth of big data has made traditional data processing methods inefficient, leading to the need for scalable solutions. Hadoop is an open-source framework that enables the distributed storage and parallel processing of massive datasets across multiple machines. Hadoop’s ability to handle large-scale data efficiently makes it essential for big data analytics, cloud computing, ...
A Data Scientist is one of the most in-demand professionals in today’s tech-driven world. Known as the “data detectives,” they analyze large volumes of structured and unstructured data to uncover trends, patterns, and actionable insights. These insights are then used to guide business strategies, improve operations, and drive innovation across industries. The role of a ...
Association Rule Mining is a powerful technique used to uncover meaningful relationships between variables within large datasets. They are designed to discover “if-then” patterns, providing insights into how data items are related and frequently occur together. These rules are particularly useful in identifying correlations and dependencies, enabling data-driven decision-making. For instance, in a retail dataset, ...
Big Data refers to the massive volumes of structured and unstructured data generated daily, which traditional systems cannot efficiently process. Its growing importance lies in its ability to transform industries by uncovering actionable insights, enabling businesses to make informed decisions, optimize operations, and drive innovation in a data-driven world. What is Big Data? Big Data ...
Data science has emerged as one of the most in-demand career paths in today’s technology-driven world. With the exponential growth of data across industries, organizations rely on data scientists to analyze and derive actionable insights that drive critical business decisions. This rising demand for skilled professionals has positioned data science as a lucrative and rewarding ...
Big Data refers to exceptionally large and complex datasets that traditional data processing tools cannot efficiently handle. These datasets are characterized by their vast volume, high velocity, and diverse variety, often referred to as the 3Vs of Big Data. Simply put, Big Data encompasses information so extensive and intricate that specialized technologies and approaches are ...
In today’s tech-driven world, Cyber Security and Data Science stand out as two of the most sought-after fields, driving innovation and securing systems in the digital age. While Cyber Security focuses on protecting networks and data from breaches, Data Science unlocks insights from vast datasets to fuel decision-making. Both fields offer immense potential for growth ...
Team Applied AI
Abhimanyu Saxena
