L2: Big Data and Data Mining

The process of examining data to draw conclusions and make informed decisions is a fundamental aspect of data science, involving statistical analysis and data-driven insights.

Vast amounts of structured, semi-structured, and unstructured data are characterized by its volume, velocity, variety, veracity and value, which, when analyzed, can provide competitive advantages and drive digital transformations.

A distributed computing environment comprising thousands or tens of thousands of interconnected computers that collectively store and process large datasets.

The ability to access cloud resources via standard mechanisms and platforms such as mobile devices, laptops, and workstations over networks.

An emerging role responsible for overseeing data-related initiatives, governance, and strategies, ensuring that data plays a central role in digital transformation efforts.

An executive is responsible for managing an organization’s information technology and computer systems, contributing to technology-related aspects of digital transformation.

The delivery of on-demand computing resources, including networks, servers, storage, applications, services, and data centers, over the Internet on a pay-for-use basis.

Categories that indicate where cloud infrastructure resides, who manages it, and how cloud resources and services are made available to users, including public, private, and hybrid models.

Models based on the layers of a computing stack, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), represent different cloud computing offerings.

Standard, off-the-shelf hardware components are used in a big data cluster, offering cost-effective solutions for storage and processing without relying on specialized hardware.

Computational procedures and mathematical models used to process and analyze data made accessible in the cloud for data scientists to deploy on large datasets efficiently.

A strategy in which data is duplicated across multiple nodes in a cluster to ensure data durability and availability, reducing the risk of data loss due to hardware failures.

An interdisciplinary field that involves extracting insights and knowledge from data using various techniques, including programming, statistics, and analytical tools.

A subset of machine learning that involves artificial neural networks inspired by the human brain, capable of learning and making complex decisions from data on their own.

The integration of digital technology into business processes and operations leads to improvements and innovations in how organizations operate and deliver value to customers.

A strategic and cultural organizational change driven by data science, especially Big Data, to integrate digital technology across all areas of the organization, resulting in fundamental operational and value delivery changes.

The practice of dividing data into smaller chunks and distributing them across multiple computers within a cluster enables parallel processing for data analysis.

A distributed storage and processing framework used for handling and analyzing large datasets, particularly well-suited for big data analytics and data science applications.

A storage system within the Hadoop framework that partitions and distributes files across multiple nodes, facilitating parallel data access and fault tolerance.

A cloud service model that provides access to computing infrastructure, including servers, storage, and networking, without the need for users to manage or operate them.

Hadoop is implemented in Java, an open-source, high-level programming language, providing the foundation for building distributed storage and processing solutions.

The initial step in Hadoop’s MapReduce programming model, where data is processed in parallel on individual cluster nodes, often used for data transformation tasks.

A characteristic where users are billed for cloud resources based on their actual usage, with resource utilization transparently monitored, measured, and reported.

The capability for users to access and provision cloud resources such as processing power, storage, and networking using simple interfaces without human interaction with service providers.