Big Data eLearning Courses
There is so much more than just Big Data. These courses cover analytics, data management, machine learning, cybersecurity and other related topics. (Even if we don't have a machine learning course now, we will in the future)
Short, Cost-Effective Training on Hot Topics Based on the Latest Research and Trends--Constantly Updated! Quartos are peer reviewed, online learning modules that quickly bring you up-to-date on the latest developments in a specific technology.
Big Data Quartos Courses
The Internet of Things (IoT) enables data collection on a large scale, but the extraction of knowledge from this data can lead to user privacy issues. This article discusses privacy challenges in the IoT.
Specifically, the author examines the consequences of unevenness in big data, digital data going from local controlled settings to uncontrolled global settings, privacy effects of reputation monitoring systems, and inferring knowledge from social media.
Cloud computing has transformed people's perception of how Internet-based applications can be deployed in datacenters and offered to users in a pay-as-you-go model. Despite the growing adoption of cloud datacenters, challenges related to big data application management still exist. One important research challenge is selecting configurations of resources as infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) layers such that big data application-specific service-level agreement goals (such as minimizing event-detection and decision-making delays, maximizing application and data availability, and maximizing the number of alerts sent per second) are constantly achieved for big data applications. This article discusses the issue of selecting resource configurations across multiple layers of a cloud computing stack by considering deployment of a real-time stock recommendation big data application over an Amazon Web Services public datacenter.
Achieving the ideal “360-degree view of the customer” is a challenge for organizations that separate Web and digital initiatives from legacy IT and marketing initiatives. The author discusses how organizations can meet this challenge by applying change management and governance principles to big data analysis.
Exponential data growth from the Internet, low-cost sensors, and high-fidelity instruments have fueled the development of advanced analytics operating on vast data repositories. These analytics bring business benefits ranging from Web content personalization to predictive maintenance of aircraft components. To construct the data repositories underpinning these systems, rapid innovation has occurred in distributed-data-management technologies, employing schemaless data models and relaxing consistency guarantees to satisfy scalability and availability requirements. These big data systems present many challenges to software architects. Distributed-software architecture quality attributes are tightly linked to both the data and deployment architectures. This causes a consolidation of concerns, and designs must be closely harmonized across these three architectures to satisfy quality requirements.
With the proliferation of social media, which is largely fostered by the boom of the Internet and mobile ecosystems, a huge amount of multimedia data has been generated, forming the multimedia big data.
The growing volumes of time-stamped data available from sensors, social media sources, Web logs, and medical histories present remarkable opportunities for researchers and policy analysts.