DELL EMC

Service Management Overview; Service Management Planning; Service Management Considerations; Service Management Front Office; Service Management Back Office; Measuring Success and Planning for the Future. Discuss service management business drivers and benefits Discuss ITaaS transformation program planning and outcomes Define service management terminology and lifecycle Apply a practical approach to the phases of the service lifecycle from a front and back office perspective List measures of success and plan for the future

The Enterprise Storage Networking Design (ESND) course educates participants about considerations and practices to design storage networking solution for classic and virtualized data center environments. The course also focuses on design practices for storage, compute, and replication solutions. It takes an ‘open’ approach focusing on technologies, concepts and practices rather than specific products. To provide context, the course utilizes best-of-breed EMC product examples and case studies. Upon completing this course, participants will have the knowledge to design more robust and efficient storage networking infrastructure for their organization, customers, and partners.

The Enterprise Backup Recovery Design (EBRD) course educates participants on considerations and practices to design backup recovery solution for classic and virtualized data center environments. The course also focuses on design considerations for data deduplication, Cloud backup, data archiving, and replication solutions. It takes an ‘open’ approach focusing on technologies, concepts and practices rather than specific products. To provide context, the course utilizes best-of-breed EMC product examples and case studies. Upon completing this course, participants will have the knowledge to design more robust and efficient backup recovery infrastructure for their organization, customers, and partners.

This course builds on skills developed in the Data Science and Big Data Analytics course. The main focus areas cover Hadoop (including Pig, Hive, and HBase), Natural Language Processing, Social Network Analysis, Simulation, Random Forests, Multinomial Logistic Regression, and Data Visualization. Taking an “Open” or technology-neutral approach, this course utilizes several open-source tools to address big data challenges. 

This course provides practical foundation level training that enables immediate and effective participation in Big Data and other analytics projects. It includes an introduction to Big Data and the data analytics lifecycle to address business challenges that leverage Big Data. The course provides grounding in basic and advanced analytic methods and an introduction to Big Data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to understand how these methods and tools may be applied to real world business challenges by a practicing data scientist.