Here is the list of courses that will be provided in the same Canvas environment, which is also used by Cisco Networking Academy. BiASC will function as the regional academy in the Benelux and will support educational institutions that are interested in teaching one or more of the following courses.
- Course 1 - Information Storage and Management
- Course 2 - Cloud Infrastructure and Services
- Course 3 - Backup Recovery Systems and Architecture
- Course 4 - Data Science and Big Data Analytics
Below we provide a short description and the list of modules of each course.
Course 1 - Information Storage and Management
Information Storage and Management (ISM) is the only course of its kind to provide a comprehensive understanding the varied storage infrastructure components in classic and virtual environments.
Participants will learn the architectures, features, and benefits of intelligent storage systems; storage networking technologies such as FC SAN, IP SAN, NAS, and object-based and unified storage; business continuity solutions such as backup and replication; the increasingly critical area of information security and management, and the emerging field of Cloud computing.
- Module 1 – Introduction to Information Storage
- Module 2 – Data Center Environment
- Module 3 – Data Protection – RAID
- Module 4 - Intelligent Storage System
- Module 5 - Fibre Channel Storage Area Network (FC SAN)
- Module 6 - IP SAN and FCoE
- Module 7 - Network-Attached Storage (NAS)
- Module 8 - Object-based and Unified storage
- Module 9 – Introduction to Business Continuity
- Module 10 – Backup and Archive
- Module 11 – Local Replication
- Module 12 – Remote Replication
- Module 13 – Cloud Computing
- Module 14 – Securing the Storage Infrastructure
- Module 15 – Managing the Storage Infrastructure
Course 2 - Cloud Infrastructure and Services
The Cloud Infrastructure and Services (CIS) course educates participants about cloud deployment and service models, cloud infrastructure, and the key considerations in migrating to cloud computing.
For all definitions of cloud computing, the course has resorted to the U.S. National Institute of Standards and Technology as a guide. The course covers technologies required to build classic (traditional), virtualized, and cloud data center environments. These technologies include compute, storage, networking, desktop and application virtualization.
Additional areas of focus include backup/recovery, business continuity, security, and management. Students will learn about the key considerations and steps involved in transitioning from the current state of their data center to a cloud computing environment. Upon completing this course, participants will have the knowledge to make informed decisions about migrating to cloud infrastructure and choosing the best deployment model for their organization.
- Module 1 : Journey to the Cloud
- Module 2 : Classic Data Center (CDC)
- Module 3 : Virtualized Data Center (VDC) – Compute
- Module 4 : Virtualized Data Center – Storage
- Module 5 : Virtualized Data Center – Networking
- Module 6 : Virtualized Data Center – Desktop and Application
- Module 7 : Business Continuity in VDC
- Module 8 : Cloud Computing Primer
- Module 9 : Cloud Infrastructure and Management
- Module 10 : Cloud Security
- Module 11 : Cloud Migration Considerations
Course 3 - Backup Recovery Systems and Architecture
- Module 1 - Course Introduction
- Module 2: Information Storage Concepts
- Module 3: Backup Client
- Module 4: Backup Storage Node
- Module 5: Backup and Recovery Planning
Course 4 - Data Science and Big Data analytics
The goal of the Data Science And Big Data Analytics Course is for you to be able to immediately participate as a Data Science team member on big data and other analytics projects
To achieve it, the course content is focused on the point of view (p-o-v) of a Data Scientist, it teaches concepts and principles in an open, vendor-neutral manner so they can be applied in any technology environment, and it provides many hands-on labs for practical experience with coaching from the instructor(s).
- Module 1. Introduction to Big Data Analytics
- Module 2. Data Analytics Lifecycle + Lab
- Module 3. Review of Basic Data Analytics Methods Using R + Labs
- Module 4. Advanced Analytics - Theory & Methods + Labs
- Module 5. Advanced Analytics - Technology & Tools + Labs
- Module 6. The Endgame, or Putting it All Together + Final Lab