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  • Computer technology is the most significant new technology since the beginning of the Industrial Revolution. It is an awesome technology, but also creates powerful problems. In this course, we describe some of the remarkable benefits of computer and communication technologies, some of the problems associated with them, and some of the means for reducing the problems and coping with their effects. This course is suitable for those who are interested in computers and desire to learn the topics. The goals of this course are: (1) to learn the implications and responsibilities of computer usage, the Internet, privacy issues, security issues, and intellectual properties; (2) to help you to understand that technology has both benefits and risks, and for you to form opinions of what those may be; (3) to challenge your mind and get you to think critically when analyzing technology; (4) to be familiar with computer, Microsoft Office, email, web, and online teaching platform.

  • After completing this course you will be able to plan an e-learning course together with exercises and elements of online teaching strategy, using a variety of tools and teaching methods selected specifically to meet your goals.

  • Failure to understand and control software requirements is the number 1 cause of project failure. This course is developed for those who are involved with defining or understanding the requirements for any system that contains software. The primary audiences are business analysts, architects, designers, developers, testers, and other technical team members. It is also for project managers in charge of software development, and stakeholders who participate in defining a product that meets their business, functional, and quality needs. This course prepares students in pursuing computer science degree. You will learn (1) how to establish the requirements of a software system, specify/develop (based on IEEE standard) and manage those requirements; (2) learn some other basic software engineering standards (process, products, resource and technique); (3) In particular, learn the basics of CMMI, a means for assessing and improving the software development process.

  • This course is divided into three modules

    Module 1: Fundamental Cloud Security

    This foundational course provides a well-rounded, end-to-end presentation of essential techniques, patterns and industry technologies for establishing cloud-based security controls and security architectures. The cloud security fundamentals covered in Module 2 are continued by introducing threat categorizations and new cloud security mechanisms. The course then delves into a series of cloud security patterns that explore a variety of topics, including cloud network security, identity and access management, and trust assurance. The following primary topics are covered:

    • Cloud Security Basics and Common Cloud Security Mechanisms
    • Cloud Security Threats and Threat Categorization Methodology
    • Identification and Treatment of Common Threats
    • Cloud Network Security Patterns and Supporting Mechanisms
    • Securing Network Connections and Cloud Authentication Gateways
    • Collaborative Monitoring and Logging, Independent Cloud Auditing
    • Cloud Identity and Access Management Patterns and Supporting Mechanisms
    • Federating and Enabling Secure Interoperability among Cloud Consumers
    • Trust Assurance Patterns and Supporting Mechanisms
    • Trust Attestation and Establishing Trustworthiness

    Module 2: Advanced Cloud Security

    This advanced course covers cloud security mechanisms and architectural design patterns that address data and access control security for virtual machines, as well as trust boundaries, geotagging and BIOS security. The course also explains common methods used by attackers to breach organizational resources and provides a methodology for countering such attacks. The course concludes by demonstrating the relationship between threats, attacks, and risks via threat modeling. The following primary topics are covered:

    • Cloud Service Security Patterns and Supporting Mechanisms
    • Virtual Machine Platform Protection Patterns
    • Considerations for Setting Up Secure Ephemeral Perimeters
    • Trusted Cloud Resource Pools and Cloud Resource Access Control
    • Permanent Data Access Loss Protection and Cloud Data Breach Protection
    • Isolated Trust Boundaries
    • The Attack Life cycle and the Security Life cycle
    • Proactive Mitigation vs. Incidence Response
    • Threats, Vulnerabilities, Impacts from Exploitation
    • Threat Modeling, Threats and Mitigation
    Module 3: Cloud Security Lab

    As a continuation of Modules 1 and 2, this lab-style course provides a series of hands-on exercises that enable participants to apply their knowledge. Participants will apply different combinations of cloud security patterns and mechanisms in order to complete a series of exercises pertaining to solving cloud security, risk, compliance and asset protection-related scenarios and problems.

  • Module 1: Fundamental Cloud Architecture

    This course provides a highly technical drill-down into the inner workings and mechanics of foundational cloud computing platforms. Private and public cloud environments are dissected into concrete, componentized building blocks (referred to as "patterns") that individually represent platform feature-sets, functions and/or artifacts, and are collectively applied to establish distinct technology architecture layers.

    Building upon these foundations, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) environments are further explored as compound patterns, comprised of unique and shared building blocks.

    The course is structured as a guided tour through these architectural layers, describing primary components, highlighting shared components and explaining how building blocks can be assembled and implemented via cloud computing mechanisms and practices. The following primary topics are covered:

    • Technology Architectural Layers of Cloud Environments
    • Public and Private Cloud Technology Architecture
    • laaS, PaaS and SaaS Technology Architecture
    • Cloud Computing Mechanisms as part of Platform and Solution Technology Architectures
    • Bare-Metal and Elastic Disk Provisioning
    • Multipath Resource Access, Broad Access and Intelligent Automation Engines
    • Usage and Pay-as-You-Go Monitoring
    • Platform Provisioning and Rapid Provisioning
    • Resource Management and Realtime Resource Availability
    • Shared Resources, Resource Pools and Resource Reservation
    • Self-Service and Usage and Administration Portals
    • Workload Distribution and Service State Management

    Module 2: Advanced Cloud Architecture

    This course builds upon CCP Module 4 to provide a deep dive into elastic, resilient and multitenant technology architectures, as well as specialized solution architectures, such as cloud bursting and cloud balancing.

    Through the study of architectural mechanisms, industry technologies and design patterns, both core and extended components are described that combine to realize elasticity, resiliency and multitenancy as primary characteristics of cloud platforms. By leveraging these native and enhanced scalability and failover-related feature-sets, specialized solution architectures are described to enable bursting between clouds and on-premise and cloud environments, as well as the balancing of runtime loads across clouds for performance and failover purposes.

    As with Module 4, the course organizes content so that architectural layers are explored sequentially and, where appropriate, in relation to each other. Newly introduced primary components are described and shared components across architectural layers are highlighted. The following primary topics are covered:

    • Elastic Environment
    • Resilient Environment
    • Multitenant Environment
    • Direct I/O Access and Direct LUN Access
    • Dynamic Data Normalization
    • Zero Downtime and Storage Maintenance Window
    • Load Balanced Virtual Servers
    • Burst In, Burst Out and Cloud Bursting
    • Cloud Balancing
    • Redundant Storage and Storage Workload Management
    • Elastic Disk Provisioning, Elastic Resource Capacity and Elastic Network Capacity
    • Intra-Storage and Cross-Storage Device Vertical Tiering
    • Redundant Physical Connections for Virtual Servers and Persistent Virtual Network Configurations
    • Load Balanced Virtual Switches and Service Load Balancing
    • Hypervisor Cluster
    • Dynamic Failure and Recovery
    • Synchronized Operating State
    • Resource Reservation
    • Module 3: Cloud Architecture Lab

    This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered previously in course modules 4 and 5. Completing this lab will help highlight areas that require further attention and will further prove hands-on proficiency in cloud computing design patterns, technology architecture layers, mechanisms, industry technologies and practices as they are applied and combined to solve real-world problems involving IaaS, PaaS and SaaS environments.

    As a hands-on lab, this course provides a set of detailed exercises, that require participants to solve a number of inter-related problems, with the ultimate goal of evaluating, designing and correcting technology architectures to fulfill specific sets of solution and business automation requirements.

    For instructor-led delivery of this lab course, the Certified Cloud Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion.

  • This course is divided into three parts.

    Module 1: Fundamental Big Data Engineering

    This part explores introductory topics pertaining to the field of developing data processing solutions-data engineering-in the context of Big Data environments. Specifically it covers concepts, techniques and technologies related to the processing and storage of Big Data datasets including MapReduce and NoSQL. It highlights the unique challenges faced when processing and storing Big Data datasets and further introduces the main components of Hadoop-the de-facto platform for data processing and data storage within Big Data environments. The following primary topics are covered:

    • Big Data Engineering - Big Data Engineering Challenges

    • Big Data Storage Terminologies (including sharding, replication, CAP theorem, ACID, BASE)

    • Big Data Storage Requirements

    • On-Disk Storage (including distributed file system - databases)

    • Introduction to NoSQL - NewSQL

    • NoSQL Rationale - Characteristics

    • NoSQL Database Types (including key-value, document, column-family and graph databases)

    • Big Data Processing Requirements

    • Big Data Processing (including batch mode and realtime mode)

    • Introduction to MapReduce for Big Data Processing (batch mode)

    • MapReduce Explained (including map, combine, partition, shuffle and sort, and reduce)

    Module 2: Advanced Big Data Engineering

    The second module builds upon the first part by exploring advanced topics pertaining to the storage and processing of Big Data datasets. Specifically it covers advanced Big Data engineering mechanisms, in-memory data storage and realtime data processing. It presents further considerations for developing MapReduce algorithms and also introduces the Bulk Synchronous Parallel (BSP) processing engine, along with a discussion of graph data processing. The Big Data mechanisms required for developing Big Data pipelines, its stages and the design process involved in developing Big Data processing solutions are also explored. The following primary topics are covered:

    • Advanced Big Data Engineering Mechanisms (including serialization & compression engines)

    • In-Memory Storage Devices, In-Memory Data Grids & In-Memory Databases

    • Read-Through, Read-Ahead, Write-Through & Write-Behind Integration Approaches

    • Polyglot Persistence (including Explanation, Issues & Recommendations)

    • Realtime Big Data Processing Concepts (including Speed Consistency Volume (SCV), Event Stream Processing (ESP) & Complex Event Processing (CEP))

    • General Realtime Big Data Processing & Realtime Big Data Processing & MapReduce

    • Advanced MapReduce Algorithm Design

    • Bulk Synchronous Parallel (BSP) Processing Engine & BSP vs. MapReduce

    • Graph Data & Graph Data Processing using BSP

    • Big Data Pipelines (including Definition and Stages)

    • Big Data with Extract-Load-Transform (ELT)

    • Big Data Solutions (including Characteristics, Design Considerations & Design Process)

    Module 3: Big Data Engineering Lab

    This course module covers a series of exercises and problems designed to test the participant's ability to apply knowledge of topics covered previously in course modules 7 and 8. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data engineering practices as they are applied and combined to solve real-world problems.

    As a hands-on lab, this course incorporates a set of detailed exercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data engineering technologies, mechanisms and techniques can be applied to solve problems in Big Data environments.

    For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion.

  • This course is divided into three modules:

    Module 1: Fundamental Big Data Analysis & Science

    This module provides an in-depth overview of essential topic areas pertaining to data science and analysis techniques relevant and unique to Big Data with an emphasis on how analysis and analytics need to be carried out individually and collectively in support of the distinct characteristics, requirements and challenges associated with Big Data datasets. The following primary topics are covered:

    • Data Science, Data Mining & Data Modeling
    • Big Data Dataset Categories
    • Exploratory Data Analysis (EDA) (including numerical summaries, rules & data reduction)
    • EDA analysis types (including univariate, bivariate & multivariate)
    • Essential Statistics (including variable categories & relevant mathematics)
    • Statistics Analysis (including descriptive, inferential, correlation, covariance & hypothesis testing)
    • Data Munging & Machine Learning
    • Variables & Basic Mathematical Notations
    • Statistical Measures & Statistical Inference
    • Distributions & Data Processing Techniques
    • Data Discretization, Binning, Clustering
    • Visualization Techniques & Numerical Summaries
    • Correlation for Big Data
    • Time Series Analysis for Big Data

    Module 2: Advanced Big Data Analysis & Science

    This module delves into a range of advanced data analysis practices and analysis techniques that are explored within the context of Big Data. The course content focuses on topics that enable participants to develop a thorough understanding of statistical, modeling, and analysis techniques for data patterns, clusters, and text analytics, as well as the identification of outliers and errors that affect the significance and accuracy of predictions made on Big Data datasets. The following primary topics are covered:

    • Statistical Models, Model Evaluation Measures (including cross-validation, bias-variance, confusion matrix & f-score)
    • Machine Learning Algorithms, Pattern Identification (including association rules & apriori algorithm)
    • Advanced Statistical Techniques (including parametric vs. non-parametric, clustering vs. non-clustering distance-based, supervised vs. semi-supervised)
    • Linear Regression & Logistic Regression for Big Data
    • Decision Trees for Big Data
    • Classification Rules for Big Data
    • K Nearest Neighbor (kNN) for Big Data
    • Naïve Bayes for Big Data
    • Association Rules for Big Data
    • K-means for Big Data
    • Text Analytics for Big Data
    • Outlier Detection for Big Data

    Module 3: Big Data Analysis & Science Lab

    This course module covers a series of exercises and problems designed to test the participant's ability to apply knowledge of topics covered previously in course modules 4 and 5. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and science practices as they are applied and combined to solve real-world problems.

    As a hands-on lab, this course incorporates a set of detailed exercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data analysis techniques can be applied to solve problems in Big Data environments and used to make significant, relevant predictions that offer increased business value.

    For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion.

  • This course is divided into three modules.

    Module 1: Fundamental Big Data

    This module provides a high-level overview of essential Big Data topic areas. A basic understanding of Big Data from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues. The following primary topics are covered:

    • Fundamental Terminology and Concepts
    • A Brief History of Big Data
    • Business Drivers leading to Big Data Innovations
    • Characteristics of Big Data
    • Benefits of Adopting Big Data
    • Challenges and Limitations of Big Data
    • Basic Big Data Analytics
    • Big Data and Traditional Business Intelligence and
    • Data Warehouses
    • Big Data Visualization
    • Common Adoption Issues
    • Planning for Big Data Initiatives
    • New Roles Introduced by Big Data Projects
    • Emerging Trends

    Module 2: Big Data Analysis & Technology Concepts

    This module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The course content does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions. The following primary topics are covered:

    • Big Data Analysis Lifecycle (from business case evaluation to data analysis and visualization)
    • A/B Testing, Correlation
    • Regression, Heat Maps
    • Time Series Analysis
    • Network Analysis
    • Spatial Data Analysis
    • Classification, Clustering
    • Outlier Detection
    • Filtering (including collaborative filtering & content-based filtering)
    • Natural Language Processing
    • Sentiment Analysis, Text Analytics
    • File Systems & Distributed File Systems, NoSQL
    • Distributed & Parallel Data Processing,
    • Processing Workloads, Clusters
    • Cloud Computing & Big Data
    • Foundational Big Data Technology Mechanisms

    Module 3: Big Data Lab

    This course module presents participants with a series of exercises and problems designed to test their ability to apply knowledge of topics covered previously in course modules 1 and 2. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and technology and practices as they are applied and combined to solve real-world problems.

    As a hands-on lab, this course provides a set of detailed exercises that require participants to solve a number of inter-related problems, with the goal of fostering a comprehensive understanding of how Big Data environments work from both front and back-ends, and how they are used to solve real-world analysis and analytic problems.

    For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion.

  • Cloud Computing is considered the fourth IT revolution after mainframe computer, personal computer, and the Internet. It has a huge impact on people's work, study and lives. This course is designed for students majored in IT or computer science, for CIO or CTO, IT professionals, and for those who have a basic background in IT industry and a desire to learn. We will cover Cloud Computing basics, Cloud's business impact and economics, virtualization, Cloud services (SaaS, PaaS, and IaaS), data storage in the Cloud, Cloud security, disaster recovery and business continuity, Cloud migration and management, mobile Cloud Computing, and Microsoft Azure and Amazon AWS Clouds. In summary, this course will teach you the fundamentals of Cloud Computing, from theory to practice.