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• Identify and choose appropriate test design techniques for different applications.
• Develop a sound understanding of advanced testing techniques and their practical application.
• Identify the roles and responsibilities of a Test Analyst in the software testing process.
• Plan, design, and implement functional and non-functional tests.
• Execute test cases and report on the outcomes.
• Produce good quality test design specifications using the most effective test design techniques.
• Understand the Test Analyst’s Tasks in the Test Process.
• Understand the Test Analyst’s Tasks in Risk-Based Testing.
• Learn about Test Techniques.
• Understand Testing Software Quality Characteristics.
• Understand Test Tools and Automation.

• Analyze and articulate the purpose and benefits of testing. Analyze and interpret the basic ideas behind software testing.
• Recognize the suitable test approach and activities based on the testing context.
• Evaluate and enhance the quality of documentation.
• Understand the key issues in testing software applications.
• Demonstrate the ability to design tests that effectively cover requirements and business events.
• Demonstrate advanced proficiency in testing expertise.
• Gain expertise in software testing and quality by studying under an industry-recognized expert.
• Understand the basic principles related to testing, the reasons why testing is required, and what the test objectives are.
• Understand the test process, the major test activities, and Testware.
• Understand the essential skills for testing.

At the end of this training, the participants will be able to:

• Use data science principles to address business issues.
• Apply the extract, transform, and load (ETL) process to prepare datasets.
• Use multiple techniques to analyze data and extract valuable insights.
• Design a machine learning approach to address business issues.
• Train, tune, and evaluate classification models, regression and forecasting models, and clustering models.
• Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.

By the end of the course, participants should be able to:

• Assess cybersecurity risks to the organization. 
• Analyze the threat landscape. 
• Analyze various reconnaissance threats to computing and network environments. 
• Analyze various attacks on computing and network environments. 
• Analyze various post-attack techniques. 
• Assess the organization's security posture through auditing, vulnerability management, and penetration testing. 
• Collect cybersecurity intelligence from various network-based and host-based sources. 
• Analyze log data to reveal evidence of threats and incidents. 
• Perform active asset and network analysis to detect incidents. 
• Respond to cybersecurity incidents using containment, mitigation, and recovery tactics. 
• Investigate cybersecurity incidents using forensic analysis techniques. 

At the end of this training, the participants will be able to:

• Explain the fundamentals of data science.
• Identify functions of data science for business.
• Implement business requirements for data science.

• Understand the foundational elements of an effective information security risk management program, including its processes and relevant regulatory and legal requirements.
• Define and categorize information systems to align with organizational risk management strategies.
• Identify, document, and tailor security and privacy controls, and develop a strategy for continuous monitoring and effectiveness evaluation.
• Implement and document the deployment of selected security and privacy controls within the information system.
• Prepare for, conduct, and report on the assessment or audit of security and privacy controls, and develop a comprehensive remediation plan.
• Compile necessary documentation for the authorization or approval process of information systems and assess the associated risks.
• Authorize or approve information systems based on documented risk assessments and management strategies.
• Analyze the impact of environmental changes on information systems and adjust monitoring strategies accordingly.

• Plan and install a home or small business network using wireless technology, then connect it to the Internet.
• Develop critical thinking and problem-solving skills using Cisco Packet Tracer.
• Practice verifying and troubleshooting network and Internet connectivity.
• Recognize and mitigate security threats to a home network.

• Understand security controls for networks, servers and applications.
• Learn security principals, the know-how-to develop compliant policies and procedures for data confidentiality and availability.
• Develop critical thinking and problem-solving skills using real equipment and Cisco Packet Tracer.

By the end of the training, participants should be able to:

• Describe the various systems that support a typical data center.
• Explain how server virtualization consolidates idle resources, reduces cost and provide better services to the business.
• Explain how the Software Defined Networking (SDN) framework plays the key role in data center virtualization.
• Use Python to read data from sensors and store data in a SQL data base.
• Use Python Data Analysis library to clean, manipulate, integrate data sets.
• Use Python Visualization Libraries to visualize real-time data end explore acquired data sets.
• Explain the fundamental principles of a modern scalable Big Data platforms like Hadoop.
• Use storytelling to present the insights gained from extracted data.

• Assimilate and master the open-source concepts and comprehend the way Linux is used.
• Understand the basics of Linux commands line.
• Learn how to use the virtual machine for experiments.
• Prepare for LPI Linux Essentials Professional Development Certificate (PDC).