Course Curriculum
Data Science and Analytics Section
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Course Section:
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Introduction to Data Science and Analytics
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Course Details:
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Python Programming Fundamentals
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Data Manipulation and Analysis
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Data Visualization
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Machine Learning Basics
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Deep Learning Fundamentals
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Big Data Technologies​​
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Artificial Intelligence and Machine Section
​1.Course Section:
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Introduction to AI and ML
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Course Details:
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Python Programming for AI and ML
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Data Preprocessing and Feature Engineering
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Supervised and Unsupervised Learning
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Neural Networks and Deep Learning
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Reinforcement Learning
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Cybersecurity Course Section
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Course Section:
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Introduction to Cybersecurity
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Course Details:
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Cybersecurity Concepts
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Networking Fundamentals
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Ethical Hacking and Penetration Testing
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Cryptography and Encryption Techniques
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Security Assessment and Risk Management
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Incident Response and Disaster Recovery
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Cloud Computing Course Section
1.Course Section:
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Introduction to Cloud Computing
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Course Details:
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Cloud Service Models
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Cloud Deployment Models
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AWS or Azure Fundamentals
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Virtualization and Containerization Technologies
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Cloud Security Best Practices
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Customized Training Couse Section
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Needs Assessment:
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Identify knowledge gaps and learning objectives.
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Gather feedback from stakeholders and participants.
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Learning Objectives:
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Define clear and measurable goals.
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Ensure alignment with training objectives.
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Curriculum Design:
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Develop an outline with topics and activities.
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Customize content to meet participants' needs.
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Delivery Method:
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Choose suitable delivery methods (in-person, online).
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Consider blended learning approaches.
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Assessment and Evaluation:
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Design assessment tools for progress tracking.
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Evaluate effectiveness and relevance.
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Feedback and Revision:
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Gather feedback for improvement.
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Make necessary adjustments to the curriculum.
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Continuous Improvement:
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Monitor program effectiveness regularly.
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Update curriculum based on feedback and trends.
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