SIP/EIS Programs

GENERATE INCOME VIA SEO TRAINING

COURSE OUTLINE

CAREER PROSPECT

AI INDUSTRIALISM IN LOGISTICS AND HANDLING OF SUPPLY CHAIN

COURSE OUTLINE

CAREER PROSPECT

CERTIFICATE PROGRAM IN DATA SCIENCE

PROGRAM OBJECTIVES

Course Content

Day 1: Introduction to Data Science (8 Hours)
1. Overview of Data Science

• Definition and importance
• The data science lifecycle
• Key roles in data science

2. Tools of the Trade

• Introduction to Python and Jupyter Notebooks
• Overview of R and SQL

3. Data Collection and Preparation

• Types of data (structured, unstructured)
• Data cleaning and preprocessing
• Introduction to data wrangling

4. Hands-on Lab: Setting up a data science environment and basic data manipulation

• Descriptive and inferential statistics
• Measures of central tendency and dispersion

2. Data Visualization

• Best practices in data visualization
• Tools: Matplotlib, Seaborn, Tableau (overview)

3. Hands-on Lab: Creating visualizations with Python (Matplotlib and Seaborn)

• Types of machine learning: supervised, unsupervised, reinforcement learning
• Key concepts: training, testing, overfitting, underfitting

2. Supervised Learning

• Regression and classification
• Algorithms: Linear Regression, Logistic Regression

3. Hands-on Lab: Building a linear regression model

1. Unsupervised Learning

• Clustering (K-Means, Hierarchical)
• Key concepts: training, testing, overfitting, underfitting

2. Model Evaluation and Optimization

• Accuracy, precision, recall, F1 score
• Cross-validation and hyperparameter tuning

3. Introduction to Deep Learning

• Basics of neural networks
• Overview of TensorFlow and Keras

4. Hands-on Lab: Clustering and evaluation metrics

• Data science in finance, healthcare, e-commerce, etc.
• Ethical considerations in data science

2. Capstone Project

• Designing and implementing a data science solution
• End-to-end data science workflow

3. Certification Preparation

• Overview of data science certifications (e.g., IBM Data Science Professional Certificate, Google Data Analytics Certificate)
• Practice tests and exam tips

4. Hands-on Lab: Presenting the capstone project

CERTIFICATE PROGRAM IN VIRTUAL REALITY (VR)

Course Content

• Definition and history of VR
• Key concepts and components of VR

2. Applications of VR

• Gaming, education, healthcare, real estate, and more

3. Overview of VR Hardware and Software

• VR headsets (Oculus, HTC Vive, etc.)
• VR development platforms (Unity, Unreal Engine)

4. Hands-on Session: Exploring VR environments using VR headsets

1. VR Hardware Components

• Head-mounted displays (HMDs)
• Motion controllers and sensors

2. Principles of Interaction Design in VR

• Immersion, presence, and usability
• Designing intuitive user experiences

3. Hands-on Lab: Configuring VR hardware and exploring basic interactions

• Basics of Unity interface and tools
• Setting up a VR project

2. 3D Modelling and Asset Creation

• Overview of 3D modelling tools (Blender, Maya)
• Importing and optimizing assets for VR

3. Hands-on Lab: Creating a simple VR environment in Unity

• Adding movement, interactivity, and physics
• Implementing VR input systems

2. Performance Optimization

• Managing frame rates and latency
• Testing and debugging VR applications

3. Hands-on Lab: Developing a VR application with interactive elements

• Mixed Reality (MR) and Augmented Reality (AR)
• Artificial Intelligence and VR integration

2. Industry Case Studies

• Successful VR projects and their impact

3. Final Project: Creating a VR Experience

• Participants design and present their VR projects

4. Certification Preparation and Wrap-Up

CERTIFICATE PROGRAM IN CLOUD COMPUTING

PROGRAM OBJECTIVES

• To understand the fundamentals of cloud computing and its service models.
• To gain hands-on experience with leading cloud platforms.
• To explore cloud architecture, security, and deployment strategies.
• To enhance career opportunities by acquiring in-demand cloud computing skills.

Course Content

• Definition and key concepts
• Evolution of cloud computing
• Benefits and challenges

2. Cloud Service Models

• Infrastructure as a Service (IaaS)
• Platform as a Service (PaaS)
• Software as a Service (SaaS)

3. Cloud Deployment Models

• Public, private, hybrid, and multi-cloud

4. Introduction to Major Cloud Providers

• AWS, Microsoft Azure, Google Cloud Platform

5. Hands-on Lab: Setting up a basic cloud environment

1. Fundamentals of Virtualization

• Virtual machines vs. containers
• Hypervisors and containerization tools

2. Cloud Networking

• Virtual networks and subnets
• Load balancing and auto-scaling

3. Storage Solutions in the Cloud

• Object storage, block storage, and file storage

4. Hands-on Lab: Configuring virtual machines and cloud storage

1. Cloud Security Fundamentals

• Identity and access management (IAM)
• Data encryption and compliance

2. Disaster Recovery and Business Continuity

• Backup strategies and recovery plans

3. Governance in the Cloud

• Monitoring and cost management

4. Hands-on Lab: Setting up IAM roles and policies

1. Developing Cloud-Native Applications

• Principles of microservices architecture
• Serverless computing and Functions-as-a-Service (FaaS)

2. Deployment Strategies

• CI/CD pipelines in the cloud
• Monitoring and logging

3. Hands-on Lab: Deploying a sample application on a cloud platform

1. Emerging Trends in Cloud Computing

• Artificial Intelligence and Machine Learning in the cloud
• Edge computing and IoT integration

2. Industry Case Studies

• Real-world applications of cloud computing

3. Certification Preparation

• Overview of popular certifications (AWS Certified Cloud Practitioner, Microsoft Azure Fundamentals, etc.)
• Practice tests and exam tips

4. Hands-on Lab: Capstone project – Designing and deploying a comprehensive cloud solution

CERTIFICATE PROGRAM IN CYBERSECURITY

PROGRAM OBJECTIVES

• To understand cybersecurity fundamentals and threat landscapes.
• To learn and apply critical security measures to protect systems and data.
• To explore the latest trends and technologies in cybersecurity.
• To enhance professional capabilities and prepare for cybersecurity certifications.

Course Content

• Definition and importance of cybersecurity
• Overview of the cybersecurity landscape
• Key cybersecurity terminologies

2. Understanding Cyber Threats

• Types of threats: malware, phishing, ransomware, etc.
• Attack vectors and threat actors

3. Cybersecurity Frameworks and Standards

• NIST Cybersecurity Framework
• ISO/IEC 27001

4. Hands-on Lab: Identifying and analysing common cyber threats

1. Network Security Basics

• Firewalls, intrusion detection/prevention systems (IDS/IPS)
• Virtual private networks (VPNs)

2. Vulnerability Management

• Identifying, analysing, and mitigating vulnerabilities
• Tools for vulnerability assessment (e.g., Nessus, Qualys)

3. Secure Network Architecture

• Designing secure networks
• Best practices for segmentation and isolation

4. Hands-on Lab: Conducting a vulnerability scan

1. Application Security Basics

• Secure software development lifecycle (SDLC)
• Common vulnerabilities (OWASP Top 10)

2. Endpoint Security

• Antivirus, endpoint detection, and response (EDR)
• Best practices for securing devices

3. Hands-on Lab: Simulating an application vulnerability test

• Developing and implementing security policies
• Security awareness and training

2. Compliance and Legal Requirements

• GDPR, HIPAA, PCI DSS
• Understanding cybersecurity laws

3. Incident Response and Recovery

• Phases of incident response
• Creating an incident response plan

4. Hands-on Lab: Designing an incident response plan

1. Emerging Trends in Cybersecurity

• Artificial intelligence in cybersecurity
• Cloud security and Zero Trust architecture

2. Case Studies

• Real-world cybersecurity breaches and lessons learned

3. Certification Preparation

• Overview of popular certifications (AWS Certified Cloud Practitioner, Microsoft Azure Fundamentals, etc.)
• Practice tests and exam tips

4. Hands-on Lab: Capstone project – Building a comprehensive security strategy

CERTIFICATE PROGRAM IN ARTIFICIAL INTELLIGENCE

PROGRAM OBJECTIVES

• To understand the fundamentals of Artificial Intelligence and machine learning.
• To gain hands-on experience with AI frameworks and tools.
• To explore AI applications in real-world scenarios.
• To enhance career opportunities by acquiring sought-after AI skills.

Course Content

1. Overview of Artificial Intelligence

• Definition and key concepts
• History and evolution of AI
• Types of AI (Narrow, General, Super AI)

2. Machine Learning Basics

• Supervised, unsupervised, and reinforcement learning
• Common algorithms and use cases

3. AI Tools and Platforms

• Overview of TensorFlow, PyTorch, and Scikit-learn

4. Ethical Considerations in AI

• Bias, fairness, and accountability

5. Hands-on Lab: Setting up a basic AI environment

1. Introduction to Data for AI

• Importance of data in AI development
• Data collection and sources

2. Data Cleaning and Preprocessing

• Handling missing data, normalization, and encoding

3. Feature Engineering

• Feature selection and dimensionality reduction

4. Hands-on Lab: Preparing a dataset for machine learning

1. Building Machine Learning Models

• Regression, classification, and clustering techniques

2. Neural Networks Basics

• Introduction to artificial neural networks
• Activation functions and backpropagation

3. Model Evaluation and Optimization

• Metrics, cross-validation, and hyperparameter tuning

4. Hands-on Lab: Training and evaluating an ML model

1. Deep Learning Fundamentals

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

2. Natural Language Processing (NLP)

• Text preprocessing, sentiment analysis, and chatbots

3. Computer Vision

• Image classification and object detection

4. AI in Industry

• Case studies in healthcare, finance, and automation

5. Hands-on Lab: Implementing a deep learning application

1. Emerging Trends in AI

• Explainable AI (XAI), Generative AI, and AI ethics

2. Industry Applications and Case Studies

• Real-world AI projects and their impact

3. Certification Preparation

• Overview of popular certifications (e.g., AI-900, TensorFlow Developer)
• Practice tests and exam tips

4. Hands-on Lab: Capstone project – Designing and deploying an AI solution