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Stellar Cybersecurity 2025

Dive into the core essentials with our premier 4-month Cohort  program designed to equip you with the practical skills you need, ranging from operating systems to web application security, ensuring you are prepared for the workforce.

This course covers Operating Systems and Security, Fundamentals of Cryptography, and Web Application Security. Upon completion, you will be equipped to pursue specialist or junior cybersecurity roles.

Cybersecurity is a rapidly expanding field, characterised by a strong demand for skilled professionals, attractive salary packages, and ample opportunities for career progression.

Introduction to Cybersecurity & Its Importance
Understanding Cyber Threats, Attack Vectors & Security Principles (CIA Triad)
Cybersecurity Frameworks & Compliance (GDPR, NIST, ISO 27001)
Fundamentals of Networking: IP Addressing, Subnetting & VLANs
Firewalls, VPNs, IDS/IPS & Wireless Security

MODULE 1: Cybersecurity & Networking Essentials

Understanding Security Risks & Risk Management Frameworks
Business Impact Analysis (BIA) & Security Controls
Introduction to Cryptography: Symmetric & Asymmetric Encryption
Public Key Infrastructure (PKI), SSL/TLS, and Digital Certificates
Cryptographic Hashing & Modern Encryption Techniques

MODULE 2: Security Risk Management & Cryptography

Identity & Access Management (IAM) Fundamentals (AAA, MFA, SSO, RBAC)
Role-Based Access Control (RBAC) & Privileged Access Management (PAM)
Vulnerability Scanning: Nessus, OpenVAS, Nikto & Nmap
Security Patch Management & System Hardening Techniques

MODULE 3: Identity Management & Vulnerability Assessment

Penetration Testing Methodologies & OWASP Top 10
Ethical Hacking & Web Application Security Basics
Incident Response Lifecycle & Malware Analysis
Digital Forensics & Post-Incident Reporting



MODULE 4: Ethical Hacking, Incident Response & Capstone Project

Cybersecurity Accelerator Cohort

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Stellar DevOps 2025

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This DevOps Essentials Cohort programme is designed to guide you as you start your journey into DevOps. With a project-based learning approach, you gain the principles, practices and implementation competency for executing the end-to-end modern software delivery process for any organisation as a DevOps engineer.

What You'll Learn:

· Source Code Management

· Linux System Administration

· Shell Scripting

· AWS Cloud Computing Fundamentals

· Continuous Integration & Deployment

· Containerization& Orchestration

· Infrastructure Provisioning

· Configuration Management

· System Monitoring


This training is for aspiring DevOps engineers who are looking to pursue a career in this revolutionary software engineering field or those looking to enhance their current skills in it.

Learn the foundational skills for version control, enabling efficient collaboration and code management across projects. Gain essential knowledge in Linux fundamentals and system administration to manage and troubleshoot server environments. Automate routine tasks and enhance productivity with efficient bash shell scripting techniques.

System Operation Essentials

Build efficient CI/CD pipelines with tools like Jenkins and GitHub Actions for streamlined software delivery. Understand the principles of cloud computing while gaining hands-on experience in AWS Identity and Access Management (IAM) and designing scalable, fault-tolerant cloud infrastructures on Amazon Web Services.

Integration Essentials 

Explore the use of Terraform for automation of infrastructure provisioning and Ansible for server administration management. Learn how to containerize applications using Docker and orchestrate them effectively with Kubernetes to manage and scale deployments seamlessly.

Runtime Essentials 

Leverage the use of Gatus for uptime monitoring, along with the use of Prometheus/Grafana stack for modern system monitoring and reliability, including insights into AI-driven DevOps practices.

Monitoring Essentials 

DevOps Accelerator Cohort

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Learn core AWS concepts: regions, availability zones, pricing, and the shared responsibility model.

- Understand key compute services: EC2, Lambda, and Elastic Beanstalk.
- Explore storage options: S3, EBS, EFS, Glacier, and storage lifecycle management.
- Build and deploy apps using S3, CloudFront, Amplify, and API Gateway.
- Dive into database services: RDS, DynamoDB, ElastiCache, and Redshift.
- Master networking: VPCs, subnets, Route 53, NAT gateways, and CloudFront.
- Implement security: IAM roles/policies, KMS encryption, and Shield/WAF for protection.
- Use containers and serverless: ECS, EKS, Fargate, and Lambda.
- Automate with DevOps tools: CodePipeline, CloudFormation, and Terraform.
- Design scalable architectures with best practices for cost, resilience, and performance.



**Establish a strong foundation in AWS core principles, compute, and storage services.**

Module 1: AWS Foundations & Core Services

**Learn to manage relational and NoSQL databases and build modern, serverless applications.**

Module 2: Databases & Serverless Technologies

**Build secure, connected, and observable AWS environments.**

Module 3: Networking, Security & Monitoring

 **Learn automation, governance, and architectural best practices for scalable applications.**

Module 4: Infrastructure Management & Architecture

Cloud computing-AWS

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Embark on a transformative journey into the world of Artificial Intelligence and Machine Learning with our comprehensive 5-month Cohort program. This course is meticulously designed to provide you with the foundational knowledge and advanced skills required to excel in the rapidly evolving field of AI and ML. From understanding the basics of machine learning to mastering advanced techniques and deploying real-world AI solutions, this program covers it all.

You'll gain hands-on experience with data preprocessing, model evaluation, and advanced ML techniques such as reinforcement learning and natural language processing. By the end of the program, you will be equipped to tackle complex problems, build intelligent systems, and deploy scalable AI solutions, preparing you for roles such as Machine Learning Engineer, Data Scientist, or AI Specialist.

AI and Machine Learning are at the forefront of technological innovation, with increasing demand for skilled professionals, competitive salaries, and vast opportunities for career growth.



This module introduces the fundamental concepts of machine learning, including supervised and unsupervised learning, regression, and classification. You'll learn essential skills such as data preprocessing and regression techniques, and get hands-on experience by working on a project to predict sales success using marketing data.

 Introduction to Machine Learning

Dive deeper into machine learning with advanced concepts like cross-validation, dimensionality reduction, and ensemble methods. This module also covers reinforcement learning, recommendation systems, and anomaly detection. You'll apply these techniques to solve complex classification or regression problems and work on projects such as stock price forecasting and personalized movie recommendation systems.

 Machine Learning Techniques & Advanced Machine Learning Techniques

Explore the fascinating fields of Natural Language Processing (NLP) and Computer Vision. Learn about language modeling, text classification, and chatbots in NLP, and image classification, object detection, and generative models in Computer Vision. You'll work on projects like building a text classifier for spam detection and developing an intelligent traffic monitoring and analysis system.

NLP and Computer Vision

This module focuses on handling large datasets and optimizing machine learning models. You'll learn introduction to distributed computing, data pipelines, and cloud-based AI solutions using tools like PySpark on AWS or Google Cloud. Additionally, you'll gain skills in model deployment, real-time data streaming, and AI ethics. The module culminates in a project where you deploy a real-time prediction model using Docker and Kafka.

Big Data and Optimization 

AI and Machine Learning 

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The Data Science Track is a comprehensive 4-month cohort programme designed to provide a hands-on, practical foundation in data science. The course covers essential skills such as Python programming, data analysis, statistics, exploratory data analysis (EDA), data visualisation, and an introduction to machine learning. As a student, you will progress from basic programming concepts to implementing machine learning models and deploying these models using tools like Streamlit. The course also includes several mini projects and capstone projects that allow you to apply your skills to real-world problems.

Python Programming Basics 

Introduction to Object Oriented Programming 

Numpy and Pandas for data manipulation 

Basics of visualization in python 

Matplotlib and Seaborn for data visualization 

MODULE 1 & 2 

Descriptive Statistics 

Probability fundamentals 

Handling missing values and outliers 

Exploratory Data Analysis 

MODULE 3 & 4 

 Data distributions 

Sampling, estimations and Cental Limit Theorem 

Hypothesis testing and Statistical tests 

Data Preparation and feature engineering 

Machine learning algorithms 

Deployment on streamlit 

MODULE 5 & 6 

Data Science

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Begin your journey with foundational knowledge crucial for analyzing and interpreting data effectively.

Our goal is to gain a strong foundation in the same tools used by professional data analysts across various industries. By the end of this course, you'll have the understanding you need to work comfortably with data, extract insights, and create impactful visualizations.

- Introduction to Excel for Data Analysis
- Data Cleaning and Formatting
- Pivot Tables and Charts
- Basic Statistical Analysis in Excel

Excel for Data Analysis

- Introduction to Databases and SQL
- Writing Basic to Advanced SQL Queries
- Data Aggregation and Joins
- Working with Large Datasets

SQL for Data Analysis

- Introduction to Python and Pandas
- Data Manipulation and Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization with Matplotlib and Seaborn

Python for Data Analysis

- Introduction to Power BI Interface
- Creating Interactive Dashboards
- Data Modeling and DAX Functions
- Connecting PowerBI to MSSQL
- Storytelling with Data

School of Artificial Intelligence and Data Science

Unlock the potential of data and AI to solve complex problems and drive innovation.

Why Choose the School of AI and Data Science?

Learn the most sought-after skills in AI and data science with hands-on training, expert mentorship, and real-world projects. Our comprehensive curriculum is designed to prepare you for high-demand roles in this exciting field.

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