ICT & Technologies

In today's economy, data is your most valuable asset—but only if you can transform it into intelligence. This workshop is designed for business leaders who need to move beyond the buzzwords and master the fundamentals of leveraging big data to drive AI-powered results.

The course demystifies the world of big data, starting with its core characteristics—the 5Vs (Volume, Velocity, Variety, Veracity, and Value)—and what they mean for your business strategy. We then cut through the complexity of the technology landscape, providing a clear overview of essential systems like distributed storage, advanced analytics, and the frameworks that support AI.

The training is built around the essential Ingest-Process-Serve workflow, the strategic blueprint for turning raw data into actionable insights. Through a dynamic blend of strategic theory, hands-on labs, and real-world case studies, this workshop equips you with the practical skills to lead data initiatives and build a bridge between your technical teams and your business objectives.

• Translate complex big data fundamentals into tangible business opportunities and strategic initiatives for your organization.
• Evaluate your company's data assets through the lens of the 5Vs (Volume, Velocity, Variety, Veracity, and Value) to identify key challenges and unlock new avenues for growth.
• Navigate the modern big data technology landscape (including Object Storage, Spark, and more) to make informed, high-impact decisions on technology investment and strategy.
• Architect a high-level Ingest-Process-Serve workflow, enabling you to effectively guide technical teams in building a scalable and efficient data pipeline.
• Develop a strategic roadmap for leveraging your data to power predictive analytics and AI applications, creating a sustainable competitive advantage for your business.

Day 1:
- The 5Vs and their impact on big data strategies.  

- Big data architectures and use cases.

- Design big data course group projects

Day 2:
- Overview of Hadoop’s impact.

- Big data storage techniques

- Introduction to big data technologies: object storage and Spark.

Day 3:
- Big data ingestion, processing and analyzing using Spark and python.  

- Big data analytics examples from storing, retrieving and analyzing data for AI.

Day 4:
- Big data AI real-world practice examples 

- Big data visualization techniques and examples

Day 5:

- Applying big data concepts in real-world projects.

- Finish course big data group projects

- Discussing trends, AI integration, and cloud solutions in big data.

-  Introduction to Big Data and its role in modern industries.

- The 5Vs of Big Data: Understanding data characteristics and challenges.

- The Hadoop ecosystem: Components, architecture, and practical applications.

- The Ingest-Store-Train-Serve-Intelligence workflow is used to handle large-scale data efficiently.

- Batch processing with MapReduce and Spark, and real-time streaming with Flink.

- Data ingestion and storage using Kafka, Flume, HBase, and NoSQL databases.

- Building scalable data pipelines for analytics and AI integration.

- Industry use cases and hands-on projects for practical experience.

- Big Data trends: Future AI, cloud computing, and machine learning advancements.

This five-day workshop is designed to provide participants with a solid foundation in Data Management.

It will focus on the principles and practices necessary to organize, maintain, and secure data across various platforms and environments.

More importantly, two days will be fully dedicated to SQL programming, on top of which participants will learn about data governance, quality control, metadata management, and data utilization in compliance with regulations and business requirements. 

• Understand the fundamentals of data governance and data stewardship.
• Learn techniques for data quality assurance, data cleaning, and maintenance.
• Gain insights into managing metadata and setting up compelling data catalogs.
• Develop strategies for data privacy and security that align with relevant regulations.
• Explore tools and techniques for data integration, warehousing, and lifecycle management.
• Build skills in creating and enforcing data policies and procedures that support business objectives. 

Day 1:

- Introduction to Data Management, Governance, and Data Quality Control.

Day 2: 

- Metadata Management, Data Security, Privacy, and Compliance

Day 3:

- SQL 1

Day 4:

- SQL 2

Day 5:

- Data Integration and Warehousing; Capstone Project Planning.

- Structured lectures with detailed course materials tailored to real-world applications.

- Hands-on labs using leading data management tools and software.

- SQL programming.

- Group activities to enhance learning through practical challenges.

- In-depth discussions on Data policies, Ethics, and Compliance issues.

- Real-world case studies illustrating data management challenges and solutions.

- A final project that applies learned concepts to a practical scenario, reinforcing the workshop’s teachings. 

Data Engineering is crucial for managing and analyzing big data, supporting advanced analytics applications across various industries.

This intensive 4-day workshop delves into the core aspects of data engineering, covering data architecture, ingestion, and storage on major big data platforms like Hadoop and Spark.

Participants will explore cloud technologies for big data and learn practical deployment techniques on AWS and Azure platforms.

• Gain a foundational understanding of big data platforms' architecture and components.
• Acquire practical skills with data ingestion tools such as Kafka and Sqoop.
• Explore and implement large-scale storage solutions including HDFS, HBase, and NoSQL databases.
• Develop competency in managing and analyzing data with Spark and Hadoop.
• Implement secure, scalable data solutions in diverse cloud environments.
• Address key data security, compliance, and governance aspects within big data engineering. 

Day 1:

- Introduction to Big Data Platforms

- Cloud computing technologies

Day 2: 

- Data Management and Analysis with Hadoop and Spark.

Day 3:

- Security, Compliance, and Capstone Project Design.

• Targeted, concise lectures complemented by comprehensive digital resources.
• Hands-on labs and practical exercises using industry-standard tools and platforms.
• Collaborative group projects designed to simulate real-world problem-solving.
• Focused discussions on deploying and securing data solutions in cloud environments.
• Case studies that highlight practical challenges and solutions in data engineering.
• A capstone project that integrates the skills learned, fostering a practical understanding of course materials. 

Data is the lifeblood of the modern web. This workshop will equip you with the full-stack skills needed to collect, analyze, and present data in engaging and insightful ways for users. 

We will explore the entire web development process, covering everything from front-end design and user experience (UX) principles to back-end data handling and visualization. 

Participants will gain a comprehensive understanding of user experience principles and best practices for designing intuitive and engaging web applications. Additionally, you will develop a strong understanding of data security and privacy best practices for web applications.

• Build robust, data-driven web applications f.
• Master front-end technologies like HTML, CSS, and JavaScript (including libraries like React or Vue.js) for creating interactive and user-friendly interfaces.
• Develop back-end APIs using languages like Python (with frameworks like Django or Flask) or Node.js to handle data storage, retrieval, and processing.
• Learn to design and implement effective database structures (e.g., SQL, NoSQL) to efficiently store and manage large datasets.
• Explore data visualization libraries and techniques to effectively communicate insights through interactive charts, graphs, and dashboards.

Day 1:
- HTML, CSS, JavaScript basics
- React/Vue.js

Day 2:
- Python/Node.js

- API design, database interactions (MongoDB).

Day 3:
- Building a Data-Driven Application
- UI and UX Principles: Wireframing, prototyping, usability testing

Day 4:
- Data Visualization and Interactive Dashboards
- Deployment and Hosting

Day 5:
- Security, scalability, performance optimization, and emerging trends

- Hands-on Projects: Building real-world web applications with a focus on data integration and visualization
- Industry Best Practices: Adhering to industry standards and best practices for web development
- Personalized Feedback: Expert guidance and feedback on individual projects
- Comprehensive Learning Materials: Access to course materials, code examples, and online resources
- Community Building: Networking opportunities with fellow learners and industry professionals 

 The Internet of Things (IoT) is transforming industries by connecting devices, collecting data, and enabling intelligent decision-making.

This workshop provides a comprehensive introduction to IoT fundamentals, covering platform design, constraints, protocols, and essential components that drive IoT solutions.

Participants will gain hands-on experience with real-world applications, exploring how IoT can optimize processes, enhance efficiency, and create new business opportunities. 

• Understand the core principles and architecture of IoT systems.
• Identify key constraints and challenges in IoT deployment.
• Learn about IoT communication protocols and data exchange mechanisms.
• Explore various IoT platforms and their integration with cloud services.
• Gain insights into IoT security and privacy considerations.
• Develop practical skills in designing and implementing IoT solutions.

Day 1:
- IoT overview and main concepts 

Day 2:
- Main architecture concepts 

Day 3:
- Design Constraints

Day 4:
- Standards and protocols 

Day 5:
- Advanced IoT concepts 

- Introduction to IoT concepts, architecture, and components.

- Key considerations for building scalable and designing an efficient IoT platforms.

-  MQTT, CoAP, HTTP, and other essential IoT communication protocols.

- Power consumption, network limitations, and device constraints.

- Processing data efficiently at the edge and in the cloud.

- Addressing cybersecurity threats and data protection in IoT.

- Real-world use cases across healthcare, smart cities, manufacturing, and more. 

In an era of increasing cyber threats, organizations must stay ahead with robust security strategies. This cybersecurity training provides a deep dive into essential security principles, focusing on real-world threats and defense mechanisms. Participants will explore reconnaissance techniques, threat modeling, securing web servers, managing permissions, and debunking common cybersecurity myths. Additionally, the workshop will introduce the power of machine learning and AI in cybersecurity, equipping attendees with cutting-edge tools to detect, prevent, and respond to cyberattacks. Whether you are an IT professional, security analyst, or business leader, this training will enhance your ability to safeguard digital assets and mitigate risks effectively. 

• Explore Security - Attack anatomy, cycles, and types
• Protect data against the latest cybersecurity threats.
• Strengthen environment with robust security frameworks.
• Detect and investigate malicious behavior in the BI system and environment. 
• Evaluate data security, analyze potential risks, and remediate. 
• Simulate real-case attacks according to the latest penetration testing methodologies. 
• Gain insights into the role of ML and AI in modern cybersecurity.

Day 1:

- Cybersecurity fundamentals & evolving threats

- Reconnaissance techniques & threat modeling

Day 2: 

- Role-Based vs. Attribute-Based Access Control

- Privilege escalation & cybersecurity myths

Day 3: AI in Cybersecurity & Threat Mitigation

- AI-driven threat detection & fraud prevention

- ML techniques for anomaly & malware analysis

- Reconnaissance & Threat Modeling: Identifying potential threats and assessing vulnerabilities.

- Web Server Security: Best practices for securing Apache, Nginx, and other web servers.

- Permissions & Common Myths: Understanding access control, privilege escalation, and security misconceptions.

- ML & AI in Cybersecurity: How machine learning and AI enhance threat detection and prevention.

- Real-World Case Studies: Examining cybersecurity incidents and mitigation strategies.