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Data center cooling is vital for lowering operational costs and maintaining equipment like servers and network devices. Cooling options include free cooling, room cooling, rack-mounted DX thermal cooling, row-based cooling, and centralized chillers. As power usage and density rise, effective heat removal has become crucial in data center design and operation.itical consideration in the design and operation of these facilities. DCT Data Center Cooling What this course is all about. Data centre cooling provides optimized cooling to ensure lower operation cost , well maintained active equipment ( servers , storage and network equipment) . The cooling options are, Free cooling , Room cooling , Rack mounted DX thermal cooling , row based cooling and centralized chiller systems. As the data center power and density has increased every year, the need to remove the heat generated has become a more important factor for the design and operation of the facility. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

The DCT PMIDC course equips project managers with the skills to manage complex projects using a defined methodology from start to finish. DCT Project Management for Infrastructure and Data Center Managers ( PMIDC ) What this course is all about. The DCT PMIDC course gives project managers the skills to handle complex projects with defined methodology from start to finish. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

DCT Cloud Computing Essentials provides insight into how cloud computing enables individuals and organizations to leverage the cost benefits of transitioning from on-premises equipment to cloud solutions, particularly with faster and more reliable internet. This course is ideal for those planning to migrate to the cloud and helps users understand the fundamental concepts of cloud computing related to computing and storage. DCT Cloud Fundamentals What this course is all about. Course Overview This 3-day hands-on workshop introduces participants to the foundational concepts of cloud computing — including cloud models, deployment architectures, and essential services. Learners will explore real-world use cases, perform guided labs using open-source or free-tier tools, and build confidence in navigating cloud environments. Target Audience IT professionals new to cloud technologies System administrators and developers transitioning to cloud environments Students or tech enthusiasts with general IT knowledge Learning Objectives By the end of the workshop, participants will be able to: Understand core cloud computing concepts and terminology Differentiate between IaaS, PaaS, and SaaS Identify and explain public, private, and hybrid cloud models Use a cloud platform to deploy and manage basic workloads Understand cloud networking, storage, and security fundamentals Estimate and optimize cloud costs Topics Day 1: Introduction to Cloud Concepts and Architecture Module 1: Cloud Fundamentals What is Cloud Computing? Key characteristics (on-demand, elasticity, scalability, pay-as-you-go) The evolution of cloud computing Benefits and challenges Common use cases Module 2: Cloud Service Models Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Software as a Service (SaaS) When to use each model Hands-on Lab 1 (2 hours): Deploying a Virtual Machine using a free-tier or open cloud (e.g., AWS Free Tier, Azure Free, or OpenStack demo) Exploring instance types, regions, and availability zones Module 3: Cloud Deployment Models Public, Private, Hybrid, and Multi-cloud Cloud providers overview: AWS, Azure, Google Cloud, OpenStack Comparing deployment strategies Day 2: Core Cloud Services and Infrastructure Module 4: Cloud Compute, Storage, and Networking Virtual Machines and Containers Object, Block, and File Storage Cloud Networking Basics (VPCs, subnets, routing, security groups) Hands-on Lab 2: Create a virtual network and connect two VMs Configure firewall rules and test connectivity Upload and retrieve files using an object storage bucket Module 5: Cloud Security and Compliance Shared Responsibility Model Identity and Access Management (IAM) Encryption basics (data at rest, in transit) Security best practices Group Activity Design a secure cloud environment for a small business scenario Day 3: Cloud Automation, Management, and Cost Optimization Module 6: Cloud Management and Monitoring Cloud dashboards and CLI tools Resource tagging and organization Monitoring and logging basics Introduction to Infrastructure as Code (IaC) Hands-on Lab 3: Automate VM creation with a simple Terraform or cloud CLI script Monitor instance performance and resource usage Module 7: Cloud Economics and Cost Optimization Pricing models (on-demand, reserved, spot) Estimating cloud costs Cost optimization strategies Tools for budgeting and billing alerts Module 8: Emerging Cloud Trends & Wrap-up Serverless computing overview Containers and Kubernetes basics Edge and hybrid cloud trends Review and final Q&A Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

DCT Cloud Computing Essentials provides insight into how cloud computing enables individuals and organizations to leverage the cost benefits of transitioning from on-premises equipment to cloud solutions, particularly with faster and more reliable internet. This course is ideal for those planning to migrate to the cloud and helps users understand the fundamental concepts of cloud computing related to computing and storage. DCT Artificial Intelligence Associate Course What this course is all about. Course Description This associate-level workshop provides a comprehensive foundation in Artificial Intelligence (AI) — combining theory, practical lab work, and real-world applications. Participants will learn how AI systems are built, trained, and deployed using both open-source and cloud tools. By the end of the course, learners will be able to design small-scale AI solutions, evaluate models, and understand AI’s ethical, business, and operational impacts. Learning Outcomes By the end of the course, participants will be able to: Explain the fundamental principles, history, and categories of Artificial Intelligence. Prepare and process data for AI and ML applications. Implement machine learning and deep learning models for basic use cases. Understand how generative AI and large language models (LLMs) function. Deploy and manage AI models in both on-premise and cloud environments. Identify ethical and responsible AI considerations. Integrate AI into IT and business processes. Target Audience IT Professionals, System Administrators, and Developers transitioning into AI Database Administrators and Data Engineers expanding into data-driven applications Cloud professionals seeking to integrate AI services into workflows Course Outline Module 1: Introduction to Artificial Intelligence and Data Foundations AI Overview and Evolution What is AI? Historical context and modern developments Key domains: Machine Learning, Deep Learning, Generative AI, Expert Systems AI use cases across industries (healthcare, finance, IT operations, etc.) Understanding AI Ecosystems AI frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face Open-source vs. cloud AI platforms (AWS, Azure, GCP) Data and the AI Lifecycle Role of data in AI projects Types of data (structured, unstructured, semi-structured) Basics of data engineering and data pipelines AI Project Lifecycle Defining the problem and use case selection Data collection and cleaning Model selection, training, testing, and deployment Hands-on Labs Setting up Python and Jupyter Notebook environment Exploring datasets using Pandas and NumPy Simple classification using a pre-trained model Module 2: Machine Learning and Model Evaluation Core Machine Learning Concepts Supervised, Unsupervised, and Reinforcement Learning Algorithms overview: Linear Regression, Decision Trees, KNN, Clustering Model training and validation workflow Feature Engineering and Data Preparation Data normalization and encoding Handling missing and imbalanced data Feature selection techniques Model Evaluation Confusion matrix, ROC curve, Precision/Recall/F1-score Avoiding overfitting and underfitting Model Optimization Hyperparameter tuning Cross-validation and grid search Hands-on Labs Building a supervised ML model from scratch Comparing algorithm performance Visualizing results using Matplotlib and Seaborn Module 3: Deep Learning, Generative AI, and Neural Networks Deep Learning Fundamentals Artificial Neural Networks (ANN) structure Convolutional and Recurrent Neural Networks (CNNs and RNNs) Transfer learning and pre-trained models Generative AI What is Generative AI? Large Language Models (LLMs): GPT, BERT, Claude, Gemini Prompt engineering basics Use cases: text generation, summarization, image synthesis Applied AI Computer vision, NLP, and speech recognition basics AI in automation (AIOps, Chatbots, Intelligent Assistants) Hands-on Labs Training a neural network on image data Text classification and summarization using pre-trained NLP models Experimenting with generative AI APIs (optional if cloud resources available) Module 4: AI Deployment, Responsible AI, and Cloud Integration AI Deployment and MLOps Model packaging and versioning REST APIs for inference Introduction to MLOps and CI/CD pipelines for AI Edge and Cloud AI Edge AI overview (TinyML, IoT inference) Cloud deployment using AWS SageMaker, Azure ML, or Vertex AI Cost and scaling considerations Ethics, Governance, and Responsible AI Bias, fairness, and transparency Explainable AI (XAI) AI regulation and governance frameworks Career and Industry Pathways AI job roles (AI Analyst, ML Engineer, Data Scientist Assistant) Continuing education and certifications roadmap Hands-on Labs Deploying an ML model as an API endpoint Model explainability exercise (SHAP/LIME demo) Group mini-project: design an AI-based solution for a business challenge Module 5 (Capstone Project) Capstone Project End-to-end AI workflow: Define a use case Collect and preprocess data Train and evaluate model Deploy locally or via a lightweight cloud environment Group presentations and feedback session Assessment and Certification Knowledge Check: Short quizzes and discussions at the end of each module Practical Evaluation: Hands-on lab submissions or in-class demos Capstone Project: Team-based AI solution (optional for shorter version) Tools & Platforms used Languages: Python Frameworks: TensorFlow, Scikit-learn, PyTorch, Keras Libraries: Pandas, NumPy, Matplotlib, Seaborn Cloud Options (optional): Azure AI Studio, AWS SageMaker, or Google Vertex AI IDE: JupyterLab / VS Code Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

Designed to give learners an understanding of how to secure their data and devices while using organizational or personal resources. DCT Cybersecurity awareness What this course is all about. Designed to give learners an understanding of how to secure their data and devices while using organizational or personal resources. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

This is Practical oriented course which covers the principles, Installation and integration of IP Surveillance, Storage & Network Video Recorder(NVR) and access control to give a unified solution on physical security. DCT IP Surveillance, Storage & Access Control What this course is all about. This is Practical oriented course which covers the principles, Installation and integration of IP Surveillance, Storage & Network Video Recorder(NVR) and access control to give a unified solution on physical security. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

Today’s Network Infrastructure for smart buildings starts with the foundation of copper and fiber cabling supported by Networking and wireless equipment to support POE devices on LAN and IoT devices. DCT Certified Network Infrastructure Professional What this course is all about. Today’s Network Infrastructure for smart buildings starts with the foundation of copper and fiber cabling supported by Networking and wireless equipment to support POE devices on LAN and IoT devices. The DCT Certified Network Infrastructure professional (CNIP) is a 5 days intensive practical course with the aim to develop knowledge and skills to design and implement complex projects. The course covers Network fundamentals , Networking standards , Wireless , Security , unified communications , Copper Systems and Fiber systems Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

Data center power systems are essential for the operation of data centers. This includes smart online UPS systems, tower UPS systems, modular UPS systems, inverters, solar power systems, and backup generators to ensure 100% uptime. An Automatic Transfer Switch (ATS) is installed to guarantee a continuous supply of power by transferring from the primary power source to a backup source in the event of a power failure.ckup source in the event of a power failure. DCT Data Center Power What this course is all about. Data Centre power systems are key to Data centre operations , which includes smart online UPS , Tower UPS , Modular UPS , Inverters , solar systems and backup generators to ensure 100% uptime . An Automatic transfer switch (ATS) is installed to ensure continuous delivery of power from primary source to a backup power source in case of power failure from the primary source. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

DCT Cloud Computing Essentials provides insight into how cloud computing enables individuals and organizations to leverage the cost benefits of transitioning from on-premises equipment to cloud solutions, particularly with faster and more reliable internet. This course is ideal for those planning to migrate to the cloud and helps users understand the fundamental concepts of cloud computing related to computing and storage. DCT Data Science Associate What this course is all about. Duration 40 Hours (5 Days) Description The Data Science Associate course is a practical, hands-on course designed to introduce participants to the fundamentals of data science, from data collection and cleaning to analysis, visualization, and basic machine learning. Over 40 hours, participants will learn how to work with real-world datasets using Python, explore data trends, build simple predictive models, and understand how data science integrates with cloud and data engineering workflows. This workshop is ideal for students, cloud professionals, database developers, and administrators looking to expand their skills into the growing field of data science. Audience Profile: Aspiring data engineers Cloud professionals expanding into analytics Database developers and administrators Students beginning their data science journey Prerequisites: Basic programming (Python preferred) Understanding of databases and SQL Familiarity with cloud or Linux environment is a plus Course Objectives By the end of this course, participants will be able to: Understand the data science lifecycle and key roles. Prepare, clean, and explore datasets effectively. Apply exploratory data analysis (EDA) techniques using Python. Build and evaluate basic machine learning models. Use visualization tools to communicate findings. Integrate cloud-based data tools for scalable analysis. Understand the link between data engineering and data science workflows. Course Outline Module 1: Introduction to Data Science and the Data Ecosystem What is Data Science? Data Science vs. Data Engineering vs. Machine Learning The Data Science Lifecycle Key tools and technologies (Python, Jupyter, Pandas, scikit-learn) Overview of roles in data teams Setting up the data science environment Lab Exercises Setting up Jupyter or VS Code notebooks Exploring a sample dataset (CSV) Simple data queries and transformations Module 2: Data Acquisition, Cleaning, and Preparation Types and sources of data (databases, APIs, files, cloud storage) Loading data with Pandas and SQL Data wrangling: handling missing data, duplicates, and outliers Data types and conversions Feature extraction and normalization Intro to ETL and integration with data engineering workflows Lab Exercises Loading data from CSV and SQL databases Cleaning and transforming a messy dataset Writing and executing data cleaning scripts in Python Module 3: Exploratory Data Analysis and Visualization Descriptive statistics and data summaries Correlation, covariance, and feature relationships Visualizing data using Matplotlib and Seaborn Identifying patterns and trends Introduction to hypothesis testing Lab Exercises Plotting and summarizing dataset features Detecting correlations and relationships visually Building a small exploratory data analysis report Module 4: Introduction to Machine Learning Machine learning fundamentals Supervised vs. Unsupervised Learning Model training, validation, and evaluation Classification and regression algorithms Model performance metrics (accuracy, precision, recall, RMSE) Introduction to scikit-learn Lab Exercises Building a regression and classification model Evaluating model accuracy Feature scaling and model tuning basics Module 5: Cloud Data Science and Capstone Project Cloud data science overview (AWS, Azure, GCP) Managed services: SageMaker, Vertex AI, Azure ML Using cloud storage and data warehouses (BigQuery, Redshift, Synapse) Integrating notebooks with cloud platforms Real-world workflow: from data ingestion to insight Capstone Lab (Comprehensive Project) Acquire, clean, and analyze a real dataset Perform EDA and build a predictive model Visualize and present results in a Jupyter notebook Discuss deployment and next steps Tools and Technologies Programming: Python 3.x Libraries: Pandas, NumPy, Matplotlib, Seaborn, scikit-learn Environment: JupyterLab or VS Code Databases: SQLite / PostgreSQL Visualization: Matplotlib, Seaborn, or Plotly Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

DCT Unified Communications is a unique Multi-vendor course that introduces IP Telephony, Unified Messaging, Unified Presence and Unified mail to ICT Professional who are interested in venturing into the world of IP communications The class based training offers advanced hands on experience labs to prepare students for any deployment scenarios. DCT Unified Communications & Video Conferencing. What this course is all about. DCT Unified Communications is a unique Multi-vendor course that introduces IP Telephony, Unified Messaging, Unified Presence and Unified mail to ICT Professional who are interested in venturing into the world of IP communications The class based training offers advanced hands on experience labs to prepare students for any deployment scenarios. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

The DCT Data Centre Design certification ( DCT-DC-DCD ) helps with design , architecting , engineering and planning of Data centres. DCT Data Center Design What this course is all about. The DCT Data Centre Design certification ( DCT-DC-DCD ) helps with design , architecting , engineering and planning of Data centres. The DCD certification covers best practices applicable in planning , documentation and construction of Data centres, Data Centre standards , Floor space , cabinet placement , Power , Cooling , Cabling and Building Management Systems (BMS) .The DCT-DC-DCD is a 5 day in-depth certified training which helps individuals to understand the concepts of building Data centre from design to construction from Feasibility study , documentation and design of a modern data centre. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here

The DCT Data Centre Design certification ( DCT-DC-DCD ) helps with design , architecting , engineering and planning of Data centres. ETA ( Fiber Optics Installer – FOI ) What this course is all about. A Fiber Optics Installer is trained in fiber installation, splicing, testing, and connectorization per TIA/EIA, ITU-T, and NEC standards, with expertise in loss testing, endface evaluation, and both fusion and mechanical splicing. Register Now Fill out your contact details below so that we can get in touch with you regarding your Registration Register here