7177 CLO courses in Cardiff delivered Online

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is someone who works, or has ambition to work, in a leadership role in data center operations such as a data center facilities manager, data center operations manager, who has the responsibility to achieve and improve the high-availability and manageability of the data center operations. Overview After completion of the course the participant will be able to: Perform the needs analysis translating business requirements to data center services Set-up and manage the data center operations team Implement and monitor safety- and security practices Identify a suitable maintenance program for the data center facility and its equipment Select the appropriate policies and procedures required for data center operations Monitor the data center availability, capacity and capability Manage and implement basic data center projects Set-up and implement an environmental sustainability program Select an appropriate back-up site to support organizational resilience Identify and respond to risk in the data center Manage and support the document life cycle Create a basic budget proposal Select and manage vendors and measure their performance Manage data center assets Managing the facilities of today?s high-end and high-availability data centers is an extremely demanding and complex task which is often underestimated. There is often very little appreciation and understanding of the complexities of managing today's mission-critical data centers where downtime is not an option, especially since many of the data centers are operating at, or near, their design limits. Operations management at the facilities layer makes all the difference. Even a data center designed to the highest redundancy level as per the ANSI/TIA-942 standard could still experience many unscheduled downtime events due to poor planning, operations, maintenance and management processes. Service Level Management Service Level Management Needs analysis Capability assessment Service portfolio Service catalogue Service Level Agreements (SLAs) Availability measurement Data points in SLA Service reporting Complaint procedure Customer satisfaction measurement Service Improvement Process (SIP) SLA content The Data Center Organization Operational issues Organization chart Roles and responsibilities Skills matrix Contingency / backup roles Shift management Performance management Career development Training and assessment Job rotation Succession planning Disciplinary program Managing Safety & Statutory Requirements Safety policies and regulations Occupational Health and Safety (OH&S) Safety awareness training Permit to Work (PTW) Lock-out / Tag-out Personal Protective Equipment (PPE) Testing and tagging of equipment Emergency preparedness and response Reporting of safety issues Reviews / internal audit / external audit Managing Physical Security Security policies and procedures Security standards and guidelines Security staff Security awareness Security incident management Disciplinary program Reviews, internal and external audits Facilities Management Maintenance policies and procedures Various maintenance programs Outsourcing of maintenance activities Maintenance contract options Warranty Maintenance schedule Service situations Spart part management Contamination control Data Center Operations Policies and procedures for data center operations Service operations and the daily data center operations Monitoring / Reporting / Control Monitoring requirements Escalation procedures Reporting Trend analysis Reviews Project Management Project management Project organization Project manager Project phases Environment Sustainability The importance of sustainability Sustainability policies Environmental management Power efficiency indicators - Waste management - Water management ICT utilisation management Environmental performance measurements Renewable energy factor (REF) Organizational Resilience Business continuity Data center facility options Business Impact Analysis Type of facility Human resources Facility, equipment and consumables Governance, Risk and Compliance Management commitment Coordination, collaboration and integration Compliance Risk management Document management Financial management Vendor management Asset management Additional course details: Nexus Humans Certified Data Center Facilities Operations Manager (CDFOM) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Certified Data Center Facilities Operations Manager (CDFOM) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies, are using AWS to lower costs, become more agile, and innovate faster.

Securing Kubernetes training course description This course introduces concepts, procedures, and best practices to harden Kubernetes based systems and container-based applications against security threats. It deals with the main areas of cloud-native security: Kubernetes cluster setup, Kubernetes cluster hardening, hardening the underlying operating system and networks, minimizing microservices vulnerabilities, obtaining supply chain security as well as monitoring, logging, and runtime security. What will you learn Harden Kubernetes systems and clusters. Harden containers. Configure and use Kubernetes audit logs. Securing Kubernetes training course details Who will benefit: Technical staff working with Kubernetes Prerequisites: Kubernetes_for_engineers_course.htm Definitive Docker for engineers Duration 2 days Securing Kubernetes training course contents This course does not only deal with the daily security administration of Kubernetes-based systems but also prepares delegates for the official Certified Kubernetes Security Specialist (CKS) exams of the Cloud Native Computing Foundation (CNCF). Structure: 50% theory 50% hands on lab exercise Module 1: User and authorization management Users and service accounts in Kubernetes Authenticating users Managing authorizations with RBAC Module 2: Supply chain security Vulnerabilit checking for images Image validation in Kubernetes Reducing image footprint Secure image registries Module 3: Validating cluster setup and penetration testing Use CIS benchmark to review the security configuration of Kubernetes components. Modify the cluster components' configuration to match the CIS Benchmark. Penetration testing Kubernetes for known vulnerabilities. Module 4: System hardening Use kernel hardening tools Setup appropriate OS level security domains Container runtime sandboxes Limit network access Module 5: Monitoring and logging Configure Kubernetes audit logs Configure Audit Policies Monitor applications behaviour with Falco

Kubernetes for engineers training course description This course covers how Kubernetes addresses the challenges of distributed systems. Hands on sessions follow all the major theory chapters. What will you learn Explain what Kubernetes is and how it works. Create and run containers on Kubernetes using the Docker image format and container runtime. Kubernetes for engineers training course details Who will benefit: Anyone working with Docker or Kubernetes. Prerequisites: Definitive Docker for engineers. Duration 2 days Kubernetes for engineers training course contents Introduction Velocity, Scaling your service and your teams, Abstracting your infrastructure. Creating and running containers Container images, Building application images with Docker, Storing images in a remote registry, The Docker container runtime. Deploying a Kubernetes cluster Installing Kubernetes on a public cloud provider, Installing Kubernetes locally using minikube, Running Kubernetes on Raspberry Pi, The Kubernetes client, Cluster components. Common kubectl Commands Namespaces, Contexts, Viewing Kubernetes API objects, Creating, Updating, and Destroying Kubernetes objects, Labelling and annotating objects, Debugging commands. Pods Pods in Kubernetes, Thinking with pods, The pod manifest, Running pods, Accessing your pod, Health checks, Resource management, Persisting data with volumes, Putting It all together. Labels and Annotations Labels, Annotations. Service Discovery What Is Service discovery? The service object, Looking beyond the cluster, Cloud integration, Advanced details. ReplicaSets Reconciliation loops, Relating pods and ReplicaSets, Designing with ReplicaSets, ReplicaSet Spec, Creating a ReplicaSet, Inspecting a ReplicaSet, Scaling ReplicaSets, Deleting ReplicaSets. DaemonSets DaemonSet scheduler, Creating DaemonSets, Limiting DaemonSets to specific nodes, Updating a DaemonSet, Deleting a DaemonSet. Jobs The job object, Job patterns. ConfigMaps and secrets ConfigMaps, Secrets, Naming constraints, Managing ConfigMaps and secrets. Deployments Your first deployment, Creating deployments, Managing deployments, Updating deployments, Deployment strategies, Deleting a deployment. Integrating storage solutions and Kubernetes Importing external services, Running reliable singletons, Kubernetes-native storage with StatefulSets. Deploying real-world applications Parse, Ghost, Redis.

NFV training course description Network Functions Virtualization (NFV) brings many benefits, this training course cuts through the hype and looks at the technology, architecture and products available for NFV. What will you learn Explain how NFV works. Describe the architecture of NFV. Explain the relationship between NFV and SDN. Recognise the impact NFV will have on existing networks. NFV training course details Who will benefit: Anyone wishing to know more about NFV. Prerequisites: Introduction to Virtualization. Duration 2 days NFV training course content Introduction What is NfV? What are network Functions? NfV benefits, NfV market drivers. ETSI NfV framework. Virtualization review Server, storage and network virtualization and NfV. Virtual machines, containers and docker. Data centres, clouds, SaaS, IaaS, PaaS. Virtualization of Network Functions Network virtualization versus Network Function virtualization. ETSI NfV architecture ETSI documents, Architecture overview, compute domain, hypervisor domain, infrastructure network domain. IETF and NfV Creating services, Service Functions, Service Function Chaining. SPRING and source packet routing. YANG and NetConf. RESTCONF. VLANs, VPNs, VXLAN. MANO Management and Orchestration. OpenStack, OpenDaylight PaaS and NfV. The VNF domain. Service graphs, MANO descriptors, Open orchestration. The virtualization layer VM centric model, containers versus hypervisors, FD.io. Summary Deploying NfV, performance, testing. Futures.

Signalling training course description An intensive course that defines and explores the signalling methods that are to be found in today's telecommunications services. What will you learn Describe the Functionality and Features of Signalling. Describe the Functionality of Analogue & Digital Subscriber Signalling. Describe the various types of signalling used on different network types. Describe the Functionality of Private Network Signalling. Describe the Functionality of Public Network Signalling. Signalling training course details Who will benefit: Personnel involved with systems design, implementation and support. Prerequisites: Telecommunications Introduction Duration 2 days Signalling training course contents Introduction What is Signalling?, Standards, ITU-T Recommendations, Signalling Categories - Supervisory Addressing, E.164, Call Information, Network Management, Network Components, Inband/Outband Switch Signalling, Analogue Vs Digital Signalling. Analogue Subscriber Signalling Analogue Local Loops/Switches/Trunks, Digital Switches/Local Loops, Telephone Handset, Accessing the Local Exchange, Pulse/Tone Dialling. Digital Subscriber Signalling Integrated Digital Access, DASS2 & DPNSS, DASS2 - Call, IMUX, Euro ISDN, Q.931 Call Control, Message Identification, Message Types, Call Establishment Messages, Call Clearing. Network Types Service Types, Circuit Switched, Packet Switched, Signalling Terminology, In-Channel Signalling, G.704, Performance and Quality, Digital Signalling, CAS, CAS Applications, Foreign Exchange, CCS, Break-In/Out Private Network Signalling Types Networking PABXs, Inter PABX Analogue Signalling Methods, E & M, Tone-On-Idle, Inter PABX Digital Signalling Methods, DPNSS, DPNSS Deployment, PABX Support for DPNSS, DPNSS Call, Q.Sig, Q.Sig support/functionality/protocol, Message Overview, Call Establishment. Public Network Signalling SS7, SS7 Operations, SS7 Topology, SSP, STP, SCP, Database Types - CMSDB NP LIDB HLR VLR, Signalling Modes, Link Types, Further Redundancy, Linksets, SS7 addressing, Point Codes, Sub-System, Global Title Addressing and Translation, ANSI PCs, ITU-T PCs, SS7 Protocol Stack, MTP Level 1, MTP Level 2, Flow Control, FISU, LSSU, MSU, MSU SIF, MTP Level 3, SCCP, TCAP, TUP, Facility Format, Main Facilities, Flow Control Negotiation, Closed User Groups, Reverse Charging, Fast Select Facility, Throughput Class Negotiation, Call Barring, On-Line Facility Registration. BTUP, ISDN ISUP, Supplementary Services, ISUP Call - IAM, Progress/Answer/Suspend/ Resume/Release Messages, Intelligent Network (IN) Introduction, IN Evolution, IN Conceptual Model, IN Target Services & Service Features, Service Independent Building Blocks

Essential GEPON training course description Designed to benefit those requiring an in depth knowledge of the principles and applications of the IEEE Ten Gigabit Ethernet and Gigabit Ethernet Passive Optical Networking and Fibre to the X in NG network applications and their associated equipment, its flexibility and function within a modern transmission network. Using an effective mix of instruction and correlation to theory based learning the delegate will gain a complete understanding of the equipment and the tasks to be undertaken in a real life situation. What will you learn Compare FTTx networks. Compare PON variants. Recognise the GEPON architecture. Explain how GEPON works. Recognise GEPON issues. Essential GEPON training course details Who will benefit: Anyone requiring GEPON knowledge. Prerequisites: Introduction to data communications and networking. Duration 2 days Essential GEPON training course contents FTTN, FTTC, FTTH Single Mode Fibre (SMF) and various types, Multimode Fibre (MMF), Fibre Safety and properties (Dispersion/attenuation), Fibre Reel cables and types, Fibre installation and air blown fibre, Transmitters and receivers - power budget/laser classes, Fibre to the home (FTTH), FTTC (Fibre to the Cabinet), FTTN (Fibre to the node), FTTD (Fibre to the Desk), FFTH Topologies and wavelengths, Active or Passive Optical Network (PON). WDM equipment and GPON OSP design Wavelength considerations, WDM/DWDM/CWDM EDFA optical amplification, AWG (Arrayed Waveguide Grating) splitters, Couplers (splitters) and losses, Optical splitters 1x2, 1x4, 1x8, 1x16, 1x32, 1x64, 2x64. IEEE PON variants Gigabit Ethernet Passive Optical Network (GEPON), Time Division PON (TDM-PON), Wave Division Multiplexing PON (WDM-PON), 1Gbps, 10Gbps, 40Ggps, 100Gbps, Strategies for TDM-PON to WDM-PON migration, Architecture of NG-PON (hybrid WDM/TDM PON), Additional services than triple play. GEPON design GEPON OSP centralized design, GEPON OSP distributed design, GEPON PON splitters x4 x8 x32, Fibre splice trays / fibre cassette trays / fibre enclosures, GEPON field testing /GEPON field installation verification, GEPON physical layer testing, Optical Time Domain Reflectometer (OTDR), Optical power source /Optical power meter, Optical Return Loss (ORL), APON/BPON/GPON/EPON/GEPON/10-GEPON comparison. IEEE 802.3ah GEPON: Ethernet in the first mile IEEE 802.3 options, Optical Ethernet options, Ethernet in the first mile, 1000BASE-LX, 1000BASE-SX, IEEE 802.1Q VLANs, Q-in-Q and MAC-in-MAC. QofS Ethernet TOS and priority methods PCP and DiffServe, Reference model / terminology / architecture, Example of ONT functional blocks, Example of OLT functional blocks, FTTx scenarios, The four switching arrangements for external access network backup. IEEE 802.3av 10-GEPON Physical layer, 10GBASE-SR, 10GBASE-LX4, 10GBASE-ER, 10GBASE-LR, 10GBASESW, 10GBASE-LW, 10GBASE-EW, Enhancement band, Bit rate and wavelengths, Compatibility, Forward error correction. IEEE 802.3ca 25G, 50G and 100G NG-EPON MAC frame structure, Downstream multiplexing / Upstream multiplexing, Media access control and ONU registration, Alarm messages. IEEE 802.3bk extended EPON Laser Types PRX40 and PR40, Reference model. GEPON issues and standards GEPON components OLT / GEPON ONT and examples GEPON management, RG (Residential Gateway), HPNA (Home Phone Network Alliance), Power Line Carrier (PLC), GPON DLNI, G.hn or G.9960 MOCA, FTTH Council certification, Standard for network certification, Qualify for use of the fibre-connected home badge, GEPON frame synchronization to network timing, Direct clock synchronization interface (BITS), Multiservice Access Platform (MSAP), Software planning tool. Superconnected cities / voucher scheme. Ethernet OAM Link monitoring, remote failure indication, Remote loopback.

ITIL® 4 Specialist: High Velocity IT: In-House Training The ITIL® 4 Specialist: High-Velocity IT module is part of the Managing Professional stream for ITIL® 4. Candidates need to pass the related certification exam for working towards the Managing Professional (MP) designation. This course is based on the ITIL® 4 Specialist: High-Velocity IT exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam. This module addresses the specifics of digital transformation and helps organizations to evolve towards a convergence of business and technology, or to establish a new digital organization. It was designed to enable practitioners to explore the ways in which digital organizations and digital operating models function in high-velocity environments. Working practices such as Agile and Lean, and technical practices and technologies such as Cloud, Automation, and Automatic Testing are included. What You Will Learn At the end of this course, participants will be able to: Understand concepts regarding the high-velocity nature of the digital enterprise, including the demand it places on IT. Understand the digital product lifecycle in terms of the ITIL operating model. Understand the importance of the ITIL guiding principles and other fundamental concepts for delivering high-velocity IT. Know how to contribute to achieving value with digital products. Course Introduction Let's Get to Know Each Other Course Learning Objectives Target Audience Characteristics ITIL® 4 Certification Scheme Course Components Course Agenda Module-End Exercises Exam Details Introduction to High-Velocity IT High-Velocity IT Digital Technology Digital Organizations Digital Transformation High-Velocity IT Approaches Relevance of High-Velocity IT Approaches High-Velocity IT Approaches in Detail High-Velocity IT Operating Models Introduction ITIL® Perspective High-Velocity IT Aspects High-Velocity IT Applications ITIL® Building Blocks for High-Velocity IT Digital Product Lifecycle Service Value Streams Four Dimensions of Service Management ITIL® Management Practices High-Velocity IT Culture Key Behavior Patterns ITIL® Guiding Principles Supporting Models and Concepts for Purpose Ethics Design Thinking Supporting Models and Concepts for People Reconstructing for Service Agility Safety Culture Stress Prevention Supporting Models and Concepts for Progress Working in Complex Environments Lean Culture ITIL® Continual Improvement Model High-Velocity IT Objectives and Techniques High-Velocity IT Objectives High-Velocity IT Techniques Techniques for Valuable Investments Prioritization Techniques Minimum Viable Products and Services Product / Service Ownership A/B Testing Techniques for Fast Developments Basic Concepts Related to Fast Development Infrastructure as Code Reviews Continual Business Analysis Continuous Integration / Continuous Delivery (CI/CD) Continuous Testing Kanban Techniques for Resilient Operations Introduction to Resilient Operations Technical Debt Chaos Engineering Definition of Done Version Control Algorithmic IT Operations ChatOps Site Reliability Engineering (SRE) Techniques for Co-created Value Basic Concepts of Co-created Value Service Experience Techniques for Assured Conformance DevOps Audit Defense Toolkit DevSecOpsPeer Review

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Duration 3 Days 18 CPD hours This course is intended for The audience for this course are data professionals and business intelligence professionals who want to learn how to accurately perform data analysis using Power BI. This course is also targeted toward those individuals who develop reports that visualize data from the data platform technologies that exist on both in the cloud and on-premises. This course covers the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Prerequisites Understanding core data concepts. Knowledge of working with relational data in the cloud. Knowledge of working with non-relational data in the cloud. Knowledge of data analysis and visualization concepts. DP-900T00 Microsoft Azure Data Fundamentals is recommended 1 - Discover data analysis Overview of data analysis Roles in data Tasks of a data analyst 2 - Get started building with Power BI Use Power BI Building blocks of Power BI Tour and use the Power BI service 3 - Get data in Power BI Get data from files Get data from relational data sources Create dynamic reports with parameters Get data from a NoSQL database Get data from online services Select a storage mode Get data from Azure Analysis Services Fix performance issues Resolve data import errors 4 - Clean, transform, and load data in Power BI Shape the initial data Simplify the data structure Evaluate and change column data types Combine multiple tables into a single table Profile data in Power BI Use Advanced Editor to modify M code 5 - Design a semantic model in Power BI Work with tables Create a date table Work with dimensions Define data granularity Work with relationships and cardinality Resolve modeling challenges 6 - Add measures to Power BI Desktop models Create simple measures Create compound measures Create quick measures Compare calculated columns with measures 7 - Add calculated tables and columns to Power BI Desktop models Create calculated columns Learn about row context Choose a technique to add a column 8 - Use DAX time intelligence functions in Power BI Desktop models Use DAX time intelligence functions Additional time intelligence calculations 9 - Optimize a model for performance in Power BI Review performance of measures, relationships, and visuals Use variables to improve performance and troubleshooting Reduce cardinality Optimize DirectQuery models with table level storage Create and manage aggregations 10 - Design Power BI reports Design the analytical report layout Design visually appealing reports Report objects Select report visuals Select report visuals to suit the report layout Format and configure visualizations Work with key performance indicators 11 - Configure Power BI report filters Apply filters to the report structure Apply filters with slicers Design reports with advanced filtering techniques Consumption-time filtering Select report filter techniques Case study - Configure report filters based on feedback 12 - Enhance Power BI report designs for the user experience Design reports to show details Design reports to highlight values Design reports that behave like apps Work with bookmarks Design reports for navigation Work with visual headers Design reports with built-in assistance Tune report performance Optimize reports for mobile use 13 - Perform analytics in Power BI Explore statistical summary Identify outliers with Power BI visuals Group and bin data for analysis Apply clustering techniques Conduct time series analysis Use the Analyze feature Create what-if parameters Use specialized visuals 14 - Create and manage workspaces in Power BI Distribute a report or dashboard Monitor usage and performance Recommend a development life cycle strategy Troubleshoot data by viewing its lineage Configure data protection 15 - Manage semantic models in Power BI Use a Power BI gateway to connect to on-premises data sources Configure a semantic model scheduled refresh Configure incremental refresh settings Manage and promote semantic models Troubleshoot service connectivity Boost performance with query caching (Premium) 16 - Create dashboards in Power BI Configure data alerts Explore data by asking questions Review Quick insights Add a dashboard theme Pin a live report page to a dashboard Configure a real-time dashboard Set mobile view 17 - Implement row-level security Configure row-level security with the static method Configure row-level security with the dynamic method Additional course details: Nexus Humans PL-300T00: Microsoft Power BI Data Analyst training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the PL-300T00: Microsoft Power BI Data Analyst course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.