Courses in Leeds

Our Global Mobility, High Potential & Scale Up Visa course is a half-day course dedicated to covering these three visas and latest updates. View the Global Mobility, High Potential and Scale Up Visa training course dates below, delivered via Microsoft Teams. Course Overview:   This course is dedicated to providing you with a comprehensive understanding of these visa routes. We will cover the requirements for each new visa category, equipping you with the knowledge to navigate the application processes successfully. Our expert trainers will guide you through the intricacies of the Global Mobility, High Potential, and Scale Up Visa routes, ensuring you have a thorough grasp of the eligibility criteria and essential documentation. You will gain insights into each category, allowing you to assist your clients or organisation in making informed decisions regarding talent recruitment and business expansion. This course will shed light on the sponsor license requirements for the Global Mobility category. Understanding these requirements is crucial for organizations seeking to employ international talent. We’ll also explore the sponsor license application processes, enabling you to navigate the necessary steps with confidence.   Course Topics: Latest Changes & Announcements The requirements of each new category Application processes The sponsor license requirements for Global Mobility Sponsor license application processes   Course joining links, materials and instructions are sent out 24hours before the course starts.

Create an eye-catching A6 journal with a leather/pleather cover and decorative exposed binding. This 48 page book makes for a great personal project or gift, with options to add decorative papers, a nifty fastening and page divider. Cass will take you through the process step-by-step with this online workshop.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.

The goal of this course is to teach you how to deploy machine learning models in Flask. You'll also learn to deploy applications to the web. You'll learn to train your models, test them, and ensure that they are of the best quality.

Duration 4 Days 24 CPD hours This course is intended for This course is for Azure Administrators. The Azure Administrator implements, manages, and monitors identity, governance, storage, compute, and virtual networks in a cloud environment. The Azure Administrator will provision, size, monitor, and adjust resources as appropriate. This course teaches IT Professionals how to manage their Azure subscriptions, secure identities, administer the infrastructure, configure virtual networking, connect Azure and on-premises sites, manage network traffic, implement storage solutions, create and scale virtual machines, implement web apps and containers, back up and share data, and monitor your solution. Prerequisites Successful Azure Administrators start this role with experience in virtualization, networking, identity, and storage. Understanding of on-premises virtualization technologies, including: VMs, virtual networking, and virtual hard disks. Understanding of network configurations, including TCP/IP, Domain Name System (DNS), virtual private networks (VPNs), firewalls, and encryption technologies. Understanding of Active Directory concepts, including users, groups, and role-based access control. Understanding of resilience and disaster recovery, including backup and restore operations. 1 - Configure Microsoft Entra ID Describe Microsoft Entra ID benefits and features Describe Microsoft Entra concepts Compare Active Directory Domain Services to Microsoft Entra ID Select Microsoft Entra editions Implement Microsoft Entra join Implement Microsoft Entra self-service password reset 2 - Configure user and group accounts Create user accounts Manage user accounts Create bulk user accounts Create group accounts Create administrative units 3 - Configure subscriptions Identify Azure regions Implement Azure subscriptions Obtain an Azure subscription Identify Azure subscription usage Implement Microsoft Cost Management Apply resource tagging Apply cost savings 4 - Configure Azure Policy Create management groups Implement Azure policies Create Azure policies Create policy definitions Create an initiative definition Scope the initiative definition Determine compliance 5 - Configure role-based access control Implement role-based access control Create a role definition Create a role assignment Compare Azure roles to Microsoft Entra roles Apply role-based access control Review fundamental Azure RBAC roles 6 - Configure Azure resources with tools Use the Azure portal Use Azure Cloud Shell Use Azure PowerShell Use Azure CLI 7 - Use Azure Resource Manager Review Azure Resource Manager benefits Review Azure resource terminology Create resource groups Create Azure Resource Manager locks Reorganize Azure resources Remove resources and resource groups Determine resource limits 8 - Configure resources with Azure Resource Manager templates Review Azure Resource Manager template advantages Explore the Azure Resource Manager template schema Explore the Azure Resource Manager template parameters Consider Bicep templates Review QuickStart templates 9 - Configure virtual networks Plan virtual networks Create subnets Create virtual networks Plan IP addressing Create public IP addressing Associate public IP addresses Allocate or assign private IP addresses 10 - Configure network security groups Implement network security groups Determine network security group rules Determine network security group effective rules Create network security group rules Implement application security groups 11 - Configure Azure DNS Identify domains and custom domains Verify custom domain names Create Azure DNS zones Delegate DNS domains Add DNS record sets Plan for Azure Private DNS zones Review Azure Private DNS zone scenarios 12 - Configure Azure Virtual Network peering Determine Azure Virtual Network peering uses Determine gateway transit and connectivity Create virtual network peering Extend peering with user-defined routes and service chaining 13 - Configure network routing and endpoints Review system routes Identify user-defined routes Determine service endpoint uses Determine service endpoint services Identify private link uses 14 - Configure Azure Load Balancer Determine Azure Load Balancer uses Implement a public load balancer Implement an internal load balancer Determine load balancer SKUs Create back-end pools Create health probes Create load balancer rules 15 - Configure Azure Application Gateway Implement Azure Application Gateway Determine Azure Application Gateway routing Configure Azure Application Gateway components 16 - Configure storage accounts Implement Azure Storage Explore Azure Storage services Determine storage account types Determine replication strategies Access storage Secure storage endpoints 17 - Configure Azure Blob Storage Implement Azure Blob Storage Create blob containers Assign blob access tiers Add blob lifecycle management rules Determine blob object replication Upload blobs Determine Blob Storage pricing 18 - Configure Azure Storage security Review Azure Storage security strategies Create shared access signatures Identify URI and SAS parameters Determine Azure Storage encryption Create customer-managed keys Apply Azure Storage security best practices 19 - Configure Azure Files and Azure File Sync Compare storage for file shares and blob data Manage Azure file shares Create file share snapshots Implement Azure File Sync Identify Azure File Sync components Deploy Azure File Sync 20 - Configure Azure Storage with tools Use Azure Storage Explorer Use the Azure Import/Export service Use the WAImportExport tool Use the AzCopy tool 21 - Configure virtual machines Review cloud services responsibilities Plan virtual machines Determine virtual machine sizing Determine virtual machine storage Create virtual machines in the Azure portal Connect to virtual machines 22 - Configure virtual machine availability Plan for maintenance and downtime Create availability sets Review update domains and fault domains Review availability zones Compare vertical and horizontal scaling Implement Azure Virtual Machine Scale Sets Create Virtual Machine Scale Sets Implement autoscale Configure autoscale 23 - Configure Azure App Service plans Implement Azure App Service plans Determine Azure App Service plan pricing Scale up and scale out Azure App Service Configure Azure App Service autoscale 24 - Configure Azure App Service Implement Azure App Service Create an app with App Service Explore continuous integration and deployment Create deployment slots Add deployment slots Secure your App Service app Create custom domain names Back up and restore your App Service app Use Azure Application Insights 25 - Configure Azure Container Instances Compare containers to virtual machines Review Azure Container Instances Implement container groups Review the Docker platform 26 - Configure file and folder backups Describe Azure Backup benefits Implement Backup Center for Azure Backup Configure Azure Recovery Services vault backup options Use the Microsoft Azure Recovery Services (MARS) agent Configure on-premises file and folder backups 27 - Configure virtual machine backups Explore options to protect virtual machine data Create virtual machine snapshots in Azure Backup Set up Azure Recovery Services vault backup options Back up your virtual machines Restore your virtual machines Implement System Center DPM and Azure Backup Server Compare the MARS agent and Azure Backup Server Implement soft delete for your virtual machines Implement Azure Site Recovery 28 - Configure Azure Monitor Describe Azure Monitor key capabilities Describe Azure Monitor components Define metrics and logs Identify monitoring data and tiers Describe activity log events Query the activity log 29 - Configure Azure alerts Describe Azure Monitor alerts Manage Azure Monitor alerts Create alert rules Create action groups 30 - Configure Log Analytics Determine Log Analytics uses Create a Log Analytics workspace Create Kusto (KQL) queries Structure Log Analytics queries 31 - Configure Network Watcher Describe Azure Network Watcher features Review IP flow verify diagnostics Review next hop diagnostics Visualize the network topology

This is a hands-on comprehensive course for beginners and in just two hours, you will learn the fundamentals of the Hadoop Ecosystem and its three main building blocks. This course will prepare you to start learning more about big data and to implement Hadoop components in Azure Cloud using HDInsight.

If you are searching for the right course to learn about iOS Development from scratch, you've just found it. This course will take you from a complete beginner to a confident practitioner, as we focus on practical iOS development without neglecting the theory.

Thinking about learning more about the ethical use of AI? The BCS Foundation Award; Understanding the Role of Ethics in the Responsible Use of AI, explores the responsibility of organisations and society towards ensuring AI is implemented for the good of others. It considers the potential harm AI may pose, and the safeguards that can be implemented to ensure it is used safely, ethically and for the good of society. You will be encouraged to explore the benefits associated with AI and the potential value it can add towards the continued evolution of humankind if managed well You will learn learn about the impact and level of responsibility of AI in business, an understanding of the need to scale up the impact and responsibility of AI to society, understand the potential harm and safeguards and understand the role of humans in an AI world.

Duration 3 Days 18 CPD hours This course is intended for Senior Executives CIOs and CTOs Business Intelligence Executives Marketing Executives Data & Business Analytics Specialists Innovation Specialists & Entrepreneurs Academics, and other people interested in Big Data Overview More specifically, BDAW addresses advanced big data architecture topics, including, data formats, transformation, real-time, batch and machine learning processing, scalability, fault tolerance, security and privacy, minimizing the risk of an unsound architecture and technology selection. Big Data Architecture Workshop (BDAW) is a learning event that addresses advanced big data architecture topics. BDAW brings together technical contributors into a group setting to design and architect solutions to a challenging business problem. The workshop addresses big data architecture problems in general, and then applies them to the design of a challenging system. Throughout the highly interactive workshop, students apply concepts to real-world examples resulting in detailed synergistic discussions. The workshop is conducive for students to learn techniques for architecting big data systems, not only from Cloudera?s experience but also from the experiences of fellow students. Workshop Application Use Cases Oz Metropolitan Architectural questions Team activity: Analyze Metroz Application Use Cases Application Vertical Slice Definition Minimizing risk of an unsound architecture Selecting a vertical slice Team activity: Identify an initial vertical slice for Metroz Application Processing Real time, near real time processing Batch processing Data access patterns Delivery and processing guarantees Machine Learning pipelines Team activity: identify delivery and processing patterns in Metroz, characterize response time requirements, identify Machine Learning pipelines Application Data Three V?s of Big Data Data Lifecycle Data Formats Transforming Data Team activity: Metroz Data Requirements Scalable Applications Scale up, scale out, scale to X Determining if an application will scale Poll: scalable airport terminal designs Hadoop and Spark Scalability Team activity: Scaling Metroz Fault Tolerant Distributed Systems Principles Transparency Hardware vs. Software redundancy Tolerating disasters Stateless functional fault tolerance Stateful fault tolerance Replication and group consistency Fault tolerance in Spark and Map Reduce Application tolerance for failures Team activity: Identify Metroz component failures and requirements Security and Privacy Principles Privacy Threats Technologies Team activity: identify threats and security mechanisms in Metroz Deployment Cluster sizing and evolution On-premise vs. Cloud Edge computing Team activity: select deployment for Metroz Technology Selection HDFS HBase Kudu Relational Database Management Systems Map Reduce Spark, including streaming, SparkSQL and SparkML Hive Impala Cloudera Search Data Sets and Formats Team activity: technologies relevant to Metroz Software Architecture Architecture artifacts One platform or multiple, lambda architecture Team activity: produce high level architecture, selected technologies, revisit vertical slice Vertical Slice demonstration Additional course details: Nexus Humans Big Data Architecture Workshop 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 Big Data Architecture Workshop 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.

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.