1672 Computing & IT courses in Banstead delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for Senior system administrators who wish to learn more about troubleshooting. Have earned a Red Hat Certified System Administrator (RHCSA) or have similar experience. It is recommend that students have earned a Red Hat Certified Engineer (RHCE) or have similar experience. Overview As a result of attending this course, students should be able to leverage the software development life-cycle process to plan, create, test, and provision host systems within an organization. Students should be able to demonstrate the following skills: - Installation of Red Hat Satellite 6 - Software life-cycle management of software used to provision and maintain configured hosts within a controlled production environment This course enriches your skills by providing the tools and techniques that you need to successfully diagnose, and fix, a variety of potential issues. You will work through hands-on problems in various subsystems to diagnose and fix common issues. You will learn develop the skills to apply the scientific method to a structured form of troubleshooting. You will then apply this approach in troubleshooting various types of problems, including boot issues, hardware issues, storage issues, RPM issues, network issues, third-party application issues, security issues, and kernel issues. At the end of the course, you will be empowered to complete various comprehensive review labs to test your skills. 1 - INTRODUCTION TO TROUBLESHOOTING Describe a generalized strategy for troubleshooting. 2 - TAKE PROACTIVE STEPS TO PREVENT SMALL ISSUES Prevent small issues from becoming large problems by employing proactive system administration techniques. 3 - TROUBLESHOOT BOOT ISSUES Identify and resolve issues that can affect a system's ability to boot. 4 - IDENTIFY HARDWARE ISSUES Identify hardware problems that can affect a system?s ability to operate. 5 - TROUBLESHOOT STORAGE ISSUES Identify and fix issues related to storage. 6 - TROUBLESHOOT RPM ISSUES Identify and fix problems in, and using, the package management subsystem. 7 - TROUBLESHOOT NETWORK ISSUES Identify and resolve network connectivity issues. 8 - TROUBLESHOOT APPLICATION ISSUES Debug application issues. 9 - DEAL WITH SECURITY ISSUES Identify and fix issues related to security subsystems. 10 - TROUBLESHOOT KERNEL ISSUES Identify kernel issues and assist Red Hat Support in resolving kernel issues. 11 - RED HAT ENTERPRISE LINUX DIAGNOSTICS AND TROUBLESHOOTING COMPREHENSIVE REVIEW Practice and demonstrate knowledge and skills learned in Red Hat Enterprise Linux Diagnostics and Troubleshooting.

Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators and network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer architecture, components, and main functions Explain the key features and benefits of NSX Advanced Load Balancer Explain and configure local load-balancing constructs such as virtual services, pools, health monitors, and related components Recognize web application breaches and threats Recognize multiple attack vectors such as web scraping, Layer 7 Denial of Service, brute force, and code injections Explain the components of NSX Advanced Load Balancer WAF that build a security pipeline to protect a web application from being attacked Describe how to configure the NSX Advanced Load Balancer WAF components Describe an NSX Advanced Load Balancer WAF operational task such as setting up an application with WAF, tuning the WAF Policy, and working with logs and analytics Explain the NSX Advanced Load Balancer WAF best practices for on-boarding a web application; configuring WAF settings for effective application security Explain how to size the NSX Advanced Load Balancer WAF data plane Explain the WAF Application learning feature, configuration of Application learning, Virtual Patching concepts, common caveats, and troubleshooting while deploying in any environment Recognize NSX Advanced Load Balancer Cloud Services that include threat Intelligence services Describe the Threat Intelligence service provided by NSX Advanced Load Balancer WAF and how the NSX Advanced Load Balancer WAF Threat Intelligence service receives live security threat feed for multiple attack vectors from Cloud Services (formerly Avi Pulse) Describe the NSX Advanced Load Balancer DataScript capabilities for detecting and defending against advance and zero-day attacks. Discuss the relevant NSX Advanced Load Balancer WAF logs and perform basic troubleshooting of applications that are protected by NSX Advanced Load Balancer WAF Explain the NSX Advanced Load Balancer WAF capability to protect Personally Identifiable Information (PII) This three-day course provides comprehensive training to install, configure, and manage a VMware NSX Advanced Load Balancer Web Application Firewall (WAF) solution. This course covers key NSX Advanced Load Balancer WAF features and functionality offered in the NSX Advanced Load Balancer 22.1.3 release for web security and application attack protection. Features include security pipeline, application learning, policy tuning, false positive mitigation, virtual patching, threat intelligence, troubleshooting, logs, analytics, and solution monitoring. Hands-on labs provide access to an NSX Advanced Load Balancer environment to reinforce the skills and concepts presented in the course. Course Introduction Introduction and course logistics Course objectives Introduction to NSX Advanced Load Balancer Illustrate NSX Advanced Load Balancer Explain NSX Advanced Load Balancer architecture and components Describe control plane clustering and high availability Describe data plane high availability mode Understand the common terminologies used with NSX Advanced Load Balancer Explain the NSX Advanced Load Balancer service elements Explain virtual service components and how to configure a virtual service Explain application profiles and network profiles Explain the pool configuration options and how to configure a pool Explain the available load-balancing algorithms Explain and configure SSL profiles and certificates Explain cloud connectors and cloud connector integration modes Explain multiple health monitor types Understand client logs Introduction to Application Security Understand web application security breaches and the implication of breaches Explain common terminologies related to Web Application Security Understand the different teams involved to secure applications Attacking Web Applications Understand the various web application security testing methodologies Understand the OWASP Top 10 vulnerabilities Understand the tools to generate a web application attack Describe a few types of web application attacks Types of Transport Understand different web traffic transport modes Describe web traffic and API traffic NSX Advanced Load Balancer WAF Components Understand the core design principles of NSX Advanced Load Balancer WAF Describe the NSX Advanced Load Balancer WAF components that build the WAF security pipeline Understand the NSX Advanced Load Balancer WAF configuration objects NSX Advanced Load Balancer WAF Operations Examine how to set up an application with WAF Describe considerations for the WAF policy Work with WAF logs and analytics Describe WAF policy tuning Describe the options available to remediate false positive mitigation NSX Advanced Load Balancer WAF Best Practices Describe technical and application considerations for onboarding an application front ended by WAF Describe best practices to remediate false positive mitigation. Describe how to manage a response from a back-end application server and client upload to the application server Describe the consideration for setting the rigidity of a WAF signature rule set Describe the options available to identify client traffic NSX Advanced Load Balancer WAF Sizing Understand how to do WAF data plane sizing in Greenfield and Brownfield deployments NSX Advanced Load Balancer WAF Custom Rules Understand WAF custom rules Describe the need and recommendation for custom rules Describe ModSecurity rules Understand the ModSecurity rule structure and explain how to construct the rule Analyze a sample custom rule for the use-case scenario for in-depth understanding of a custom rule NSX Advanced Load Balancer WAF Application Learning Understand the significance of Application Learning Explain the Positive Security Model architecture Describe the WAF multifaceted Application Learning technique to build an application model for creating positive security rules Describe how to view the data that is learned by the Application learning module Describe the WAF Virtual Patching technique to construct a WAF policy from Dynamic Application Security Testing (DAST) scanner results Understand the conditions for sharing WAF Learning Data and PSM Group in WAF Policy. Malware Protection Through ICAP in NSX Advanced Load Balancer Understand Malicious File Upload Protection and ICAP workflow Describe ICAP configuration and log analytics NSX Advanced Load Balancer IP Reputation Understand IP Reputation concepts and their integration with NSX Advanced Load Balancer Describe IP Reputation configuration, log analytics, and troubleshooting DataScript for Application Security Describe DataScript events and reference Describe application security using DataScript Explain how to troubleshoot DataScript issues Rate Limiting and DOS Describe and configure the NSX Advanced Load Balancer rate limiter technique Describe protection from denial of service (DoS) attacks and distributed DoS (DDoS) attacks in NSX Advanced Load Balancer Explain the Service Engine general advice and guidance for DDOS Bot Management Understand Bots Describe the Bot Management mechanism in NSX Advanced Load Balancer Describe how to configure NSX Advanced Load Balancer Bot Management Managing Personally Identifiable Information in NSX Advanced Load Balancer Understand Personally Identifiable Information (PII) Understand the scope of managing PII in NSX Advanced Load Balancer Describe how to configure the hidden PII in NSX Advanced Load Balancer logs using profiles and WAF rules. Threat Intelligence Introduce the Threat Intelligence service Describe the Threat Intelligence live security threat feed for multiple attack vectors Describe how to configure Threat Intelligence in NSX Advanced Load Balancer Application Programming Interface Security Define Application Programming Interface (API) Security Understand API authentication and authorization using virtual service authentication mechanisms used for a virtual service such as LDAP, SAML, JSON Web Token, and OAUTH Understand API Rate Limiting in NSX Advanced Load Balancer Understand the NSX Advanced Load Balancer WAF Protection for API Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware NSX Advanced Load Balancer: Web Application Firewall Security [V22.x] 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 VMware NSX Advanced Load Balancer: Web Application Firewall Security [V22.x] 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.

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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.

Duration 3 Days 18 CPD hours This course is intended for This course is for technical professionals to gain skills in writing rules for Snort-based Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). The primary audience includes: Security administrators Security consultants Network administrators System engineers Technical support personnel using open source IDS and IPS Channel partners and resellers Overview After taking this course, you should be able to: Describe the Snort rule development process Describe the Snort basic rule syntax and usage Describe how traffic is processed by Snort Describe several advanced rule options used by Snort Describe OpenAppID features and functionality Describe how to monitor the performance of Snort and how to tune rules The Securing Cisco Networks with Snort Rule Writing Best Practices (SSFRules) v2.1 course shows you how to write rules for Snort, an open-source intrusion detection and prevention system. Through a combination of expert-instruction and hands-on practice, this course provides you with the knowledge and skills to develop and test custom rules, standard and advanced rules-writing techniques, how to integrate OpenAppID into rules, rules filtering, rules tuning, and more. The hands-on labs give you practice in creating and testing Snort rules. Course Outline Introduction to Snort Rule Development Snort Rule Syntax and Usage Traffic Flow Through Snort Rules Advanced Rule Options OpenAppID Detection Tuning Snort

Duration 2 Days 12 CPD hours This course is intended for Software Test Engineers and Test Leaders with minimum two years of experience. Specialists who have experience in software testing and want to deepen both their theoretical and practical knowledge. Overview Please refer to Overview. Practical training that you will learn how to apply additional tools in your work which will help you to optimize it. You will be able to analyze requirements better and to prioritize your tasks. Requirements analysis Effective approaches in requirements analyze What does 'Good Requirement' mean? Change Management Subsystems and actors Practice - Analysis of an actual functional specification Applicable Models and Priorities Specifications of the extraordinary models software projects Prioritizing the tasks of the test team Practice case - Iteration and sprint specifications Test planning Test design approaches Applicable techniques Practice cases with different techniques Defects management Defect status management Applicable metrics Best practices Practical examples of extraordinary applications Additional course details: Nexus Humans Modern approaches and practical tips in software testing 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 Modern approaches and practical tips in software testing 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.

Duration 3 Days 18 CPD hours This course is intended for This advanced course is for IT professionals tasked with administering a Spectrum Scale system. Overview Please see Overview This course is intended for IT professionals tasked with administering a Spectrum Scale system. It includes information on installing, configuring and monitoring a Spectrum Scale cluster. Migrating to IBM Spectrum Scale 4.2Spectrum Scale 4.2 GUIMulti-clusterClustered NFSCluster Export Services for multi-protocol supportSMB Protocol SupportNFS Support in CES; Ganesha overview/performanceActive File ManagementAFM-Based Disaster Recovery (DR) and Asynchronous DRPlanning LTFS and GPFS environment for data archivingFile Placement OptimizerIBM© GPFS-FPO and integration with GPFS Hadoop connectorIBM© Spectrum Scale Call HomeMonitoring and performance tuningFlash Cache metadata migration

Duration 3 Days 18 CPD hours This course is intended for Information System Owners Analysts Ethical Hackers ISSOs Cyber Security Managers IT Engineers Overview Upon completion, the Certified Vulnerability Assessor candidate will be able to competently take the exam. This course provides a solid understanding of the tools an IT engineer needs to review an Information System. In this course, you will learn the importance of vulnerability assessments and how they are used to prevent serious cyber break-ins. Lessons include understanding malware and viruses and how they can infiltrate an organization?s network. After you take this course, you will be able to assess a company?s security posture and perform a basic vulnerability test.ÿPlus, you will be able to generate reports to guide new security implementation. Course Outline Why Vulnerability Assessment Vulnerability Types Assessing the Network Assessing Web Servers and Applications Assessing Remote and VPN Services Vulnerability Assessments & Tools of the Trade Output Analysis

Duration 5 Days 30 CPD hours This course is intended for This course is designed for technology leaders, solution developers, project managers, organizational decision makers, and other individuals seeking to demonstrate a vendor-neutral, cross-industry understanding of ethics in emerging data-driven technologies, such as AI, robotics, IoT, and data science. This course is also designed for professionals who want to pursue the CertNexus Certification Exam CET-110: Certified Ethical Emerging Technologies. Overview In this course, you will incorporate ethics into data-driven technologies such as AI, IoT, and data science. You will: Describe general concepts, theories, and challenges related to ethics and emerging technologies. Identify ethical risks. Practice ethical reasoning. Identify and mitigate safety and security risks. Identify and mitigate privacy risks. Identify and mitigate fairness and bias risks. Identify and mitigate transparency and explainability risks. Identify and mitigate accountability risks. Build an ethical organization. Develop ethical systems in technology-focused organizations. Mutually reinforcing innovations in computing and engineering are catapulting advances in technological production. From blockchain and artificial intelligence (AI) to gene editing and the Internet of Things (IoT), these advances come with tremendous opportunities for improvement in productivity, efficiency, and human well-being. But as scandals increasingly demonstrate, these advances also introduce new and serious risks of conflict and harm.Technology professionals now face growing demands to identify and mitigate ethical risks to human rights and the environment, as well as to navigate ethical tradeoffs between qualities such as privacy and accuracy, fairness and utility, and safety and accountability. This course provides the tools to identify and manage common ethical risks in the development of emerging data-driven technologies. It distills ethical theory, public regulations, and industry best practices into concrete skills and guidelines needed for the responsible development of digital products and services. By following the course's practical, problems-based approach, learners will become adept at applying theories, principles, frameworks, and techniques in their own roles and organizations. Introduction to Ethics of Emerging Technologies Topic A: What?s at Stake Topic B: Ethics and Why It Matters Topic C: Ethical Decision-Making in Practice Topic D: Causes of Ethical Failures Identifying Ethical Risks Topic A: Ethical Reasons Topic B: Stumbling Blocks for Ethical Reasoning Topic C: Identify Ethical Risks in Product Development Topic D: Tools for Identifying Ethical Risks Topic E: Use Regulations, Standards, and Human Rights to Identify Ethical Risks Ethical Reasoning in Practice Topic A: Ethical Theories Topic B: Use Ethical Decision-Making Frameworks Topic C: Select Options for Action Topic D: Avoid Problems in Ethical Decision-Making Identifying and Mitigating Security Risks Topic A: What Is Security? Topic B: Identify Security Risks Topic C: Security Tradeoffs Topic D: Mitigate Security Risks Identifying and Mitigating Privacy Risks Topic A: What Is Privacy? Topic B: Identify Privacy Risks Topic C: Privacy Tradeoffs Topic D: Mitigate Privacy Risks Identifying and Mitigating Fairness and Bias Risks Topic A: What Are Fairness and Bias? Topic B: Identify Bias Risks Topic C: Fairness Tradeoffs Topic D: Mitigate Bias Risks Identifying and Mitigating Transparency and Explainability Risks Topic A: What Are Transparency and Explainability? Topic B: Identify Transparency and Explainability Risks Topic C: Transparency and Explainability Tradeoffs Topic D: Mitigate Transparency and Explainability Risks Identifying and Mitigating Accountability Risks Topic A: What Is Accountability? Topic B: Identify Accountability Risks Topic C: Accountability Tradeoffs Topic D: Mitigate Accountability Risks Building an Ethical Organization Topic A: What Are Ethical Organizations? Topic B: Organizational Purpose Topic C: Ethics Awareness Topic D: Develop Professional Ethics within Organizations Developing Ethical Systems in Technology-Focused Organizations Topic A: Policy and Compliance Topic B: Metrics and Monitoring Topic C: Communication and Stakeholder Engagement Topic D: Ethical Leadership

ChatGPT, along with other AI tools, aims not to replace the human touch in management, but to enhance it. By addressing repetitive, daily tasks, these tools free up managers to concentrate on core responsibilities like strategic decision-making, team development, and innovation. As we move further into the digital age, integrating tools such as ChatGPT isn't a luxury; it's the future of proactive leadership. In this guide, we'll delve into 10 practical ways through which AI can elevate your efficiency and refine the quality of your work. Gain familiarity with prominent AI tools in the market Efficiently compose and respond to emails Generate concise summaries of complex reports and data. Obtain quick insights, data, and research across varied topics Streamline the writing of articles, training notes, and posts Craft interview tests, form relevant questions, and design checklists for the hiring process 1 Streamlining emails An inbox can be a goldmine of information but also a significant time drain for managers. Here's how to optimise it: Drafting responses: Give the AI a brief, and watch it craft a well-structured response. Sorting and prioritising: By employing user-defined rules and keywords, ChatGPT can flag important emails, ensuring no vital communication slips through the cracks. 2 Efficient report writing Reports, especially routine ones, can be time-intensive. Here's a smarter approach: Automate content: Supply key data points to the AI, and let it weave them into an insightful report. Proofreading: Lean on ChatGPT for grammar checks and consistency, ensuring each report remains crisp and error-free. 3 Rapid research From competitor insights to market trends, research is a pivotal part of management. Data synthesis: Feed raw data to the AI and receive succinct summaries in return. Question-answering: Pose specific questions about a dataset to ChatGPT and extract swift insights without diving deep into the entire content. 4 Reinventing recruitment Hiring can be a lengthy process. Here's how to make it more efficient: Resume screening: Equip the AI to spot keywords and qualifications, ensuring that only the most fitting candidates are shortlisted. Preliminary interviews: Leverage ChatGPT for the initial rounds of interviews by framing critical questions and evaluating the responses. 5 Enhancing training Especially for extensive teams, training can be a monumental task. Here's how ChatGPT can assist: Customised content: Inform the AI of your training goals, and it will draft tailored content suitable for various roles. PowerPoint design: Create visually appealing slide presentations on any topic in minimal time.

Artificial Intelligence (AI) is the most disruptive technology since the internet came onto the scene. AI is transforming every aspect of how we manage projects from developing a business case, to planning the work, managing risk, and tracking performance. Because the technology and market are moving so fast, it can be difficult to know how to start using AI on projects. Generative AI for Project Management will engage you with diverse Generative AI tools to start, plan, and manage either your own project or a generic case study. We will embrace a tool agnostic approach to adopting, integrating, and scaling Generative AI without compromising data or trust. You will have hands-on practice utilizing AI tools to optimize your time and your outcomes. You will be accessing a variety of AI tools requiring you to register for a free account. A computer is required for all traditional classroom deliveries. None At the end of this program, you will be able to: Define essential terms and concepts related to artificial intelligence (AI) Illustrate how prompts facilitate interaction with Generative AI Recognize the capabilities of Large Language Models Craft prompts to develop project origination documents Create prompts to assist in planning a project Develop user stories with Generative AI Analyze project performance using Generative AI Identify the limitations of Generative AI Identify the risks associated with using Generative AI Articulate the need for governance and ethics when establishing an AI program in an organization Course Overview Getting Started Foundation Concepts Understanding essential terms and concepts related to AI Exploring various Generative AI Models Understanding Prompts Creating Prompts for Project Startup Prompts for starting a project Prompts for planning a project Best Practices for prompt engineering Creating Prompts for Managing Projects Creating agile user stories Measuring project performance Analyzing a schedule Using Generative AI Responsibly Limitations of AI Models Establishing an AI governance framework Future trends and next steps Summary and Next Steps