121 Statistics courses in Shaw delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Sage 50 Training: Overview Starting our Sage 50 Accounting courses will enhance your career potentials and give you the skills and knowledge you need to get started in Finance and Accountancy Industry. In Addition, our courses are designed to comply with AAT and Sage certification exams. Why wait, start a new direction to your career in Accountancy. According to statistics, the average salary for Accountants is over £50,000 (Source: Reed Salary Checker). In this sector, the employability rate is higher than in any other sector. Professional or Industry specific qualification

Sage 50 Courses: Overview Starting our Sage 50 Accounting courses will enhance your career potentials and give you the skills and knowledge you need to get started in Finance and Accountancy Industry. In Addition, our courses are designed to comply with AAT and Sage certification exams. Why wait, start a new direction to your career in Accountancy. According to statistics, the average salary for Accountants is over £50,000 (Source: Reed Salary Checker). In this sector, the employability rate is higher than in any other sector. Professional or Industry specific qualification

Mental Health First Aid England Aware is an introductory course designed to increase mental health awareness and give an understanding of how to look after wellbeing and challenge stigma. Through an interactive instructor-led live session, you will learn: What mental health is and how to challenge stigma An introduction to some common mental health issues Confidence to support someone who may be experiencing mental ill health Ways to look after your own mental health and support wellbeing Outline What is mental health? Mental Health Continuum Factors that affect mental health Stigma Stress and stress management Spotting signs of distress Mental health conditions:DepressionAnxiety disordersPsychosisEating disordersSuicideSelf-harm Recovery Take 10 Together - starting a supportive conversation Supporting mental health in the workplace Useful statistics Helpful resources

Duration 4 Days 24 CPD hours This course is intended for The Microsoft Security Operations Analyst collaborates with organizational stakeholders to secure information technology systems for the organization. Their goal is to reduce organizational risk by rapidly remediating active attacks in the environment, advising on improvements to threat protection practices, and referring violations of organizational policies to appropriate stakeholders. Responsibilities include threat management, monitoring, and response by using a variety of security solutions across their environment. The role primarily investigates, responds to, and hunts for threats using Microsoft Sentinel, Microsoft Defender for Cloud, Microsoft 365 Defender, and third-party security products. Since the Security Operations Analyst consumes the operational output of these tools, they are also a critical stakeholder in the configuration and deployment of these technologies. Learn how to investigate, respond to, and hunt for threats using Microsoft Sentinel, Microsoft Defender for Cloud, and Microsoft 365 Defender. In this course you will learn how to mitigate cyberthreats using these technologies. Specifically, you will configure and use Microsoft Sentinel as well as utilize Kusto Query Language (KQL) to perform detection, analysis, and reporting. The course was designed for people who work in a Security Operations job role and helps learners prepare for the exam SC-200: Microsoft Security Operations Analyst. Prerequisites Basic understanding of Microsoft 365 Fundamental understanding of Microsoft security, compliance, and identity products Intermediate understanding of Windows 10 Familiarity with Azure services, specifically Azure SQL Database and Azure Storage Familiarity with Azure virtual machines and virtual networking Basic understanding of scripting concepts. 1 - Introduction to Microsoft 365 threat protection Explore Extended Detection & Response (XDR) response use cases Understand Microsoft Defender XDR in a Security Operations Center (SOC) Explore Microsoft Security Graph Investigate security incidents in Microsoft Defender XDR 2 - Mitigate incidents using Microsoft 365 Defender Use the Microsoft Defender portal Manage incidents Investigate incidents Manage and investigate alerts Manage automated investigations Use the action center Explore advanced hunting Investigate Microsoft Entra sign-in logs Understand Microsoft Secure Score Analyze threat analytics Analyze reports Configure the Microsoft Defender portal 3 - Protect your identities with Microsoft Entra ID Protection Microsoft Entra ID Protection overview Detect risks with Microsoft Entra ID Protection policies Investigate and remediate risks detected by Microsoft Entra ID Protection 4 - Remediate risks with Microsoft Defender for Office 365 Automate, investigate, and remediate Configure, protect, and detect Simulate attacks 5 - Safeguard your environment with Microsoft Defender for Identity Configure Microsoft Defender for Identity sensors Review compromised accounts or data Integrate with other Microsoft tools 6 - Secure your cloud apps and services with Microsoft Defender for Cloud Apps Understand the Defender for Cloud Apps Framework Explore your cloud apps with Cloud Discovery Protect your data and apps with Conditional Access App Control Walk through discovery and access control with Microsoft Defender for Cloud Apps Classify and protect sensitive information Detect Threats 7 - Respond to data loss prevention alerts using Microsoft 365 Describe data loss prevention alerts Investigate data loss prevention alerts in Microsoft Purview Investigate data loss prevention alerts in Microsoft Defender for Cloud Apps 8 - Manage insider risk in Microsoft Purview Insider risk management overview Create and manage insider risk policies Investigate insider risk alerts Take action on insider risk alerts through cases Manage insider risk management forensic evidence Create insider risk management notice templates 9 - Investigate threats by using audit features in Microsoft Defender XDR and Microsoft Purview Standard Explore Microsoft Purview Audit solutions Implement Microsoft Purview Audit (Standard) Start recording activity in the Unified Audit Log Search the Unified Audit Log (UAL) Export, configure, and view audit log records Use audit log searching to investigate common support issues 10 - Investigate threats using audit in Microsoft Defender XDR and Microsoft Purview (Premium) Explore Microsoft Purview Audit (Premium) Implement Microsoft Purview Audit (Premium) Manage audit log retention policies Investigate compromised email accounts using Purview Audit (Premium) 11 - Investigate threats with Content search in Microsoft Purview Explore Microsoft Purview eDiscovery solutions Create a content search View the search results and statistics Export the search results and search report Configure search permissions filtering Search for and delete email messages 12 - Protect against threats with Microsoft Defender for Endpoint Practice security administration Hunt threats within your network 13 - Deploy the Microsoft Defender for Endpoint environment Create your environment Understand operating systems compatibility and features Onboard devices Manage access Create and manage roles for role-based access control Configure device groups Configure environment advanced features 14 - Implement Windows security enhancements with Microsoft Defender for Endpoint Understand attack surface reduction Enable attack surface reduction rules 15 - Perform device investigations in Microsoft Defender for Endpoint Use the device inventory list Investigate the device Use behavioral blocking Detect devices with device discovery 16 - Perform actions on a device using Microsoft Defender for Endpoint Explain device actions Run Microsoft Defender antivirus scan on devices Collect investigation package from devices Initiate live response session 17 - Perform evidence and entities investigations using Microsoft Defender for Endpoint Investigate a file Investigate a user account Investigate an IP address Investigate a domain 18 - Configure and manage automation using Microsoft Defender for Endpoint Configure advanced features Manage automation upload and folder settings Configure automated investigation and remediation capabilities Block at risk devices 19 - Configure for alerts and detections in Microsoft Defender for Endpoint Configure advanced features Configure alert notifications Manage alert suppression Manage indicators 20 - Utilize Vulnerability Management in Microsoft Defender for Endpoint Understand vulnerability management Explore vulnerabilities on your devices Manage remediation 21 - Plan for cloud workload protections using Microsoft Defender for Cloud Explain Microsoft Defender for Cloud Describe Microsoft Defender for Cloud workload protections Enable Microsoft Defender for Cloud 22 - Connect Azure assets to Microsoft Defender for Cloud Explore and manage your resources with asset inventory Configure auto provisioning Manual log analytics agent provisioning 23 - Connect non-Azure resources to Microsoft Defender for Cloud Protect non-Azure resources Connect non-Azure machines Connect your AWS accounts Connect your GCP accounts 24 - Manage your cloud security posture management? Explore Secure Score Explore Recommendations Measure and enforce regulatory compliance Understand Workbooks 25 - Explain cloud workload protections in Microsoft Defender for Cloud Understand Microsoft Defender for servers Understand Microsoft Defender for App Service Understand Microsoft Defender for Storage Understand Microsoft Defender for SQL Understand Microsoft Defender for open-source databases Understand Microsoft Defender for Key Vault Understand Microsoft Defender for Resource Manager Understand Microsoft Defender for DNS Understand Microsoft Defender for Containers Understand Microsoft Defender additional protections 26 - Remediate security alerts using Microsoft Defender for Cloud Understand security alerts Remediate alerts and automate responses Suppress alerts from Defender for Cloud Generate threat intelligence reports Respond to alerts from Azure resources 27 - Construct KQL statements for Microsoft Sentinel Understand the Kusto Query Language statement structure Use the search operator Use the where operator Use the let statement Use the extend operator Use the order by operator Use the project operators 28 - Analyze query results using KQL Use the summarize operator Use the summarize operator to filter results Use the summarize operator to prepare data Use the render operator to create visualizations 29 - Build multi-table statements using KQL Use the union operator Use the join operator 30 - Work with data in Microsoft Sentinel using Kusto Query Language Extract data from unstructured string fields Extract data from structured string data Integrate external data Create parsers with functions 31 - Introduction to Microsoft Sentinel What is Microsoft Sentinel? How Microsoft Sentinel works When to use Microsoft Sentinel 32 - Create and manage Microsoft Sentinel workspaces Plan for the Microsoft Sentinel workspace Create a Microsoft Sentinel workspace Manage workspaces across tenants using Azure Lighthouse Understand Microsoft Sentinel permissions and roles Manage Microsoft Sentinel settings Configure logs 33 - Query logs in Microsoft Sentinel Query logs in the logs page Understand Microsoft Sentinel tables Understand common tables Understand Microsoft Defender XDR tables 34 - Use watchlists in Microsoft Sentinel Plan for watchlists Create a watchlist Manage watchlists 35 - Utilize threat intelligence in Microsoft Sentinel Define threat intelligence Manage your threat indicators View your threat indicators with KQL 36 - Connect data to Microsoft Sentinel using data connectors Ingest log data with data connectors Understand data connector providers View connected hosts 37 - Connect Microsoft services to Microsoft Sentinel Plan for Microsoft services connectors Connect the Microsoft Office 365 connector Connect the Microsoft Entra connector Connect the Microsoft Entra ID Protection connector Connect the Azure Activity connector 38 - Connect Microsoft Defender XDR to Microsoft Sentinel Plan for Microsoft Defender XDR connectors Connect the Microsoft Defender XDR connector Connect Microsoft Defender for Cloud connector Connect Microsoft Defender for IoT Connect Microsoft Defender legacy connectors 39 - Connect Windows hosts to Microsoft Sentinel Plan for Windows hosts security events connector Connect using the Windows Security Events via AMA Connector Connect using the Security Events via Legacy Agent Connector Collect Sysmon event logs 40 - Connect Common Event Format logs to Microsoft Sentinel Plan for Common Event Format connector Connect your external solution using the Common Event Format connector 41 - Connect syslog data sources to Microsoft Sentinel Plan for syslog data collection Collect data from Linux-based sources using syslog Configure the Data Collection Rule for Syslog Data Sources Parse syslog data with KQL 42 - Connect threat indicators to Microsoft Sentinel Plan for threat intelligence connectors Connect the threat intelligence TAXII connector Connect the threat intelligence platforms connector View your threat indicators with KQL 43 - Threat detection with Microsoft Sentinel analytics What is Microsoft Sentinel Analytics? Types of analytics rules Create an analytics rule from templates Create an analytics rule from wizard Manage analytics rules 44 - Automation in Microsoft Sentinel Understand automation options Create automation rules 45 - Threat response with Microsoft Sentinel playbooks What are Microsoft Sentinel playbooks? Trigger a playbook in real-time Run playbooks on demand 46 - Security incident management in Microsoft Sentinel Understand incidents Incident evidence and entities Incident management 47 - Identify threats with Behavioral Analytics Understand behavioral analytics Explore entities Display entity behavior information Use Anomaly detection analytical rule templates 48 - Data normalization in Microsoft Sentinel Understand data normalization Use ASIM Parsers Understand parameterized KQL functions Create an ASIM Parser Configure Azure Monitor Data Collection Rules 49 - Query, visualize, and monitor data in Microsoft Sentinel Monitor and visualize data Query data using Kusto Query Language Use default Microsoft Sentinel Workbooks Create a new Microsoft Sentinel Workbook 50 - Manage content in Microsoft Sentinel Use solutions from the content hub Use repositories for deployment 51 - Explain threat hunting concepts in Microsoft Sentinel Understand cybersecurity threat hunts Develop a hypothesis Explore MITRE ATT&CK 52 - Threat hunting with Microsoft Sentinel Explore creation and management of threat-hunting queries Save key findings with bookmarks Observe threats over time with livestream 53 - Use Search jobs in Microsoft Sentinel Hunt with a Search Job Restore historical data 54 - Hunt for threats using notebooks in Microsoft Sentinel Access Azure Sentinel data with external tools Hunt with notebooks Create a notebook Explore notebook code

Duration 2 Days 12 CPD hours This course is intended for This course is intended for network administrators, operators, and engineers responsible for managing the normal day-to-day operation and administration of a BIG-IP application delivery network. This course presents the prerequisite knowledge for many other of F5's BIG-IP instructor-led training courses. Overview Getting started with the BIG-IP system Traffic processing with BIG-IP Local Traffic Manager (LTM) Using the TMSH (TMOS Shell) command line interface Using NATs and SNATs Monitoring application health and managing object status Modifying traffic behavior with profiles, including SSL offload and re-encryption Modifying traffic behavior with persistence, including source address affinity and cookie persistence Troubleshooting the BIG-IP system, including logging (local, high-speed, and legacy remote logging), and using tcpdump User roles and administrative partitions vCMP concepts Customizing application delivery with iRules This course gives network administrators, network operators, and network engineers a functional understanding of the BIG-IP system as it is commonly deployed in an application delivery network. The course introduces students to the BIG-IP system, its configuration objects, how it processes traffic, and how typical administrative and operational activities are performed. The course includes lecture, hands-on labs, interactive demonstrations, and discussions. Setting Up the BIG-IP System Introducing the BIG-IP System Initially Setting Up the BIG-IP System Configuring the Management Interface Activating the Software License Provisioning Modules and Resources Importing a Device Certificate Specifying BIG-IP Platform Properties Configuring the Network Configuring Network Time Protocol (NTP) Servers Configuring Domain Name System (DNS) Settings Configuring High Availability Options Archiving the BIG-IP Configuration Leveraging F5 Support Resources and Tools Traffic Processing Building Blocks Identifying BIG-IP Traffic Processing Objects Configuring Virtual Servers and Pools Load Balancing Traffic Viewing Module Statistics and Logs Using the Traffic Management Shell (TMSH) Understanding the TMSH Hierarchical Structure Navigating the TMSH Hierarchy Managing BIG-IP Configuration State and Files BIG-IP System Configuration State Loading and Saving the System Configuration Shutting Down and Restarting the BIG-IP System Saving and Replicating Configuration Data (UCS and SCF) Using NATs and SNATs Address Translation on the BIG-IP System Mapping IP Addresses with NATs Solving Routing Issues with SNATs Configuring SNAT Auto Map on a Virtual Server Monitoring for and Mitigating Port Exhaustion Monitoring Application Health Introducing Monitors Types of Monitors Monitor Interval and Timeout Settings Configuring Monitors Assigning Monitors to Resources Managing Pool, Pool Member, and Node Status Using the Network Map Modifying Traffic Behavior with Profiles Introducing Profiles Understanding Profile Types and Dependencies Configuring and Assigning Profiles Introducing SSL Offload and SSL Re-Encryption Managing Object State Modifying Traffic Behavior with Persistence Understanding the Need for Persistence Introducing Source Address Affinity Persistence Managing Object State Administering the BIG-IP System Configuring Logging Legacy Remote Logging Introducing High Speed Logging (HSL) High-Speed Logging Filters HSL Configuration Objects Configuring High Speed Logging Using TCPDUMP on the BIG-IP System Leveraging the BIG-IP iHealth System Viewing BIG-IP System Statistics Defining User Roles and Administrative Partitions Leveraging vCMP Configuring High Availability Introducing Device Service Clustering (DSC) Preparing to Deploy a DSC Configuration Configuring DSC Communication Settings Establishing Device Trust Establishing a Sync-Failover Device Group Synchronizing Configuration Data Exploring Traffic Group Behavior Understanding Failover Managers and Triggers Achieving Stateful Failover with Mirroring

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

A high-impact programme designed to be fun and to get people fully involved. The first-class, jargon-free content is based on what people need to know in practice, not off-putting legal language. This introductory course covers: Introducing Working Safely: Accidents can happen to anyone. The realities of the human suffering behind the statistics. The importance of personal responsibility. Defining hazard and risk: Focusing on the six broad hazard groups, participants are asked to think about the hazards and risks they come across in their own work. 'Risk assessment' demystified. Identifying common hazards: All the main issues - aggression and violence, asbestos, bullying, chemicals and harmful substances, computer workstations, confined spaces, drugs and alcohol, electricity, fire, getting in and out, height, housekeeping, lighting, manual handling, noise, personal hygiene, plant and machinery, slips and trips, stress, temperature, vehicles and transport, and welfare facilities. Improving safety performance: Bridging the gap between management and workforce, encouraging participants to play their part. Also covered: contract work, inspections, safe systems and permits, protective equipment, signage, emergency procedures, reporting and health checks.

The course is relevant to anyone requiring an understanding of the use of Agile or looking to adopt it. This includes, but is not limited to, organisational leaders and managers, marketing executives and managers, and/or all professionals working in an Agile environment, including software sesters, developers, business analysts, UX designers, project management office (PMO), project support and project coordinators.

Description Our comprehensive course combines " Accidents, Incidents, and Breakdowns and Vehicle Marshal and Banksman Training combines 2 aspects: Road safety measures to handle accidents, incidents, and breakdowns and essentials of critical health and safety responsibilities. Accidents, Incidents, and Breakdowns Training Content: Recognizing risks and potential incidents, including breakdowns, collisions, and other scenarios. Duty of care, taking preventative measures, and handling incidents. Understanding health and safety regulations and related statistics. Prevention strategies and appropriate responses in case of an incident. Personal safety and actions to be taken during a vehicle collision. Steps to follow when facing a breakdown, including monitoring gauges and risk assessments. Ensuring the safety of other road users and cooperating with emergency services and recovery operators. Handling bridge strikes, including prevention and actions in case of an incident. Vehicle Marshal and Banksman Learn critical health and safety responsibilities Maneuvering vehicles safely during loading and unloading operations The safe use of work equipment Best practices for guiding vehicles as a Banksman Join us to enhance your knowledge of road safety preparedness. Register today to ensure your drivers are well-versed in the rules of the road and equipped to handle unexpected challenges on their journeys. Please review our Terms and Conditions for more information.