304 Courses in London

Effective Change Management 1 Day Workshop in Heathrow

Employee Engagement 1 Day Training in Bromley

Employee Engagement 1 Day Training in Heathrow

This one day masterclass is designed to provide a practical application of the content that is covered within The DEI Playbook and is aimed at anyone tasked with launching and implementing diversity and inclusion within their organisation.

Venepuncture and cannulation course Venepuncture training for healthcare professionals Cannulation skills development Intravenous access techniques Blood sample collection training Infection control in venepuncture Hands-on venepuncture practice Cannula insertion training Nursing revalidation hours CPD accredited course Healthcare professional skills development Venepuncture and cannulation procedures Venepuncture certification program Intravenous catheter insertion Best practices in venepuncture Patient assessment for venepuncture Troubleshooting venepuncture complications Venepuncture and cannulation simulation Real-life venepuncture scenarios Healthcare career advancement with venepuncture skills

Gastrostomy tube care Gastrostomy tube management Gastrostomy tube training Enteral feeding care Gastrostomy tube complications Gastrostomy site care Gastrostomy tube insertion Gastrostomy tube feeding techniques Gastrostomy tube nursing Gastrostomy care for healthcare professionals Gastrostomy tube education CPD accredited course Nursing revalidation hours Practical gastrostomy care training Hands-on gastrostomy tube practice Patient education in gastrostomy care Ethical considerations in gastrostomy tube care Cultural sensitivity in gastrostomy care Gastrostomy tube complications prevention High-quality gastrostomy care certification PEG

Business Process Modeling This course is part of IIL's Business Analysis Certificate Program (BACP), a program designed to help prepare individuals pass the IIBA® Certification exam to become a Certified Business Analysis Professional (CBAP®). Learn more at www.iil.com/bacp A process model is a description of a process in terms of its steps or actions, the data flowing between them and participants in the process, machines, systems, and organizations involved. Modeling is a critical business analysis skill. It applies graphical and text communication techniques to describe the actions, objects, and relationships acted upon in the process and the steps that act upon them. This course teaches the technique of process modeling and ties together the core methods of process, behavior, and data modeling to enable business analysts to fully describe business processes in levels of detail from multiple perspectives. What you will Learn Upon completion, participants will be able to: Identify business processes and their components Work with UML diagrams Use process modeling in business diagramming Diagram and model business processes Foundation Concepts The role of the business analyst The IIBA® BABOK® Knowledge Areas Business Process Modeling (BPM) and the business analyst A practical approach to business process modeling The Context for Modeling Business Processes Overview of context for business process modeling Analyzing stakeholder information Modeling best practices Critical inputs for BPM: Business Rules Critical inputs for BPM: Context Diagrams Data Models Overview of data modeling Entity relationship diagrams Object-oriented approach Class diagrams Other data models Process Models - Part I (Non-UML) Overview of process modeling Data flow diagrams Workflow diagrams Flowcharts Process Models - Part II (UML) Overview of UML Process Models UML Activity Diagrams UML Sequence Diagrams Usage Models - Part I (Non-UML) Overview of usage modeling Prototyping options Static prototyping and storyboards Dynamic prototyping User Interface Design and user stories Usage Models - Part II (UML Use Cases) Overview of Use Cases Use Case diagrams Use Case descriptions Use Cases and the product life cycle Integrating the Models Overview of integrating the models General analysis best practices Specific analysis techniques summary Best practices for transition to design Summary and Next Steps What did we learn and how can we implement this in our work environments?

Business Process Modeling: In-House Training This course is part of IIL's Business Analysis Certificate Program (BACP), a program designed to help prepare individuals pass the IIBA® Certification exam to become a Certified Business Analysis Professional (CBAP®). Learn more at www.iil.com/bacp A process model is a description of a process in terms of its steps or actions, the data flowing between them and participants in the process, machines, systems, and organizations involved. Modeling is a critical business analysis skill. It applies graphical and text communication techniques to describe the actions, objects, and relationships acted upon in the process and the steps that act upon them. This course teaches the technique of process modeling and ties together the core methods of process, behavior, and data modeling to enable business analysts to fully describe business processes in levels of detail from multiple perspectives. What you will Learn Upon completion, participants will be able to: Identify business processes and their components Work with UML diagrams Use process modeling in business diagramming Diagram and model business processes Foundation Concepts The role of the business analyst The IIBA® BABOK® Knowledge Areas Business Process Modeling (BPM) and the business analyst A practical approach to business process modeling The Context for Modeling Business Processes Overview of context for business process modeling Analyzing stakeholder information Modeling best practices Critical inputs for BPM: Business Rules Critical inputs for BPM: Context Diagrams Data Models Overview of data modeling Entity relationship diagrams Object-oriented approach Class diagrams Other data models Process Models - Part I (Non-UML) Overview of process modeling Data flow diagrams Workflow diagrams Flowcharts Process Models - Part II (UML) Overview of UML Process Models UML Activity Diagrams UML Sequence Diagrams Usage Models - Part I (Non-UML) Overview of usage modeling Prototyping options Static prototyping and storyboards Dynamic prototyping User Interface Design and user stories Usage Models - Part II (UML Use Cases) Overview of Use Cases Use Case diagrams Use Case descriptions Use Cases and the product life cycle Integrating the Models Overview of integrating the models General analysis best practices Specific analysis techniques summary Best practices for transition to design Summary and Next Steps What did we learn and how can we implement this in our work environments?

Lean Six Sigma Black Belt Certification Program This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

Lean Six Sigma Black Belt Certification Program: In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)