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My Virtual Surgery - Coronary Masterclass A Dry-Lab Masterclass in Coronary Anastomosis A NEW LSOC Course! Led by My Virtual Surgery Founder, and Internationally - renowned coronary surgeon, Professor Paul Sergeant, and expert senior Cardiac Surgeon, Mr Alex Shipolini. This course provides a comprehensive masterclass in coronary anastomosis, featuring innovative anastomotic techniques, specialised surgical ergonomics, and expert guidance. PLEASE REVIEW THE IMPORTANT INFORMATION BELOW BEFORE PURCHASING YOUR TICKET (including regarding pre-course learning). When: Saturday, 9th August 2025. Where: St.Bartholomew's Hospital, London. Required Equipment: Needle holder and forceps for pre-course learning (see below). Ticket Types: Course Only This will provide access to the elements detailed below. This does NOT include the costs of instruments. Please purchase this ticket if you have access to a set of needle holders and thin-tipped forceps at home (ideally a Castroviejo needle holder and Geralds/Ring tip forceps). Course + Instruments This will provide course access + procurement and shipping of a Castroviejo needle holder and Geralds/Ring tip forceps, for you to keep. Please purchase this ticket if you would also like to purchase instruments. Cost includes the course cost, instrument costs, inc. shipping, processing and handling fees. All Tickets include: Access to pre-course training with the MyVirtualSurgery Programme. All day face-to-face seminar on coronary artery surgery from coronary experts. Lifetime access to MyVirtualAnastamosis - the MVS Coronary Anastomosis platform, including direct feedback at home from Prof Sergeant, and ongoing virtual learning. Additionally included in the Course and Instrument Ticket: Procurement of Castroviejo Needle Holder and Forceps to support home training. Sales Open: 22nd June, 7pm Please note: Mandatory Pre-Course Learning Pre-Requisites Pre - Course Learning Deadline All participants must complete Mandatory Pre-Course Learning before the course begins. To ensure we can prepare adequately for the course, including confirming the number of attendees who have completed the pre-course training, a fixed deadline of Monday 28th July will be set for all participants to finish the pre-course training. Not completing the pre-course learning by Monday, 28th July, will lead to forfeiting your place in the course, and no refunds will be issued. Pre-Course Learning Instruments This pre-course learning requires access to a needle holder and thin-tipped forceps. We strongly recommend using a Castroviejo needle holder along with either Geralds or Ring Tips forceps, as this platform is designed for these instruments, and alternatives may not provide as comprehensive training. Furthermore, these are essential instruments for cardiac surgery and will be used during the course. These instruments will also be required for the remainder of the steps on the platform. If you cannot independently acquire a needle holder and forceps, LSOC can purchase a Castroviejo needle holder and a pair of forceps on your behalf and send them directly to you. In this case, please make sure to purchase a Course + Instruments Ticket, to include the cost of these instruments. We look forward to you joining us on our newest course soon!

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: 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)

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)

Salsa or Latin Dance rhythm private class in London for couples or individuals to achieve a good knowledge of the Salsa, Samba, Bachata, Merengue or Latin dance steps One to One lesson, 5 minimum pack lesson booking. Taught by native instructor You can have this lesson at our premises, on Zoom, Pre-recorded or home visit, the choice is yours!!! The Program includes: 10 or 20 Lesson 1on1 lesson Walking & Leading techniques Female & Male Teachers Footwork & Upper body techniques Steps & routines names (brain method) Body posture and language Tempo & beat music technique Fully equipped dance studio Ownership of your footage work progress & Docs Music library via dropbox or Spotify Salsa or Latin Dance rhythm private class in London for couples or individuals to achieve a good knowledge of the salsa, samba, bachata, merengue or Latin dance steps One to One lesson, 4 minimum pack lesson book Teach by the native instructor We have been training people in salsa in London for almost 20 years and are still in business as salsa in west London is increasing massively.  SALE

This two day programme is designed to support participants to work through the technical steps and stages as well as the more practical realities of project management.

Introduction to Design Thinking Innovation is the cornerstone of highly successful companies, especially those that continue to be successful over the years and decades. Design thinking practices fuel this continual innovation, as they are the critical links from inspiration to delivery, concept to showroom floor, and start-up to global business. Design thinking is a structured approach to promoting innovation and creative problem-solving. It is not a new approach. It has been around for centuries, as the art, architecture, and inventions of mankind illustrate. By examining the steps to achieving great design and maximum utility of product, design thinking approaches provide a framework in which to develop new solutions to problems and new products to sell. This highly interactive course is designed to help participants think like designers to generate innovation, and to help teams to produce more innovation and creativity. Since design thinking is based on doing rather than thinking, we participants are challenged to apply the techniques, in the classroom, to create new ideas and solutions to a case study project. What you will Learn At the end of this program, you will be able to: Explain the underlying principles and value of using Design Thinking for innovation Describe the basic concepts of the Stanford Model for Design Thinking Evaluate a set of basic Design Thinking techniques for application to your projects Apply tools, techniques, and skills aligned with the 5 stages of the Stanford Model Drive innovation through Design Thinking at some level in your work environment Foundation Concepts Problems and solutions The Design Thinking difference Design Thinking skills and abilities Design Thinking mindset Design Thinking frameworks Stages of Design Thinking Problems and solutions The Design Thinking difference Design Thinking skills and abilities Design Thinking mindset Design Thinking frameworks General Practices Team formation Visualization Improvisation Personalization Empathize Practices Overview of Empathize techniques Observation Engagement Interviews Define Practices Overview of Define practices Unpacking techniques Defining the customer techniques Integrating the Define experience Ideate Practices Overview of Ideate practices Reusable techniques for the Ideate stage New Ideate techniques to explore Prototype & Test Practices Overview of Prototype practices Examples of prototypes Overview of Testing practices Forms of testing techniques Adopt and Adapt Design Thinking Overview of Design Thinking implementation Design Thinking implementation challenges Success in implementing Design Thinking Summary and Next Steps Workshop summary Next steps: Personal Action Plans

Introduction to Design Thinking: In-House Training Innovation is the cornerstone of highly successful companies, especially those that continue to be successful over the years and decades. Design thinking practices fuel this continual innovation, as they are the critical links from inspiration to delivery, concept to showroom floor, and start-up to global business. Design thinking is a structured approach to promoting innovation and creative problem-solving. It is not a new approach. It has been around for centuries, as the art, architecture, and inventions of mankind illustrate. By examining the steps to achieving great design and maximum utility of product, design thinking approaches provide a framework in which to develop new solutions to problems and new products to sell. This highly interactive course is designed to help participants think like designers to generate innovation, and to help teams to produce more innovation and creativity. Since design thinking is based on doing rather than thinking, we participants are challenged to apply the techniques, in the classroom, to create new ideas and solutions to a case study project. What you will Learn At the end of this program, you will be able to: Explain the underlying principles and value of using Design Thinking for innovation Describe the basic concepts of the Stanford Model for Design Thinking Evaluate a set of basic Design Thinking techniques for application to your projects Apply tools, techniques, and skills aligned with the 5 stages of the Stanford Model Drive innovation through Design Thinking at some level in your work environment Foundation Concepts Problems and solutions The Design Thinking difference Design Thinking skills and abilities Design Thinking mindset Design Thinking frameworks Stages of Design Thinking Problems and solutions The Design Thinking difference Design Thinking skills and abilities Design Thinking mindset Design Thinking frameworks General Practices Team formation Visualization Improvisation Personalization Empathize Practices Overview of Empathize techniques Observation Engagement Interviews Define Practices Overview of Define practices Unpacking techniques Defining the customer techniques Integrating the Define experience Ideate Practices Overview of Ideate practices Reusable techniques for the Ideate stage New Ideate techniques to explore Prototype & Test Practices Overview of Prototype practices Examples of prototypes Overview of Testing practices Forms of testing techniques Adopt and Adapt Design Thinking Overview of Design Thinking implementation Design Thinking implementation challenges Success in implementing Design Thinking Summary and Next Steps Workshop summary Next steps: Personal Action Plans