1504 Courses

Program Management Skills: In-House Training Program managers coordinate and give oversight to the efforts of marketing groups, project teams, product delivery, maintenance and support, operations and staff from various functional groups, including suppliers, business partners, and other external bodies. The goal is to ensure that proposed business transformation, through the delivery of complex products and processes, is implemented to realize the organization's strategic benefits and objectives, for which the program was selected. The goals of this course are twofold: To provide participants with key program management principles and techniques, recognized as best practices, to enable more effective program management; and to leverage core elements of the program management life cycle, processes, tools and techniques, to enable program management effectiveness. The participant will learn and apply the principles of program management through discussions, activities, and case study exercises. What You Will Learn At the end of this workshop, you will be able to: Maximize the transformational impact of a program according to the business needs Explain management principles and techniques and apply them within a program context Implement program governance and organization that will produce expected benefits Plan for and manage benefit realization, risks, issues, and quality Manage component projects' interdependencies that are linked to both program and strategic objectives Engage program stakeholders effectively. Improve communication and action planning effectiveness for programs in organizations Getting Started Introductions Course structure Course goals and objectives Foundation Concepts Fundamental definitions and concepts Program challenges and benefits Program best practices and success criterion Stakeholder management Governance: program management office and program boards Standard for Program Management overview Vision, Leadership, and the Business Case What is vision, why, and how? Leadership vs. Management Program business case Program Organization and Governance Program organization Program governance Program board roles and responsibilities Benefits Management Benefits explored Benefits management Benefits realization Program Management Planning Program management plan Program blueprint and roadmap Program component dossier Program tranches Program estimating Program scheduling Program Monitoring and Controlling Program Control - An Overview Program Monitoring and Controlling Monitoring and Controlling Transition Program Risk and Issue Management Risk and issue management overview Program risk management Program issue management Program Quality Management Program quality management overview Program quality management principles Program Stakeholder Management Stakeholder engagement overview Stakeholder engagement planning EI, trust, communication and stakeholder engagement Program Closure and Benefits Sustainment Program closure overview Closing the program Program benefits sustainment Summary What did we learn, and how can we implement this in our work environments?

Management of Risk (M_o_R®) Foundation This M_o_R® Foundation course prepares learners to demonstrate knowledge and comprehension of the four elements of the M_o_R framework: Principles, Approach, Processes, Embedding and Reviewing and how these elements support corporate governance. The M_o_R Foundation Course is also a prerequisite for the M_o_R Practitioner qualification. What you will Learn At the end of the M_o_R Foundation course, participants will gain competencies in and be able to: Describe the key characteristics of risk and the benefits of risk management List the eight M_o_R Principles List and describe the use of the key M_o_R Approach documents Create Probability and Impact scales Define and distinguish between risks and issues Create a Risk Register Create a Stakeholder map Identify the key roles in risk management Use the key techniques and describe specialisms in risk management Undertake the M_o_R Foundation examination Introduction Introduction to the M_o_R course What is a risk? What is risk management? Why is risk management so important? Basic risk definitions The development of knowledge about risk management Corporate governance and internal control Where and when should risk management be applied? M_o_R Principles The purpose of M_o_R principles Aligns with objectives Fits the context Engages stakeholders Provides clear guidance Informs decision-making Facilitates continual improvement Creates a supportive culture Achieves measurable value Risk management maturity models M_o_R Approach Relationship between the documents Risk management policy Risk management process guide Risk management strategy Risk register Issue register Risk response plan Risk improvement plan Risk communications plan M_o_R Process Common process barriers Identify contexts Identify the risks Assess estimate Assess evaluate Plan Implement Communication throughout the process M_o_R Perspectives Strategic perspective Program perspective Project perspective Operational perspective Risk Specialisms Business continuity management Incident and crisis management Health and Safety management Financial risk management Environmental risk management Reputational risk management Contract risk management

Management of Risk (M_o_R®) Foundation: In-House Training This M_o_R® Foundation course prepares learners to demonstrate knowledge and comprehension of the four elements of the M_o_R framework: Principles, Approach, Processes, Embedding and Reviewing and how these elements support corporate governance. The M_o_R Foundation Course is also a prerequisite for the M_o_R Practitioner qualification. What you will Learn At the end of the M_o_R Foundation course, participants will gain competencies in and be able to: Describe the key characteristics of risk and the benefits of risk management List the eight M_o_R Principles List and describe the use of the key M_o_R Approach documents Create Probability and Impact scales Define and distinguish between risks and issues Create a Risk Register Create a Stakeholder map Identify the key roles in risk management Use the key techniques and describe specialisms in risk management Undertake the M_o_R Foundation examination Introduction Introduction to the M_o_R course What is a risk? What is risk management? Why is risk management so important? Basic risk definitions The development of knowledge about risk management Corporate governance and internal control Where and when should risk management be applied? M_o_R Principles The purpose of M_o_R principles Aligns with objectives Fits the context Engages stakeholders Provides clear guidance Informs decision-making Facilitates continual improvement Creates a supportive culture Achieves measurable value Risk management maturity models M_o_R Approach Relationship between the documents Risk management policy Risk management process guide Risk management strategy Risk register Issue register Risk response plan Risk improvement plan Risk communications plan M_o_R Process Common process barriers Identify contexts Identify the risks Assess estimate Assess evaluate Plan Implement Communication throughout the process M_o_R Perspectives Strategic perspective Program perspective Project perspective Operational perspective Risk Specialisms Business continuity management Incident and crisis management Health and Safety management Financial risk management Environmental risk management Reputational risk management Contract risk management

Are you a graduate or an industry professional ready to take your career to the next level, develop a new specialism, or see your film idea or project realised? Our MA in Screen and Film Production could enable you to advance your career, develop and enhance your skillset, and align yourself with top industry professionals. Through research-based practice this new MA puts you and your aspirations at the centre of your learning, whilst refining your chosen specialist pathway. 

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)

Management of Value (MoV®) Practitioner This interactive MoV® Practitioner course provides a modular and case-study-driven approach to learning Management of Value (MoV). The core knowledge is structured and comprehensive; and well-rounded modules cover the methodology and various techniques. A case study is used to help appreciate the relevance of MoV in its practical application. What you will Learn The MoV Practitioner Course prepares you for the MoV Practitioner exam. Individuals certified at the MoV Practitioner level will be able to: Apply Management of Value (MoV) principles, processes and techniques, and advocate the benefits of this application appropriately to the senior Management. Develop a plan of MoV activities for the whole lifecycle of small and large projects and programs. Plan an MoV study, tailoring it to particular projects or programs and developing practical study or workshop handbooks as required. Understand and articulate value in relation to organizational objectives. Prioritize value drivers using function analysis and use these to demonstrate how value might be improved. Quantify monetary and non-monetary value using the Value Index, Value Metrics and the Value for Money ratio. Describe and comment on the application of various techniques relevant to MoV. Monitor improvements in value realized throughout a project lifecycle and capture learning which can be transferred to future projects. Offer suggestions and guidance about embedding MoV into an organization, including policy issues, undertake a health check, assess maturity and competence, and provide guidance on typical roles and responsibilities. Understand and articulate the use of MoV within other Best Management Practice methods and its contributions to them Benefits of Taking This Course Upon successful completion of this course, you will be able to: Organise and contribute constructively to a Management of Value (MoV) Study Demonstrate a knowledge of MoV principles, processes, approach, and environment Analyse a company, programme or project to establish its organisational value includes identification and weighting of Value Drivers Pass the AXELOS Practitioner Examination Function Analysis Customer FAST Diagram Value Tree Development Weighting Attributes Paired Comparisons Developing a Value Profile Developing a Value Index Value for Money Ratio Stimulating Innovation Value Engineering Option Evaluation and Selection Evaluation Matrix Value and Value for Money Timing and Planning Teams and Stakeholders MoV in the Organization Integrating with Best Management Practice Relationship between Process and Approach

Lean Six Sigma Green Belt Certification Program This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. Green Belt is not just a role, it is also a competency required for leadership positions at many top companies. This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. With a real-world project focus, the series will teach the fundamental methodology, tools, and techniques of the Define, Measure, Analyze, Improve and Control Process Improvement Methodology. This course is delivered through sixteen 3-hour online sessions. What you Will Learn At the end of this program, you will be able to: Identify strategies for effectively leading high performing process improvement teams Analyze whether projects align with business strategy Apply process improvement methodologies to DMAIC steps, based on real world scenarios Explain ways to appropriately respond to process variation Distinguish among best practice problem solving methodologies Evaluate and effectively communicate data-driven decisions, based on real world scenarios Introduction Lean Six Sigma & quality The vision The methodologies The metric Project Selection Why Projects Random idea generation Targeted idea generation CTQs (Critical to Quality) & projects Project screening criteria Quick improvements Introduction to Define Project Planning Developing the core charter Developing a project charter Facilitation Process Management Business process management Top-down process mapping Voice of the Customer Voice of Customer Stakeholder analysis Communication planning Kicking off the project Define Summary Introduction to Measure Data Collection Fact-based decision making Data sampling Operations definitions Data collection plan Measurement system analysis Graphical Statistics for Continuous Data Meet Six SigmaXL Graphical & statistical tools Data stratification Graphical Statistics for Discrete Data Pareto analysis Dot plots Plotting data over time: Looking for patterns Variation Concepts Variation is reality Special Cause and Common Cause variation Example of standard business reporting Individuals Control Chart Process Capability Genesis of process capability Calculating the metrics of Six Sigma Yield metrics: Measuring process efficiency Cost of Poor Quality The Cost of Poor Quality (COPQ) Cost of Quality categories Calculating the Cost of Poor Quality Measure Summary Introduction to Analyze Process Analysis Introduction to process analysis Value-added analysis Cycle time analysis WIP & pull systems Analyzing bottlenecks and constraints Cause & Effect Analysis Fishbone/Ishikawa diagram 5-Whys analysis Graphical & statistical tools Advanced Analysis Why use hypothesis rests? Hypothesis tests Correlation and regression analysis Analyze Summary Introduction to Improve Solutions Creativity techniques Generating alternative solutions Solution selection techniques Introduction to Design of Experiments Introduction to DOE DOE activity Error Proofing Failure mode & effect analysis Poka-Yoke Project Management Fundamentals Successful teams Project roles Conflict management Standardization Standardization The Visual Workplace 5S Piloting & Verifying Results What is a pilot? Evaluating results Improve Summary Introduction to Control Statistical Process Control Review of Special & Common Cause variation Review of Individual Control Chart P-Chart for discrete proportion data Transition Planning Control plan Project closure Control Summary Summary and Next Steps

Lean Six Sigma Green Belt Certification Program: In-House Training This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. Green Belt is not just a role, it is also a competency required for leadership positions at many top companies. This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. With a real-world project focus, the series will teach the fundamental methodology, tools, and techniques of the Define, Measure, Analyze, Improve and Control Process Improvement Methodology. This course is delivered through sixteen 3-hour online sessions. What you Will Learn At the end of this program, you will be able to: Identify strategies for effectively leading high performing process improvement teams Analyze whether projects align with business strategy Apply process improvement methodologies to DMAIC steps, based on real world scenarios Explain ways to appropriately respond to process variation Distinguish among best practice problem solving methodologies Evaluate and effectively communicate data-driven decisions, based on real world scenarios Introduction Lean Six Sigma & quality The vision The methodologies The metric Project Selection Why Projects Random idea generation Targeted idea generation CTQs (Critical to Quality) & projects Project screening criteria Quick improvements Introduction to Define Project Planning Developing the core charter Developing a project charter Facilitation Process Management Business process management Top-down process mapping Voice of the Customer Voice of Customer Stakeholder analysis Communication planning Kicking off the project Define Summary Introduction to Measure Data Collection Fact-based decision making Data sampling Operations definitions Data collection plan Measurement system analysis Graphical Statistics for Continuous Data Meet Six SigmaXL Graphical & statistical tools Data stratification Graphical Statistics for Discrete Data Pareto analysis Dot plots Plotting data over time: Looking for patterns Variation Concepts Variation is reality Special Cause and Common Cause variation Example of standard business reporting Individuals Control Chart Process Capability Genesis of process capability Calculating the metrics of Six Sigma Yield metrics: Measuring process efficiency Cost of Poor Quality The Cost of Poor Quality (COPQ) Cost of Quality categories Calculating the Cost of Poor Quality Measure Summary Introduction to Analyze Process Analysis Introduction to process analysis Value-added analysis Cycle time analysis WIP & pull systems Analyzing bottlenecks and constraints Cause & Effect Analysis Fishbone/Ishikawa diagram 5-Whys analysis Graphical & statistical tools Advanced Analysis Why use hypothesis rests? Hypothesis tests Correlation and regression analysis Analyze Summary Introduction to Improve Solutions Creativity techniques Generating alternative solutions Solution selection techniques Introduction to Design of Experiments Introduction to DOE DOE activity Error Proofing Failure mode & effect analysis Poka-Yoke Project Management Fundamentals Successful teams Project roles Conflict management Standardization Standardization The Visual Workplace 5S Piloting & Verifying Results What is a pilot? Evaluating results Improve Summary Introduction to Control Statistical Process Control Review of Special & Common Cause variation Review of Individual Control Chart P-Chart for discrete proportion data Transition Planning Control plan Project closure Control Summary Summary and Next Steps

Scrum Master and Product Owner Workshop This workshop builds on the specific roles and responsibilities of the Product Owner and Scrum Master in a Scrum environment, and how they need to work together as part of the Scrum methodology. During these sessions, you will explore who does what before, during, and after the Scrum Sprint cycles, as well as how to make the process work best in your specific Agile environment. You will come away from this workshop with a much deeper understanding of the roles and responsibilities so that individual performance improves on the job. Improved target results include providing focused leadership, making effective decisions, guiding Agile teams, and delivering business value. Foundation Concepts Agile History, Values, and Mindset Introduction to Scrum Scrum Events Scrum Artifacts Scrum Roles and Responsibilities Scrum Roles Product Owner Responsibilities Scrum Master Responsibilities The Scrum Team Responsibilities Cross-functional Teams Product Ownership Product Ownership Vision Understand Your Customers and Market Stakeholder Management and Engagement Product Backlog What is a User Story? Epics and User Stories Acceptance Criteria Preparing User Stories for a Sprint Definition of Ready (DoR) and Definition of Done (DoD) User Story Estimation Using Planning Poker Backlog Grooming Roadmaps, Story Maps, Impact Mapping Product Backlog Prioritization, MoSCoW, Kano Analysis Technical Debt The Sprint Team Capacity and Velocity Planning Sprint Planning Meeting and Sprint Plan The Sprint: Learning to Become Self-managing, Self-organizing, Self-improving Sprint Review Meeting Retrospectives Project Progress and Completion The Daily Scrum The Task Board and The Burndown Chart Information Radiators Closing a Scrum Project Summary and Next Steps Review of course goals, objectives, and content