4832 Model courses in Cardiff delivered Online

This beginner to the intermediate-level course on .NET MAUI mobile application development enables you to advance your Visual Studio coding skills and implement interfaces using XAML. Learn to develop and publish Minimal API with .NET Core using event driven development while understanding logic and syntax when working on the .NET environment.

Risk Management for IT Projects: Virtual In-House Training IT projects may have direct bottom-line impact on the organization, cost millions of dollars, cause organizational change and change the way the organization is perceived by clients. Many IT projects are notoriously hard to predict and are filled with risk. IT Risk Management takes a comprehensive look at IT project risk management using PMI's PMBOK® Guide Risk Management Model in the context of IT Project Life Cycle phases. The goal of this course is to arm the practitioner with a rigorous, common-sense approach to addressing uncertainty in projects. This approach includes the ability to influence project outcomes, avoid many potential project risks, and be ready to calmly and efficiently respond to unavoidable challenges. What you will Learn You'll learn how to: Describe the risk management process, using the PMBOK® Guide's standard models and terminology Discuss the potential barriers to managing risk effectively in IT project organizations Develop an effective risk management plan for IT projects Identify project risks using IT-specific, practical tools Analyze individual risk events and overall project risk using IT-specific, practical approaches Plan effective responses to IT-specific risk based on the results of risk analysis and integrate risk responses into project schedules and cost estimates Manage and control risk throughout the IT project life cycle Implement selected elements of IT project risk management on your next project Foundation Concepts Basic concepts and purpose Risk and project constraints Risk and corporate cultures Risk management and IT PLC standards Plan Risk Management for IT Projects Plan Risk management process Plan Risk management activities Design a standard template Assess the project-specific needs Tailor the template Produce a project-specific risk management plan Gain consensus and submit as part of overall project plan A risk management plan of IT projects Identify Risks for IT Projects Identify risk process overview Risk categories and examples Risk identification tools Risk events by project life-cycle phases Perform Risk Analysis for IT Projects Perform qualitative risk analysis overview Core qualitative tools for IT projects Auxiliary qualitative tools for cost and schedule estimates When to use quantitative analysis for IT projects Plan Risk Response for IT Projects Plan risk response overview Active risk response strategies for IT projects (Threat and Opportunity) Acceptance and contingency reserves Contingency planning for IT projects Plan risk responses for IT projects Implement Risk Response for IT Projects Implement Risk Responses Executing Risk Response Plans Techniques and Tools Used Continuous Risk Management Monitor Risks for IT Projects Monitor risks overview Monitor risks tips for IT projects Technical performance measurement systems Risk management implementation for IT projects

Welcome to a hands-on SQL course. In this course, you will learn the fundamentals of SQL databases and the relational model. We will cover core concepts such as inserting data into SQL databases, extracting data from SQL databases, aggregations, subqueries, joins, unions, and window functions. This course will help you write complex queries to perform tasks as required in technical interviews or on the job.

Course Overview Learn how to create a low poly scene in Maya and import it into Unity for mobile game development, with this step-by-step training course. The Modeling Environments for Mobile Games in Maya & Unity 3D course covers everything you need to know to set up and create your project. It will teach you how to import and export the models from Maya to Unity, model the base of the island, add special camera effects, birds and much more. This course is ideal for game developers and 3D artists. By the end of this training, you will be able to set up the scene in Unity 3D to create the incredible mood and atmosphere of the Monument Valley game. You will have a range of in-demand skills required to work across a wide range of creative fields. This best selling Modeling Environments for Mobile Games in Maya & Unity 3D has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Modeling Environments for Mobile Games in Maya & Unity 3D is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Modeling Environments for Mobile Games in Maya & Unity 3D is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Modeling Environments for Mobile Games in Maya & Unity 3D is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Modeling Environments for Mobile Games in Maya & Unity 3D, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Modeling Environments for Mobile Games in Maya & Unity 3D will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Modeling Environments for Mobile Games in Maya & Unity 3D to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

A Solution Circle is a 30 minute creative Problem Solving Process for getting unstuck…Ideal for busy people! lt was designed by Marsha Forest & Jack Pearpoint. This is a short and powerful tool. It is effective in getting “unstuck” from a problem in life or work. Solution Circles are tools of “community capacity”. It assumes and demonstrates that nearby people – in any community or work place have the capacity to help – if asked. It requires a person to ASK – not an easy thing in our culture of privacy and “do it alone”. This tool puts all the values we espouse into practice and demonstrates that TOGETHER WE’RE BETTER. Course Category Problem Solving Description A Solution Circle is a 30 minute creative Problem Solving Process for getting unstuck…Ideal for busy people! lt was designed by Marsha Forest & Jack Pearpoint. This is a short and powerful tool. It is effective in getting “unstuck” from a problem in life or work. Solution Circles are tools of “community capacity”. It assumes and demonstrates that nearby people – in any community or work place have the capacity to help – if asked. It requires a person to ASK – not an easy thing in our culture of privacy and “do it alone”. This tool puts all the values we espouse into practice and demonstrates that TOGETHER WE’RE BETTER. In this training you will learn how to set up and facilitate Solution Circles  Learning Objectives For participants to learn how to facilitate a Solution Circle For participants to understand the underlying rationale and ethos of this way of working For participants to learn the power of team work and problem solving when including challenging individuals Who Is It For? Team Around the Child agencies Parents and Professional working together Families Multi Agency Teams Social workers CAMHS teams Year Managers Primary and secondary staff teams Early Years and School based Practitioners Heads and Deputies SENDCOs Advanced Skills Teachers Primary and secondary teachers Local Authority Support Services Course Content This can be introduced in a twilight, half day or even a full day workshop with individualised coaching. We will provide background context for this approach and will directly model the process with real live issues facing participants. So bring your most challenging concerns to this training. The course answers the questions: What to when you are stuck Can we find an alternative to permanent exclusion or special unit or school placement? Struggling with a child for whom praise is ineffective? Feeling isolated in your responses to a child’s needs? Worried about where your instinctive reactions are leading you? Cannot seem to get any consensus view of a young person’s behaviour/needs amongst all the family and professionals involved – is everyone pulling in different directions? We will cover: Setting the tone Background and detailed teaching of process Inclusion values underpinning this work Graphic and process facilitation skills Processes modelled and opportunities to try process out with coaching Practical setting up of insights and solutions circles Lessons learned

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

Overview Sometimes, it can be difficult to recruit the perfect candidate for the organisation and terminate those who are unable to reach the goals. The Employee Recruitment and Termination Course can make this task less complicated. This course aims to give you a clear understanding of both the recruitment and termination process. This course is divided into easy-to-follow modules. These modules will educate you on the recruitment process model and the method as well. Here, you will learn about the essentials of human resource management. In addition, the course will teach you effective skills for virtual interviewing. Furthermore, you will get the chance to enhance your understanding of the employee termination procedure. By the end of the course, you will have the ability to select the right candidate for your organisation. Start today! Course Preview Learning Outcomes Familiarise yourself with the recruitment process model Deepen your knowledge of the recruitment method Acquire effective human resource management skills Grasp the techniques of virtual interviewing Get introduced to the employee termination procedure Why Take This Course From John Academy? Affordable, well-structured and high-quality e-learning study materials Meticulously crafted engaging and informative tutorial videos and materials Efficient exam systems for the assessment and instant result Earn UK & internationally recognised accredited qualification Easily access the course content on mobile, tablet, or desktop from anywhere, anytime Excellent career advancement opportunities Get 24/7 student support via email What Skills Will You Learn from This Course? Recruitment Virtual interviewing HRM Employee termination Who Should Take This Employee Recruitment and Termination Course? Whether you're an existing practitioner or an aspiring professional, this course is an ideal training opportunity. It will elevate your expertise and boost your CV with key skills and a recognised qualification attesting to your knowledge. Are There Any Entry Requirements? This Employee Recruitment and Termination Course is available to all learners of all academic backgrounds. But learners should be aged 16 or over to undertake the qualification. And a good understanding of the English language, numeracy, and ICT will be helpful. Employee Recruitment and Termination Course Certification After completing and passing the Employee Recruitment and Termination Course successfully, you will be able to obtain a Recognised Certificate of Achievement. Learners can obtain the certificate in hard copy at £14.99 or PDF format at £11.99. Career Path​ This exclusive Employee Recruitment and Termination Course will equip you with effective skills and abilities and help you explore career paths such as  Recruitment Consultant Recruitment Team Leader Recruitment Manager HR Manager Business Owner/Entrepreneur Module 01: Introduction To Recruitment Process Model Introduction to Recruitment Process Model 00:42:00 Module 02: Recruitment Methods Recruitment Method 00:36:00 Module 03: Human Resource Management Human Resource Management 00:43:00 Module 04: Key Skills And Issues In Recruitment Function Key Skills and Issues in Recruitment 00:43:00 Module 05: Virtual Interviewing Virtual Interviewing1 00:34:00 Module 06: Employee On-Boarding Employee Onboarding 00:37:00 Module 07: Introduction To Employee Termination Introduction to The Employee Termination 00:50:00 Module 08: The Employee Termination Procedure Employee Termination Letter and Guide 00:37:00 Module 09: Employee Termination Letter And Guide The Employee Termination Procedure 01:03:00 Order Your Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

This training gives an opportunity to focus on how to embed effective approaches to meeting emotional needs in schools. We explore a range of ways into meeting emotional needs of pupils across a school. We focus on including pupils with extreme emotional needs. Course Category Meeting emotional needs Description This training gives an opportunity to focus on how to embed relevant ideas and materials in schools. We explore how to make these materials really come to life as we explore a range of ways into meeting emotional needs of pupils across a school. We focus on including pupils with extreme emotional needs. There are lots of opportunities for personal and professional reflection on engagement with challenging pupils as we provide processes to support reflection, visioning and problem solving. Testimonials ‘Thank you so much for the work you did with us yesterday – I have since been in 2 schools today and have spoken to an number of other colleagues who were present – all were totally overwhelmed by the session – they loved it.”NOTTINGHAM SEAL COORDINATOR “I was totally blown away, when I realised how passionate people were about SEAL” “So nice to reflect and realise what a long way we have come” “That was so powerful and motivational”.’ Learning Objectives Shared vision of a school that is truly meeting all social and emotional needs Access to a wider range of practical strategies to impact on meeting emotional and behaviour problems Deeper understanding of how to embed positive Mental Health approaches in a school Opportunity to reflect on professional attitudes and behaviour towards children with emotional challenge Who Is It For? All practitioners who are leading on Mental Health work in schools or on behalf of a Local Authority Full range of agencies committed to meeting social and emotional needs in schools Course Content The training explores the questions: What would a school look like when Mental Health was truly part of everything that was happening, a shared vision? How can we truly embed Mental Health approaches and materials in our school? What can we do to meet complex and challenging emotional needs in schools? Can we learn a process to understand at a deeper level highly complex social and emotional needs? This training will cover: Bringing positive Mental Health approaches to life in schools: creating a shared vision and a set of grounded goals together Circle of Adults process for self-reflection and understanding emotional needs of high profile pupils. We will model and teach how this process links in and strengthens PSHE work in schools. If you liked this try: FRESH APPROACHES TO BEHAVIOUR AND RELATIONSHIPS or: RESTORATIVE INTERVENTIONS

Lean Six Sigma Yellow Belt Certification Program: Virtual In-House Training This course is designed to instill an in-depth understanding of Lean Six Sigma and a clear sense of what is required to define high-impact improvement projects, establish Lean Six Sigma measurements, and complete Lean Six Sigma projects using the systematic and proven Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. This course is designed to instill an in-depth understanding of Lean Six Sigma and a clear sense of what is required to define high-impact improvement projects, establish Lean Six Sigma measurements, and complete Lean Six Sigma projects using the systematic and proven Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. Participants will learn basic tools and techniques of Lean Six Sigma and those who pass a thirty-question exam (70% or above) will become a Certified Lean Six Sigma Yellow Belt. This course is delivered through four 3-hour online sessions. What you Will Learn You'll learn how to: Establish the structure that supports and sustains Lean Six Sigma Quality Identify and calculate key Lean Six Sigma Measurements (Sigma, DPMO, and Yield) Select successful, high-impact projects that match strategic objectives Document, measure, and improve key processes using the DMAIC (Define, Measure, Analyze, Improve, and Control) Methodology Utilize data-based thinking to make key business decisions Introduction to the Fundamentals and Vision of Lean Six Sigma Lean Six Sigma's focus on the customer, on quality, and on results The costs of poor quality Critical factors to consider when deploying Lean Six Sigma Lean Six Sigma as a process improvement methodology Lean Six Sigma metrics Why do it - ROI and payback for Lean Six Sigma Business Process Management Critical Lean Six Sigma roles and responsibilities Main aspects of managing the organizational change Project selection Metrics of Lean Six Sigma and the DMAIC Model How to strategically align business metrics and projects within an organization How to identify and measure quality characteristics which are critical to customers What does the customer (internal or external) really want from our products and services? Establishing appropriate teams and setting those teams up to be successful What defines a good measurement system? How are we doing (learning the secret to measuring the right things, right)? How to improve output measures by understanding and measuring the process Where are there defects (how to properly select and scope high-impact projects)? Where is the process broken (the Lean Six Sigma version of root cause analysis)? How to determine the process efficiency, or value add, of a process The appropriate use of quality tools Understanding the concept of variation and how to reduce knee-jerk reactions How to achieve breakthrough results for any key measure How can we ensure the identified improvements will be sustainable (the basics of process control)?

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)