14146 Organ courses

Objective-C programming training course description A hands on introduction that will allow you to master Objective-C and start using it to write powerful native applications for even the newest Macs and iOS devices! Using The step-by-step approach, will let you get comfortable with Objective-C's unique capabilities and Apple's Xcode 5 development environment. Make the most of Objective-C objects and messaging. Work effectively with design patterns, collections, blocks, foundation classes, threading, Git and a whole lot more. Every session builds on what you've already learned, giving a rock-solid foundation for real-world success! What will you learn Use Xcode 5. Declare classes, instance variables, properties, methods, and actions. Use arrays, dictionaries, and sets. Expand and extend classes with protocols, delegates, categories, and extensions. Use Apple's powerful classes and frameworks. Objective-C programming training course details Who will benefit: Developers wanting to learn Objective-C. Prerequisites: Software development fundamentals. Duration 5 days Objective-C programming training course contents PART 1: GETTING STARTED WITH OBJECTIVE-C The Developer Program: Objective-C, enrolling as an Apple Developer, setting up the development environment, Xcode. Your first project. OO programming with Objective-C: OO projects, Frameworks, classes and instances, encapsulation, accessors, Inheritance. OO features in Objective-C: Messages, methods, working with id, nesting messages, method signatures and parameters. allocating and initializing objects. Using Xcode: Xcode, source code control, git and Xcode, Using a Remote Repository. Compiler Directives: Projects, Compiler Directives, Prefix headers, main.m, .h files. PART 2: OBJECTIVE-C BASICS Messaging in a Testbed App: Setting Up the Testbed Apps, Adding a Text Field and Connecting It to Your Code, Sending a Message to the Text Field, Reviewing the Message Syntax. Declaring a Class in an Interface File: Context, Creating an Instance Variable with id, What Happens When Execution Stops, dynamic binding, Creating an Instance Variable for with the Class Name and with a Superclass Name, instance variable visibility. Properties in an Interface File: Interface Variables vs Properties, Declared Properties, Using Attributes. Implementing Properties. @synthesize, @dynamic. Methods in an Interface File: Methods in a Class, class and instance methods, Method declaration, returning complex data structures from Methods. Actions in an Interface File: Actions, Actions in OS X and iOS, disconnecting actions. Routing messages with selectors: Receiver and selector objects in messages, Objective-C Runtime, SEL and @selector (), performSelector, NSInvocation, testing whether an Instance can respond to a selector. Building on the Foundation: The Foundation Framework, Foundation Classes, Foundation Paradigms and Policies; Mutability, class clusters, notifications. Defining a Class in Implementation Files: Projects, dynamic typing, creating a new App, implementing a method, expanding Classses with init Methods. Organizing Data with Collections: Collecting Objects, Property Lists, Runtime, comparing the Collection Classes, Creating a Collection, Objective-C Literal Syntax, Enumerating collections, Testing Membership in a Collection, Accessing an Object in a Collection. Managing Memory and Runtime Objects: Managing objects in memory, managing reference counts manually and with ARC, variable qualifiers, variable autorelease. PART 3: EXPANDING AND EXTENDING CLASSES Protocols and Delegates: Subclassing, Protocols, Delegates, Looking Deeper Inside Protocols. Categories and Extensions: Comparing categories and protocols, categories vs subclasses, working with categories, class extensions, informal protocols. Associative References and Fast Enumeration: Objective-C 2.0 Time-Saving Features, Extending Classes by Adding Instance Variables (Sort of), Using Fast Enumeration. Blocks: Revisiting Blocks, Callbacks, Blocks, Exploring Blocks in Cocoa, Cocoa Blocks and Memory. PART 4: BEYOND THE BASICS Handling Exceptions and Errors: Exception and Error classes: NSException, NSError, Identifying exceptions, throwing exceptions, catching exceptions. Queues and Threading: Getting Started with Concurrency, Introducing Queues, Dispatch Sources, Using Dispatch Queues. Working with the Debugger: Logging Information, Console Logs, NSLog, Smart Breakpoints, enhancing breakpoints with messages. Using Xcode Debug Gauges for Analysis: Debug Gauges, Monitoing CPU and memory utilization, monitoring energy, Using Instruments. PART 5: OPTIONAL TOPICS C Syntax Summary: Data Types, Control Structures. Apps, Packages, and Bundles: Project Bundles, lproj Files, Asset Catalogs, plist Files, Precompiled Header Files (.pch). Archiving and Packaging Apps for Development and Testing: Archiving.

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)

Has the Computer System Validation Engineer left and you’ve been handed their responsibilities? Do the thoughts of your next audit fill you with dread? CSV can be frustrating but this program will show you how to manage electronic data in a regulated manufacturing/laboratory/clinical environment using the GAMP framework and ensure compliance with FDA’s 21 CFR Part 11, EU Annex 11 or other regulatory guidelines.

Trauma Risk Management is an evidence based, peer delivered risk assessment and ongoing support system, designed specifically to help in the management of traumatic events.

CRRUK equips professionals with the concepts, skills and tools to build conscious, intentional relationships, and to coach relationship systems of any size.

WE WORK HARD TO BE MORE PRODUCTIVE AND AVOID WASTING TIME. THEREFORE, THE MEETINGS WE HAVE SHOULD BE IMPORTANT. In order to make the best use of our time, we need to be efficient. Our main goal is to increase productivity and avoid wasting time. Therefore, it is important that the meetings we have are meaningful and necessary. Unlike the story of “The Boy Who Cried Wolf,” we want our meeting attendees to understand the value of our work. We show them by setting a good example. Over time, they realize that our meetings are held for important reasons. When attendees know that our meetings are productive, respectful (in terms of length and content relevance), and serve a purpose, they are happy to attend and contribute their best. This class will help participants organize their ideas and communicate clearly. As facilitators, you will learn to be concise and communicate with confidence. By running more productive sessions, you will increase satisfaction and open up dialogue with your audience. FORGE AHEAD – THE REWARDS AWAIT.THROUGH THE IMPROVING COMMUNICATIONS RUNNING EFFECTIVE MEETINGS CLASS, YOU WILL: Gain new perspective and methods to create better, more productive sessions; Have a clear purpose to keep focus and engage with interest; Inspire facilitator-audience dialogue, ensuring a genuine exchange of ideas; Manage distractions and disruptions appropriately, keeping efficiency; and Probe for information, getting the most useful level of detail for improvement.   Online Format—Running Effective Meetings is a 4-hour interactive online class for up to six people. Register for this class and you will be sent ONLINE login instructions prior to the class date. As a trainer and human resources professional, I truly believe in the value that Dr. Atkins can add to any organization. The seminar he provided was easy to follow and thought provoking. His personable demeanor along with his humble spirit and obvious expertise make his sessions gems that you cannot afford to pass up. Deirdre Cash, PHRPaco Group, NYC

Professional Management is crucial to your company's success. In fact, managers can have the single largest impact on your organisation's productivity and overall profitability, with more influence over employee morale and performance than anyone else.

Overview Extending business activities beyond organisational boundaries to create value for the organisation is not new. However, the quantity and complexity of the relationships that now exist beyond organisational boundaries are increasing, as is regulatory and stakeholder scrutiny. While partnerships with third parties, including critical suppliers, can be beneficial to the organisation on so many levels, such alliances can expose the organisation to many unknowns, and those unknowns will undoubtedly increase the level of risk. The key, then, is properly managing the infrastructure, systems, staff and outside support to adequately manage that risk. 

Overview TQM is a completely structured, comprehensive, and organization-wide approach taken towards organisational structuring and management that continuously seeks to improve the quality rendered by the products, services belted out by the organization and the productivity of the resources handling the product. The aim is to continuously induce quality at each and every step of the organisational workflow in a bid to refine and fine- tune the processes and receive continuous feedback as per customers' norms to refine still further.