1174 Courses

The Data Analyst: Data Analytics Diploma With Complete Career Guide Course is designed for individuals eager to delve into the world of data analysis, offering a solid foundation in the field. This course comprehensively covers essential concepts such as data manipulation, statistical analysis, data visualisation, and working with various software tools that are pivotal in the modern data-driven environment. The curriculum is thoughtfully structured to guide learners through the fundamentals and progressively tackle more advanced topics. It allows participants to grasp key techniques and theories, making it ideal for anyone who wants to gain expertise in data analysis at their own pace. The course not only covers technical aspects but also provides valuable insights into building a successful career in data analysis. Learners will gain a clear understanding of the industry’s trends, career paths, and the skills necessary to excel in a data-centric job. The knowledge gained will enable individuals to approach data with confidence, making informed decisions and contributing to organisations across various sectors. With flexible learning that can be accessed anytime, it offers a convenient way for individuals to equip themselves with the tools they need to enter the field of data analysis. Key Features: CPD Certified Data Analyst : Data Analytics Diploma With Complete Career Guide 10 Instant e-certificate and hard copy dispatch by next working day Fully online, interactive course with audio voiceover Developed by qualified professionals in the field Self-paced learning and laptop, tablet, smartphone-friendly 24/7 Learning Assistance Discounts on bulk purchases Course Curriculum: Module 01: Introduction to the World of Data Module 02: Basics of Data Analytics Module 03: Statistics for Data Analytics Module 04: Actions Taken in the Data Analysis Process Module 05: Gathering the Right Information Module 06: Storing Data Module 07: Data Mining Module 08: Excel for Data Analytics Module 09: Tools for Data Analytics Module 10: Data-Analytic Thinking Module 11: Data Visualisation That Clearly Describes Insights Module 12: Data Visualisation Tools ________________________________________________________________________ Complete Career Guide for Data Analyst : Data Analytics Diploma (A to Z) This isn't just a course; it's your ticket to thriving in the sector and your roadmap to the Data Analyst sector. In addition to mastering the essentials of Data Analyst, you'll also gain valuable skills in CV writing, job searching, communication, leadership, and project management. These 9 complementary courses are designed to empower you at every stage of your journey. Stand out in your career, from crafting a winning CV to excelling in interviews. Develop the leadership skills to inspire your team and efficiently manage projects. This holistic approach ensures you're not just job-ready but career-ready. Enrol today, and let's build your success story together in Data Analyst. Your dream career starts here! List of career guide courses included in Data Analyst : Data Analytics Diploma With Complete Career Guide: Course 01: Professional CV Writing and Job Searching Course 02: Communication Skills Training Course 03: Career Development Training Course 04: Time Management Course 05: Returning to Work Training Course 06: Level 3 Diploma in Project Management Course 07: Leadership Skills Course 08: Body Language Course 09: Interview and Recruitment ________________________________________________________________________ Learning Outcomes: Gain a comprehensive understanding of the fundamentals of data analytics. Apply statistical techniques to extract valuable insights from data sets. Execute the entire data analysis process efficiently and effectively. Utilize tools like Excel and other data analytics tools proficiently. Develop a strategic and analytical mindset for problem-solving in data. Master the art of data visualization using various tools for communication. Accreditation All of our courses, including theData Analyst : Data Analytics Diploma With Complete Career Guidecourse, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Certification Once you've successfully completed your Data Analyst : Data Analytics Diploma With Complete Career Guide, you will immediately be sent your digital certificates. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our certifications have no expiry dates, although we recommend renewing them every 12 months. Assessment At the end of the courses, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven't, there's no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself. CPD 100 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals aspiring to enter the data analytics field. Professionals seeking to enhance their data analysis skills. Students interested in building a career in data-driven decision-making. Anyone keen on mastering Excel and other data analytics tools. Requirements There are no formal requirements for this bundle courses to be enrolled. Career path Data Analyst - £30K to 45K/year. Business Intelligence Analyst - £35K to 50K/year. Statistical Analyst - £32K to 48K/year. Data Scientist - £40K to 60K/year. Business Analyst - £35K to 50K/year. Certificates 10 CPD Accredited e-Certificates Digital certificate - Included 10 CPD Accredited Hard Copy Certificates Hard copy certificate - Included

Gain a thorough grasp of time series analysis and its effects, as well as practical tips on how to apply machine learning methods and build RNNs. Learn to train RNNs efficiently while taking crucial concepts such as overfitting and underfitting into account. The course offers a useful, hands-on manner for learning Python methods and principles.

If you aim to enhance your Data Engineering skills, our comprehensive Data Engineering course is perfect for you. Designed for success, this Data Engineering course covers everything from basics to advanced topics in Data Engineering. Each lesson in this Data Engineering course is crafted for easy understanding, enabling you to become proficient in Data Engineering. Whether you are a beginner or looking to sharpen your existing skills, this Data Engineering is the ideal choice. With our Data Engineering exclusive bundle, you will get a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. Courses are Included in This Data Engineering Bundle: Course 01: Diploma in Data Analysis Fundamentals Course 02: Python for Data Analysis Course 03: Data Analytics with Tableau Course 04: SQL Masterclass: SQL For Data Analytics Course 05: Basic Google Data Studio Course 06: Data Analysis and Forecasting in Excel Why Choose Our Data Engineering Course? FREE Data Engineering certificate accredited Get a free student ID card with Data Engineering Training Get instant access to this Data Engineering course. Learn Data Engineering from anywhere in the world The Data Engineering is affordable and simple to understand The Data Engineering is an entirely online, interactive lesson with voiceover audio Lifetime access to the Data Engineering course materials The Data Engineering comes with 24/7 tutor support So enrol now in this Data Engineering Today to advance your career! Start your learning journey straightaway! This Data Engineering's curriculum has been designed by Data Engineering experts with years of Data Engineering experience behind them. The Data Engineering course is extremely dynamic and well-paced to help you understand Data Engineering with ease. You'll discover how to master the Data Engineering skill while exploring relevant and essential topics. Assessment Process Once you have completed all the courses in the Data Engineering bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards. CPD 60 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Engineering bundle is suitable for everyone. Requirements You will not need any prior background or expertise. Career path This Data Engineering bundle will allow you to kickstart or take your career in the related sector to the next stage. Certificates CPD Accredited Digital certificate Digital certificate - Included CPD Accredited Hard copy certificate Hard copy certificate - £29 If you are an international student, you will be required to pay an additional fee of 10 GBP for international delivery, and 4.99 GBP for delivery within the UK, for each certificate

Business Intelligence: In-House Training Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

Business Intelligence Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

>> 12-Hour Knowledge Knockdown! Prices Reduced Like Never Before << In the era of big data, the demand for skilled data science professionals has skyrocketed in the UK. According to a recent report, the data science job market in the UK is expected to grow by over 25% by 2026. Aside from that, Candidates with data science skills have a 96% employment rate and can earn on average £40,000 per year. Our Complete Data Science bundle is about to take you on a tour starting from the beginning. This CCTV Operator Training Bundle Contains 4 of Our Premium Courses for One Discounted Price: Course 01: Complete Data Science Course 02: Data Science with Python Course 03: Information Management Course 04: GDPR Data Protection Take our Complete Data Science Bundle to learn how to maximise your potential and climb your chosen professional ladder. By participating in these popular courses, you can learn the fundamentals of Python. Discover Python data types. Loops, list comprehension, functions, lambda expressions, maps, and filters should all be taught. Learn about the numpy. Indexing, slicing, broadcasting, and boolean masking are all covered in our Complete Data Science course. Recognise arithmetic and universal functions. Discover everything there is to know about pandas. Learn how to use Python to become an expert in data analysis and visualisation. Learning Outcomes of Data Science Develop a comprehensive understanding of the data science lifecycle. Master data analysis techniques and Python programming for data manipulation. Gain proficiency in information management and data organization strategies. Understand data protection regulations, including GDPR, and their implications. Learn to build robust data-driven applications and predictive models. Enhance data visualization skills for effective communication of insights. Invest in your future by enrolling today and gain a competitive edge in the rapidly evolving field of data science. Why Choose Our Data Science bund;e? Get a Free CPD Accredited Certificate upon completion of Data Science Get a free student ID card with Data Science Training The Data Science is affordable and simple to understand Lifetime access to the Data Science course materials The Data Science comes with 24/7 tutor support Start your learning journey straightaway! *** Course Curriculum *** Course 01: Complete Data Science Welcome, Course Introduction & overview, and Environment set-up Python Essentials Python for Data Analysis using NumPy Python for Data Analysis using Pandas Python for Data Visualization using matplotlib Python for Data Visualization using Seaborn Python for Data Visualization using pandas Python for interactive & geographical plotting using Plotly and Cufflinks Capstone Project - Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn - Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems - (Additional Topic) Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 02: Data Science with Python Unit 01: Introduction To Python Data Science Unit 02: Data Cleaning Packages Unit 03: Data Visualization Packages Course 03: Information Management Module 01: Introduction To Information Management Module 02: Information Management Strategy Module 03: Databases And Information Management Module 04: Management Information Systems (MIS) Module 05: Auditing Information Systems Module 06: Ethical And Social Issues And Data Protection Course 04: GDPR Data Protection Module 01: Basics Of GDPR Module 02: Principles Of GDPR Module 03: Legal Foundation For Processing Module 04: Rights Of Individuals Module 05: Accountability And Governance Module 06: Data Protection Officer Module 07: Security Of Data Module 08: Personal Data Breaches Module 09: International Data Transfers After The Brexit Module 10: Exemptions - Part One and much more... How will I get my Certificate? After successfully completing the course, you will be able to order your Certificates as proof of your achievement. PDF Certificate: Free (Previously it was £12.99*4 = £51) CPD Hard Copy Certificate: £29.99 (Each) CPD 40 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Science bundle is suitable for everyone. This bundle is ideal for: Data scientist Data analyst-statistician CSE Students Interns App Developer Coders' Requirements You will not need any prior background or expertise to enrol in this Data Science bundle. Career path This Data Science Training bundle will allow you to kickstart or take your career in the related sector to the next stage. Data Analyst Data Scientist Business Analyst Marketing Analyst Data Engineer Certificates CPD Accredited Digital Certificate Digital certificate - Included Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free. CPD Accredited Hard Copy Certificate Hard copy certificate - £29 Please note that International students have to pay an additional £10 as a shipment fee.

Learn Python programming by developing robust GUIs and games

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)

Project Quality Management: Virtual In-House Training In today's environment, quality is the responsibility of everyone. Project success is no longer just the fulfillment of a project on schedule, on budget, and within the scope. Today, projects aren't successful unless the customer's needs are met at the highest level of quality at the lowest cost to the organization. Project Managers must know customer needs, and manage to them throughout the project lifecycle, in order to gain acceptance. Project Quality Management provides an interactive, hands-on environment for participants to practice identification of critical quality requirements (quality planning), fulfillment of those requirements through well-designed processes (Quality Assurance), and statistical awareness of technical specifications of project deliverables (Quality Control). What You Will Learn You'll learn how to: Plan for higher quality project deliverables Measure key performance indicators on projects, processes, and products Turn data into useful project information Take action on analyzed data that will drive down non-value-added costs and drive up customer acceptance and satisfaction Reduce defects and waste in current project management processes Foundation Concepts Quality Defined Customer Focus Financial Focus Quality Management Process Management Cost of Quality Planning for Quality Project Manager Role in Planning Voice of the Customer Quality Management Plan Measurement System Accuracy Data Gathering Data Sampling Manage Quality Process Management Process Mapping Process Analysis Value Stream Mapping Standardization Visual Workplace and 5S Error Proofing (Poka-Yoke) Failure Mode and Effect Analysis Control Quality The Concept of Variation Common Cause Special Cause Standard Business Reports Tracking Key Measurements Control Charts Data Analysis Variation Root Cause Analysis Variance Management Designing for Quality