1101 Data Analysis courses

***24 Hour Limited Time Flash Sale*** Data Visualisation Professional - CPD Certified Admission Gifts FREE PDF & Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Data Visualisation Professional - CPD Certified Bundle provides unique insights and tools that can help you achieve your goals. Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Data Visualisation Professional - CPD Certified bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level. But that's not all. When you enrol in Data Visualisation Professional - CPD Certified Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free. Courses Are Included In this Data Visualisation Professional - CPD Certified Career Bundle: Course 01: Master JavaScript with Data Visualization Course 02: SQL for Data Science, Data Analytics and Data Visualization Course 03: Spatial Data Visualization and Machine Learning in Python Course 04: Data Analysis Course 05: Excel Data Analysis Course 06: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 07: Python Programming Bible Course 08: Python Data Science with Numpy, Pandas and Matplotlib Course 09: Data Science & Machine Learning with Python Course 10: Statistics & Probability for Data Science & Machine Learning Course 11: R Programming for Data Science Course 12: Quick Data Science Approach from Scratch Course 13: Mastering SQL Programming Course 14: Learn Python, JavaScript, and Microsoft SQL for Data science Course 15: Web Scraping and Mapping Dam Levels in Python and Leaflet Course 16: Business Data Analysis Course 17: Business Intelligence and Data Mining Diploma Course 18: Google Data Studio: Data Analytics Course 19: Google Analytics for Everyone Course 20: Big Data Analytics with PySpark Tableau Desktop and MongoDB Course 21: Big Data Analytics with PySpark Power BI and MongoDB Course 22: Introduction to Data Analytics with Tableau Course 23: Microsoft Power BI - Master Power BI in 90 Minutes! Course 24: PowerBI Formulas Course 25: Develop Big Data Pipelines with R & Sparklyr & Tableau Course 26: Develop Big Data Pipelines with R, Sparklyr & Power BI Course 27: Microsoft Access Tables and Queries Course 28: Statistical Analysis Course 29: Research Methods in Business Course 30: Customer Analytics Training With Data Visualisation Professional - CPD Certified, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner. Don't let this opportunity pass you by. Enrol in Data Visualisation Professional - CPD Certified today and take the first step towards achieving your goals and dreams. Why buy this Data Visualisation Professional - CPD Certified? Free CPD Accredited Certificate upon completion of Data Visualisation Professional - CPD Certified Get a free student ID card with Data Visualisation Professional - CPD Certified Lifetime access to the Data Visualisation Professional - CPD Certified course materials Get instant access to this Data Visualisation Professional - CPD Certified course Learn Data Visualisation Professional - CPD Certified from anywhere in the world 24/7 tutor support with the Data Visualisation Professional - CPD Certified course. Start your learning journey straightaway with our Data Visualisation Professional - CPD Certified Training! Data Visualisation Professional - CPD Certified premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of the Data Visualisation Professional - CPD Certified is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience. Certification You have to complete the assignment given at the end of the Data Visualisation Professional - CPD Certified course. After passing the Data Visualisation Professional - CPD Certified exam You will be entitled to claim a PDF & Hardcopy certificate accredited by CPD Quality standards completely free. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Visualisation Professional - CPD Certified course is ideal for: Students seeking mastery in Data Visualisation Professional - CPD Certified Professionals seeking to enhance Data Visualisation Professional - CPD Certified skills Individuals looking for a Data Visualisation Professional - CPD Certified-related career. Anyone passionate about Data Visualisation Professional - CPD Certified Requirements This Data Visualisation Professional - CPD Certified doesn't require prior experience and is suitable for diverse learners. Career path After the assessment, you will be ready for countless job opportunities. Some of them are- Business Analyst Business Intelligence Analyst Analytics Manager Data Analyst Data Scientist However, in the growing market of the UK, these professionals are expected to earn between £25k to £80k yearly on average. Certificates CPD Accredited Digital Certificate Digital certificate - Included CPD Accredited Hard Copy Certificate Hard copy certificate - Included If you are an international student, then you have to pay an additional 10 GBP for each certificate as an international delivery charge.

Duration 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts

Feeling Stuck in Your Career? The Data Analyst (Data Analytics) Bundle is Your Skill-Building Solution. This exceptional collection of 30 premium courses is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the Data Analyst-(Data Analytics) bundle provides an engaging learning experience, helping you learn skills across various disciplines. With the Data Analyst (Data Analytics) Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The Data Analyst-(Data Analytics) bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set. By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Data Analyst-(Data Analytics) bundle on your job life and career growth! This Data Analyst (Data Analytics) Bundle Comprises the Following CPD Accredited Courses: Course 01: Basic Data Analysis Course 02: Business Data Analysis Course 03: Introduction to Data Analytics with Tableau Course 04: Google Data Studio: Data Analytics Course 05: SQL Database Basics for Everyone Course 06: R Programming for Data Science Course 07: 2021 Data Science & Machine Learning with R from A-Z Course 08: Learn Python, JavaScript, and Microsoft SQL for Data Science Course 09: Spatial Data Visualisation and Machine Learning in Python Course 10: Building Big Data Pipelines with PySpark MongoDB and Bokeh Course 11: Complete Python Machine Learning & Data Science Fundamentals Course 12: Clinical Data Management with SAS Programming Course 13: Certificate in Data Entry and Management Course 14: Quick Data Science Approach from Scratch Course 15: Web Mapping and Data Visualizations Course 16: Programming AutoCAD with SQL Server Database Using C# Course 17: Big Data Analytics with PySpark Power BI and MongoDB Course 18: Develop Big Data Pipelines with R & Sparklyr & Tableau Course 19: Develop Big Data Pipelines with R, Sparklyr & Power BI Course 20: Data Center Training Essentials: Power & Electrical Course 21: Business Intelligence and Data Mining Course 22: Set Menu Prices for your restaurant using data Course 23: Data Analysis In Excel Course 24: Data Protection Course 25: Reporting and Data Course 26: Career Development Plan Fundamentals Course 27: CV Writing and Job Searching Course 28: Learn to Level Up Your Leadership Course 29: Networking Skills for Personal Success Course 30: Ace Your Presentations: Public Speaking Masterclass What will make you stand out? Upon completion of this online Data Analyst (Data Analytics) Bundle, you will gain the following: CPD QS Accredited Proficiency with this Data Analyst-(Data Analytics) bundle After successfully completing the Data Analyst-(Data Analytics) bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities. Lifetime access to the whole collection of learning materials of this Data Analyst-(Data Analytics) bundle The online test with immediate results You can study and complete the Data Analyst-(Data Analytics) bundle at your own pace. Study for the Data Analyst-(Data Analytics) bundle using any internet-connected device, such as a computer, tablet, or mobile device. The Data Analyst (Data Analytics) bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development. Advance the future of learning with the Data Analyst-(Data Analytics) bundle, a comprehensive, complete collection of 30 courses. Each course in the Data Analyst-(Data Analytics) bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience. Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the Data Analyst-(Data Analytics) bundle offers the flexibility and convenience to learn at your own pace. Make the Data Analyst-(Data Analytics) package your trusted partner in your lifelong learning journey. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Data Analyst (Data Analytics) bundle is perfect for: Expand your knowledge and skillset for a fulfilling career with the Data Analyst-(Data Analytics) bundle. Become a more valuable professional by earning CPD certification and mastering in-demand skills with the Data Analyst-(Data Analytics) bundle. Discover your passion or explore new career options with the diverse learning opportunities in the Data Analyst-(Data Analytics) bundle. Learn on your schedule, in the comfort of your home - the Data Analyst-(Data Analytics) bundle offers ultimate flexibility for busy individuals. Requirements You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment. Career path Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the Data Analyst (Data Analytics) bundle. Certificates CPD Certificates Digital certificate - Included

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

Data Analyst (Data Analytics) - CPD Certified Have you ever wondered how companies get insights from massive volumes of data to stay competitive and make wise decisions? If so, then participate in our exclusive Data Analytics Course. This Data Analytics Course describes the fundamentals of data, statistics, and an introduction to data analytics. How to get data and where to find it is explained in the Data Analytics Course. Moreover, this Data Analytics Course covers data cleansing, preprocessing, and exploratory data analysis (EDA). Additionally, the Data Analytics Course provides an introduction to Python and Excel for data analytics. This thorough Data Analytics Course includes lessons on data wrangling with Pandas (python) and data visualisation using Matplotlib and Seaborn (python). Enrol in our Data Analytics Course to study the fundamentals of statistical analysis and machine learning. Special Offers of this Data Analyst (Data Analytics) Course Data Analyst (Data Analytics) Course includes a FREE PDF Certificate. Lifetime access to this Data Analyst (Data Analytics) Course Instant access to this Data Analyst (Data Analytics) Course Get FREE Tutor Support from Monday to Friday in this Data Analyst (Data Analytics) Course Courses are included in this Data Analyst (Data Analytics) Course Course 01: Cyber Security Course 02: GDPR Course 03: Business Administration [ Note: Free PDF certificate as soon as completing the Data Analyst (Data Analytics) Course] Course Curriculum of Data Analyst (Data Analytics) - CPD Certified Module 1: Introduction to Data Analytics Module 2: Basics of Data and Statistics Module 3: Data Collection and Sources Module 4: Data Cleaning and Preprocessing Module 5: Exploratory Data Analysis (EDA) Module 6: Introduction to Excel for Data Analytics Module 7: Introduction to Python for Data Analytics Module 8: Data Wrangling with Pandas (Python) Module 9: Data visualisation with Matplotlib and Seaborn (Python) Module 10: Introduction to Basic Statistical Analysis Module 11: Introduction to Machine Learning Module 12: Capstone Project - Exploratory Data Analysis Assessment Method After completing each module of the Data Analyst (Data Analytics) Course, you will find automated MCQ quizzes. To unlock the next module, you need to complete the quiz task and get at least 60% marks. Certification After completing the MCQ/Assignment assessment for this Data Analyst (Data Analytics) course, you will be entitled to a Certificate of Completion from Training Tale. The certificate is in PDF format, which is completely free to download. A printed version is also available upon request. It will also be sent to you through a courier for £13.99. Who is this course for? Data Analyst (Data Analytics) - CPD Certified For business professionals, entrepreneurs, or anybody else looking to have a thorough grasp of data analysis in a commercial setting, this Data Analytics Course is ideal. Requirements There are no specific requirements for Data Analyst (Data Analytics) Course because it does not require any advanced knowledge or skills. Career path Data Analyst (Data Analytics) - CPD Certified This Data Analytics Course will assist you in obtaining positions as a business analyst, marketing analyst, data analyst, and in related fields. Certificates Certificate of completion Digital certificate - Included

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

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

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

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)