144 Data Analysis courses delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who receive Power BI data visualizations and reports from data analysts or from data visualization engineers. These data report recipients want to use the features and capabilities of Power BI to fully explore the visualizations and initial analyses provided to them in reports, perform additional analysis to ask next-level questions of the data, and to customize and create new visualizations and dashboards in order to share new insights and create compelling reports. Overview Explore Power BI reports. Analyze data to get answers and insights. Sort and group data for analysis and reporting. Filter visualizations. Prepare reports. Troubleshoot, collaborate, and share reports. As data acquisition, access, analysis, and reporting are interwoven with our businesses and lives, more and more data is collected about business and personal activities. This abundance of data and the computing power to analyze it has increased the use of data analysis and data visualization across a broad range of job roles. Decision makers of all types, including managers and executives, must interact with, interpret, and develop reports based on data and analysis provided to them. Microsoft Power BI software is designed for data analysis and the creation of visualizations. Data analysts prepare data, perform initial analysis, and create visualizations that are then passed to business data decision makers. These decision makers can use Power BI's tools to explore the data, perform further analysis to find new insights, make decisions, and create customized reports to share their findings. Prerequisites To ensure your success in this course, you have experience managing data with Microsoft Excel or Google Sheets 1. Exploring Power BI Reports Topic A: Data Analysis Workflow with Power BI Topic B: Explore Reports in the Power BI Service Topic C: Edit Reports 2. Analyzing Data to Get Answers and Insights Topic A: Configure Data Visualizations Topic B: Ask New Questions by Changing Aggregation Topic C: Find Answers with Calculations 3. Sorting and Grouping Data for Analysis and Reporting Topic A: Sort Data Topic B: Group Data 4. Filtering Visualizations Topic A: Filter Data to Refine Analysis Topic B: Create Slicers for Reports 5. Preparing Reports Topic A: Format and Annotate Reports Topic B: Emphasize Data in Reports 6. Troubleshooting, Sharing, and Collaborating Topic A: Troubleshoot Data Issues Topic B: Collaborate in Power BI Topic C: Collaborate with Non-Power BI Users

Diploma in Data Analysis Fundamentals Course Overview The Diploma in Data Analysis Fundamentals provides a comprehensive introduction to the core principles and techniques used in data analysis. Throughout this course, learners will explore various data analysis tools and methods, such as Pareto charts, histograms, and control charts, to gain insights into processes and performance. By focusing on key data analysis skills, including identifying variation and interpreting results, this course empowers learners to use data-driven approaches to improve business processes. Upon completion, learners will have the ability to assess and present data effectively, enabling informed decision-making in a wide range of industries. Course Description This course covers essential topics in data analysis, including the principles of process management, tools for data analysis, and methods to interpret and present performance data. Learners will delve into key techniques such as Pareto charts, histograms, run charts, and control charts, focusing on how to use these tools to identify patterns, variations, and areas for improvement. The course also includes a structured approach to performance measurement and provides exercises to reinforce the theoretical knowledge gained. By the end of the course, learners will be equipped with the skills to analyse data, identify trends, and contribute to process improvement initiatives within their organisations. Diploma in Data Analysis Fundamentals Curriculum Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding (See full curriculum) Who is this course for? Individuals seeking to enhance their data analysis skills. Professionals aiming to improve decision-making through data insights. Beginners with an interest in process improvement and business analysis. Data enthusiasts looking to build a solid foundation in analysis techniques. Career Path Data Analyst Business Intelligence Analyst Process Improvement Specialist Operations Analyst Quality Control Analyst

Duration 1 Days 6 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who receive Tableau data visualizations from data analysts or from data visualization engineers. These data report recipients want to take advantage of the many Tableau features and capabilities that enable them to explore the data behind the initial analysis, perform additional analysis to ask next-level questions of the data, and to customize visualizations and dashboards to share new insights and create compelling reports. Overview Explore Tableau reports. Analyze data to get answers and insights. Sort and group data for analysis and reporting. Filter views. Prepare reports. Troubleshoot, collaborate, and share views and analysis As data acquisition, access, analysis, and reporting are interwoven with our businesses and lives, more and more data is collected about business and personal activities. This abundance of data and the computing power to analyze it has increased the use of data analysis and data visualization across a broad range of job roles. Decision makers of all types, including managers and executives, must interact with, interpret, and develop reports based on data and analysis provided to them. Tableau© software is designed for data analysis and the creation of visualizations. Data analysts prepare data, perform initial analysis, and create visualizations that are then passed on to business data-driven decision makers. These decision makers can use Tableau's tools to explore the data, perform further analysis to find new insights, make decisions, and create customized reports to share their findings. Prerequisites To ensure your success in this course, you should have experience managing data with Microsoft© Excel© or Google Sheets? Lesson 1: Exploring Tableau Reports Topic A: Data Analysis Workflow with Tableau Topic B: Explore Views Topic C: Edit Workbooks Lesson 2: Analyzing Data to Get Answers and Insights Topic A: Configure Marks with the Marks Card Topic B: Ask New Questions by Changing Aggregation Topic C: Find Answers with Calculations Topic D: Answer Questions with Table Calculations Lesson 3: Sorting and Grouping Data for Analysis and Reporting Topic A: Sort Data Topic B: Group Data Lesson 4: Filtering Views Topic A: Filter Data to Refine Analysis Topic B: Create Interactive Filters for Reports Lesson 5: Preparing Reports Topic A: Format and Annotate Views to Tell Your Story Topic B: Emphasize Data in Reports Topic C: Animate Visualizations for Clarity Lesson 6: Troubleshooting, Sharing, and Collaborating Topic A: Troubleshoot Data Issues Topic B: Collaborate in Tableau Online Topic C: Collaborate with Non-Tableau Users

Duration 3 Days 18 CPD hours This course is intended for The audience for this course are data professionals and business intelligence professionals who want to learn how to accurately perform data analysis using Power BI. This course is also targeted toward those individuals who develop reports that visualize data from the data platform technologies that exist on both in the cloud and on-premises. This course covers the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Prerequisites Understanding core data concepts. Knowledge of working with relational data in the cloud. Knowledge of working with non-relational data in the cloud. Knowledge of data analysis and visualization concepts. DP-900T00 Microsoft Azure Data Fundamentals is recommended 1 - Discover data analysis Overview of data analysis Roles in data Tasks of a data analyst 2 - Get started building with Power BI Use Power BI Building blocks of Power BI Tour and use the Power BI service 3 - Get data in Power BI Get data from files Get data from relational data sources Create dynamic reports with parameters Get data from a NoSQL database Get data from online services Select a storage mode Get data from Azure Analysis Services Fix performance issues Resolve data import errors 4 - Clean, transform, and load data in Power BI Shape the initial data Simplify the data structure Evaluate and change column data types Combine multiple tables into a single table Profile data in Power BI Use Advanced Editor to modify M code 5 - Design a semantic model in Power BI Work with tables Create a date table Work with dimensions Define data granularity Work with relationships and cardinality Resolve modeling challenges 6 - Add measures to Power BI Desktop models Create simple measures Create compound measures Create quick measures Compare calculated columns with measures 7 - Add calculated tables and columns to Power BI Desktop models Create calculated columns Learn about row context Choose a technique to add a column 8 - Use DAX time intelligence functions in Power BI Desktop models Use DAX time intelligence functions Additional time intelligence calculations 9 - Optimize a model for performance in Power BI Review performance of measures, relationships, and visuals Use variables to improve performance and troubleshooting Reduce cardinality Optimize DirectQuery models with table level storage Create and manage aggregations 10 - Design Power BI reports Design the analytical report layout Design visually appealing reports Report objects Select report visuals Select report visuals to suit the report layout Format and configure visualizations Work with key performance indicators 11 - Configure Power BI report filters Apply filters to the report structure Apply filters with slicers Design reports with advanced filtering techniques Consumption-time filtering Select report filter techniques Case study - Configure report filters based on feedback 12 - Enhance Power BI report designs for the user experience Design reports to show details Design reports to highlight values Design reports that behave like apps Work with bookmarks Design reports for navigation Work with visual headers Design reports with built-in assistance Tune report performance Optimize reports for mobile use 13 - Perform analytics in Power BI Explore statistical summary Identify outliers with Power BI visuals Group and bin data for analysis Apply clustering techniques Conduct time series analysis Use the Analyze feature Create what-if parameters Use specialized visuals 14 - Create and manage workspaces in Power BI Distribute a report or dashboard Monitor usage and performance Recommend a development life cycle strategy Troubleshoot data by viewing its lineage Configure data protection 15 - Manage semantic models in Power BI Use a Power BI gateway to connect to on-premises data sources Configure a semantic model scheduled refresh Configure incremental refresh settings Manage and promote semantic models Troubleshoot service connectivity Boost performance with query caching (Premium) 16 - Create dashboards in Power BI Configure data alerts Explore data by asking questions Review Quick insights Add a dashboard theme Pin a live report page to a dashboard Configure a real-time dashboard Set mobile view 17 - Implement row-level security Configure row-level security with the static method Configure row-level security with the dynamic method Additional course details: Nexus Humans PL-300T00: Microsoft Power BI Data Analyst training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the PL-300T00: Microsoft Power BI Data Analyst course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Course Overview This comprehensive course on "Data Analysis and Forecasting in Excel" provides learners with essential skills to manage, analyse, and visualise data effectively using Excel. Whether you are analysing historical data or forecasting future trends, this course covers key tools such as PivotTables, charts, and lookup functions to make your data work for you. Learners will also gain proficiency in automating tasks and creating dynamic reports, which are invaluable for decision-making processes in various professional settings. By the end of the course, you will have the capability to work with complex data sets, produce insightful reports, and apply forecasting techniques to guide future strategies. Course Description In this course, learners will delve into the full spectrum of data analysis capabilities offered by Excel. Topics include modifying worksheets, working with lists, and using advanced tools such as PivotTables, PivotCharts, and lookup functions. Learners will explore the process of visualising data through charts and sparklines, allowing them to convey complex information in an accessible manner. The course also covers automating workbook functionality and creating mapping data for better analysis. Additionally, learners will gain expertise in forecasting data trends to support strategic planning. By the end of the course, participants will have developed a comprehensive understanding of Excel’s analytical tools, enabling them to manage data with efficiency and precision in various business contexts. Course Modules Module 01: Modifying a Worksheet Module 02: Working with Lists Module 03: Analyzing Data Module 04: Visualizing Data with Charts Module 05: Using PivotTables and PivotCharts Module 06: Working with Multiple Worksheets and Workbooks Module 07: Using Lookup Functions and Formula Auditing Module 08: Automating Workbook Functionality Module 09: Creating Sparklines and Mapping Data Module 10: Forecasting Data (See full curriculum) Who is this course for? Individuals seeking to enhance their Excel data analysis skills. Professionals aiming to improve their forecasting and reporting capabilities. Beginners with an interest in data management and analysis. Those seeking to enhance their proficiency in Excel for career advancement. Career Path Data Analyst Business Analyst Financial Analyst Marketing Analyst Operations Manager Project Manager Excel Specialist in various industries such as finance, marketing, and logistics

Duration 2 Days 12 CPD hours This course is intended for New users of IBM SPSS Statistics Users who want to refresh their knowledge about IBM SPSS Statistics Anyone who is considering purchasing IBM SPSS Statistics Overview Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables. Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders Additional course details: Nexus Humans 0G53BG IBM SPSS Statistics Essentials (V26) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the 0G53BG IBM SPSS Statistics Essentials (V26) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is people who are moving into a database role, or whose role has expanded to include database technologies. Developers that deliver content from SQL Server databases will also benefit from this material. Overview After completing this course, you will be able to: Describe key database concepts in the context of SQL Server Describe database languages used in SQL Server Describe data modelling techniques Describe normalization and denormalization techniques Describe relationship types and effects in database design Describe the effects of database design on performance Describe commonly used database objects This course is provided as an introductory class for anyone getting started with databases. It will be useful to programmers and other IT professionals whose job roles are expanding into database management. Students will learn fundamental database concepts through demonstrations and hands-on labs on a SQL Server instance. This material updates and replaces course Microsoft course 10985 which was previously published under the same title. Module 1: Introduction to databases Introduction to Relational Databases Other Databases and Storage Data Analysis SQL Server Database Languages Module 2: Data Modeling Data Modelling Designing a Database Relationship Modeling Module 3: Normalization Fundamentals of Normalization Normal Form Denormalization Module 4: Relationships Introduction to Relationships Planning Referential Integrity Module 5: Performance Indexing Query Performance Concurrency Module 6: Database Objects Tables Views Stored Procedures, Triggers and Functions

Project Quality Management: 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

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

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