{"product_id":"introduction-to-data-analytics","title":"Introduction to Data Analytics","description":"\u003cdiv\u003e\u003cp\u003eAs data evolves for organizations, employees must understand the value of the data they hold. This \u003cstrong\u003eData Analytics Introduction\u003c\/strong\u003e provides a clear understanding of data analytics's purpose, tools, and techniques. In addition, it will help attendees to plan the data and digital strategy for their organizations.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eIntroduction to Data Analytics Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eBack at work, attendees will be able to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDefine what Data Analytics is and how it helps with business-focused decision-making\u003c\/li\u003e\n\u003cli\u003eUnderstand the fundamentals of pattern recognition\u003c\/li\u003e\n\u003cli\u003eDifferentiate between data roles such as Data Analyst, Data Scientist, Data Engineer, Business Analyst, and Business Intelligence Analyst.\u003c\/li\u003e\n\u003cli\u003eRecognize the value, terminology, and challenges of Business Intelligence\u003c\/li\u003e\n\u003cli\u003eUnderstand how Data Mining builds knowledge, insights, patterns, \u0026amp; data advantages\u003c\/li\u003e\n\u003cli\u003eAppreciate the usefulness of data visualization, visual patterns, and Infographics for stakeholder communication\u003c\/li\u003e\n\u003cli\u003eImprove awareness of the value of the data your organization holds and how to manipulate it\u003c\/li\u003e\n\u003cli\u003eHave excellent fundamental knowledge of data, how it is captured, and how it is visualized for us in the business\u003c\/li\u003e\n\u003cli\u003ePosition Data Warehouses as data management facilities that help to:\n\u003cul\u003e\n\u003cli\u003eCreate reports and analysis\u003c\/li\u003e\n\u003cli\u003eSupport managerial decision making\u003c\/li\u003e\n\u003cli\u003eEngineered for efficient reporting and querying\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTraining Prerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eA basic understanding of what data is and the function of data analysis\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCertification Information\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLearning Tree Exam included\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData Analytics Introduction Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 1: Data Analytics Introduction\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eBusiness Intelligence \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExample: MoneyBall: Data Mining in Sports\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePattern Recognition \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTypes of Patterns\u003c\/li\u003e\n\u003cli\u003eFinding a Pattern\u003c\/li\u003e\n\u003cli\u003eUses of Patterns\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThe Data Processing Chain \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData Database\u003c\/li\u003e\n\u003cli\u003eData Warehouse\u003c\/li\u003e\n\u003cli\u003eData Mining\u003c\/li\u003e\n\u003cli\u003eData Visualization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Analytics Terminology and Careers \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview Wheel\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 2:  BI Concepts \u0026amp; Applications\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eIntroduction\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExample: Schools and Academies\u003c\/li\u003e\n\u003cli\u003eBI in Education\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eBI for Better Decisions \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecision types \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBI Tools\u003c\/li\u003e\n\u003cli\u003eBI Skills\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eBI Applications \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eCustomer Relationship Management\u003c\/li\u003e\n\u003cli\u003eHealthcare and Wellness\u003c\/li\u003e\n\u003cli\u003eEducation\u003c\/li\u003e\n\u003cli\u003eRetail Banking\u003c\/li\u003e\n\u003cli\u003eFinancial Services\u003c\/li\u003e\n\u003cli\u003eInsurance Manufacturing\u003c\/li\u003e\n\u003cli\u003eSupply Chain Management\u003c\/li\u003e\n\u003cli\u003eTelecom\u003c\/li\u003e\n\u003cli\u003ePublic Sector\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview Wheel \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase Study Exercise \u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 3: Data Warehousing\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eIntroduction\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExample: University Health System – BI in Healthcare\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDesign Considerations for DW \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDW Development Approaches \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDW Architecture\u003c\/li\u003e\n\u003cli\u003eData Sources\u003c\/li\u003e\n\u003cli\u003eData Loading Processes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Warehouse Design \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDW Access\u003c\/li\u003e\n\u003cli\u003eDW Best Practices\u003c\/li\u003e\n\u003cli\u003eData Lakes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview Wheel\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase Study Exercise: Step 2 \u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 4:  Data Mining Introduction\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eIntroduction\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExample: Target Corp – Data Mining in Retail\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGathering and selecting data\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData cleansing and preparation\u003c\/li\u003e\n\u003cli\u003eOutputs of Data Mining\u003c\/li\u003e\n\u003cli\u003eEvaluating Data Mining Results\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Mining Techniques\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTools and Platforms for Data Mining\u003c\/li\u003e\n\u003cli\u003eData Mining Best Practices\u003c\/li\u003e\n\u003cli\u003eMyths about data mining\u003c\/li\u003e\n\u003cli\u003eData Mining Mistakes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview Wheel\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase Study Exercise: Step 3\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 5: Data Visualization\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eIntroduction\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExample: Dr. Hans Rosling - Visualizing Global Public Health\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExcellence in Visualization\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTypes of Charts\u003c\/li\u003e\n\u003cli\u003eVisualization Example\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTips for Data Visualization\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview Wheel\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase Study Exercise: Step 4\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 6 Popular Data Mining Techniques\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eDecision Trees\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eExample: Predicting Heart Attacks using Decision Trees\u003c\/li\u003e\n\u003cli\u003eDecision Tree problem\u003c\/li\u003e\n\u003cli\u003eDecision Tree Construction\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003e Regression and Time Series Analysis\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eCorrelations and Relationships\u003c\/strong\u003e\u003c\/li\u003e\n\u003cli\u003eA visual look at relationships\u003c\/li\u003e\n\u003cli\u003eRegression\u003c\/li\u003e\n\u003cli\u003eNon-linear regression\u003c\/li\u003e\n\u003cli\u003eLogistic Regression\u003c\/li\u003e\n\u003cli\u003eAdvantages and Disadvantages of Regression\u003c\/li\u003e\n\u003cli\u003eTime Series Analysis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Neural Networks\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eExample: IBM Watson - Analytics in Medicine\u003c\/li\u003e\n\u003cli\u003e\u003cstrong\u003ePrinciples of an Artificial Neural Network\u003c\/strong\u003e\u003c\/li\u003e\n\u003cli\u003eBusiness Applications of ANN Design\u003c\/li\u003e\n\u003cli\u003eRepresentation of a Neural Network\u003c\/li\u003e\n\u003cli\u003eArchitecting a Neural Network\u003c\/li\u003e\n\u003cli\u003eDeveloping an 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