Course Objectives
Applied Data Analysis Masterclass: Visualization, Statistics and Advanced Programs - Virtual Learnin
Course Objectives
By the end of the course, participants will be able to:
Comprehend and plan the lifecycle of a good data analysis project
Translate any business into a comprehensive database
Evaluate data quality for analysis and reporting
Describe and interpret data basics with complete descriptive statistics
Explore the complete story behind data analysis
Target Audience
Applied Data Analysis is the foundation for all Machine Learning and Artificial Intelligence (AI) practitioners. It is prerequisite knowledge that is applicable in all industries and data related functions.
Target Competencies
Project Design
Findings Visualization
Data Analysis
Problem Solving using analytical tools
Course Outline
Data visualization and descriptive statisticsThe different types of DataData sourcesDataVariablesData visualizationPies, Doughnuts, BarsHistograms, Lines, Scatter plotsHeat maps and Tuckey boxesGeographical mapsCentral tendency measurementsAverageMedianModeScatter tendency measurementsQuartileVarianceStandard deviationEstimationsPunctualConfidence Interval
Comparing two groupsTwo mean testEqual variances (t-test)Unequal variances (t-test – Welch correction)Two variance test (F-Test)Two proportion test (Chi Square test)Two distribution test (Chi Square test)Attraction – Repulsion MatrixVertical and horizontal profiling
Comparing multiple groupsMultiple mean testEqual variances (F-Test and ANOVA Table)Unequal variances (F-Test – Welch Correction)Multiple Variance testLevene testChi Square testMultiple proportion test (Chi Square test)Multiple distribution test (Chi Square test)Attraction – Repulsion MatrixVertical and horizontal profilingMean pair comparisons methods:GeneralBonferroniTukey - Kramer
Simple regressionsSimple linear regressionLine equationTesting the regression line validity (t-nullity test)R vs. R Square interpretationANOVA table analysisSimple logistic regressionProbabilistic modelTesting the model validity (Chi Square test)Predicting classificationOdds ratio interpretation
Data analysis project best practicesData analysis project best practicesAskDesignPreviewAnalyzeCommunicateSampling methodsRandom and systematicMultilevel, stratified and clusterConvenient, quota and judgmentalPMP for research projects overviewIntegration, cost, scope, time, cost, quality, communicationRisk, procurement and stakeholders