Invitation to Quantitative Data Management and Analysis with R training
Title: Invitation to Quantitative Data Management and Analysis with R training Quantitative Data Management and Analysis with R course-Sep 28 to Oct 2,2020 for 5 DaysUse this link to Register online attendance or Normal attendance as individual or groupDownload PDF CalendarFoscore Development Center IntroductionThis course is designed for participants who plan to use R for the management, coding, analysis and visualization of qualitative data. The course’s content is spread over seven modules and includes: Basics of Applied Statistical Modelling, Essentials of the R Programming, Statistical Tools, Probability Distributions, Statistical Inference, Relationship between Two Different Quantitative Variables and Multivariate Analysis. The course is entirely hands-on and uses sample data to learn R basics and advanced features.DURATION5 daysWHO SHOULD ATTEND?Statistician, analyst, or a budding data scientist and beginners who want to learn how to analyze data with R,Course Objective:Analyze t data by applying appropriate statistical techniquesInterpret the statistical analysisIdentify statistical techniques a best suited to data and questionsStrong foundation in fundamental statistical conceptsImplement different statistical analysis in R and interpret the resultsBuild intuitive data visualizationsCarry out formalized hypothesis testingImplement linear modelling techniques such multiple regressions and GLMsImplement advanced regression analysis and multivariate analysisCourse contentMODULE ONE:Basics of Applied Statistical ModellingIntroduction to the Instructor and CourseData & Code Used in the CourseStatistics in the Real WorldDesigning Studies & Collecting Good Quality DataDifferent Types of DataMODULE TWO: Essentials of the R Programming Rationale for this sectionIntroduction to the R Statistical Software & R StudioDifferent Data Structures in RReading in Data from Different SourcesIndexing and Subletting of DataData Cleaning: Removing Missing ValuesExploratory Data Analysis in RMODULE THREE: Statistical Tools Quantitative DataMeasures of CenterMeasures of VariationCharting & Graphing Continuous DataCharting & Graphing Discrete DataDeriving Insights from Qualitative/Nominal DataMODULE FOUR: Probability DistributionsData Distribution: Normal DistributionChecking For Normal DistributionStandard Normal Distribution and Z-scoresConfidence Interval-TheoryConfidence Interval-Computation in RMODULE FIVE: Statistical Inference Hypothesis TestingT-tests: Application in RNon-Parametric Alternatives to T-TestsOne-way ANOVANon-parametric version of One-way ANOVATwo-way ANOVAPower Test for Detecting EffectMODULE SIX: Relationship between Two Different Quantitative VariablesExplore the Relationship Between Two Quantitative VariablesCorrelationLinear Regression-TheoryLinear Regression-Implementation in RConditions of Linear RegressionMulti-collinearityLinear Regression and ANOVALinear Regression With Categorical Variables and Interaction TermsAnalysis of Covariance (ANCOVA)Selecting the Most Suitable Regression ModelViolation of Linear Regression Conditions: Transform VariablesOther Regression Techniques When Conditions of OLS Are Not MetRegression: Standardized Major Axis (SMA) RegressionPolynomial and Non-linear regressionLinear Mixed Effect ModelsGeneralized Regression Model (GLM)Logistic Regression in RPoisson Regression in RGoodness of fit testingMODULE SEVEN: Multivariate AnalysisIntroduction Multivariate AnalysisCluster Analysis/Unsupervised LearningPrincipal Component Analysis (PCA)Linear Discriminant Analysis (LDA)Correspondence AnalysisSimilarity & Dissimilarity Across SitesNon-metric multi-dimensional scaling (NMDS)Multivariate Analysis of Variance (MANOVA)General NotesAll our courses can be Tailor-made to participants needsThe participant must be conversant with EnglishPresentations are well guided, practical exercise, web based tutorials and group work. Our facilitators are expert with more than 10years of experience.Upon completion of training the participant will be issued with Foscore development center certificate (FDC-K)Training will be done at Foscore development center (FDC-K) center in Nairobi Kenya. We also offer more than five participants training at requested location within Kenya, more than ten participant within east Africa and more than twenty participant all over the world.Course duration is flexible and the contents can be modified to fit any number of days. Learn MoreFoscore Development Centre (FDC-K)Foscore Development Center Kenya (FDC-K) is a global training and consulting firm that has been assisting organizations and individuals to achieve their objectives and goals. We specialize in monitoring and evaluation, impact assessment of institutional, human capacity deve
Invitation to Quantitative Data Management and Analysis with R training
Title: Invitation to Quantitative Data Management and Analysis with R training Quantitative Data Management and Analysis with R course-Sep 28 to Oct 2,2020 for 5 DaysUse this link to Register online attendance or Normal attendance as individual or groupDownload PDF CalendarFoscore Development Center IntroductionThis course is designed for participants who plan to use R for the management, coding, analysis and visualization of qualitative data. The course’s content is spread over seven modules and includes: Basics of Applied Statistical Modelling, Essentials of the R Programming, Statistical Tools, Probability Distributions, Statistical Inference, Relationship between Two Different Quantitative Variables and Multivariate Analysis. The course is entirely hands-on and uses sample data to learn R basics and advanced features.DURATION5 daysWHO SHOULD ATTEND?Statistician, analyst, or a budding data scientist and beginners who want to learn how to analyze data with R,Course Objective:Analyze t data by applying appropriate statistical techniquesInterpret the statistical analysisIdentify statistical techniques a best suited to data and questionsStrong foundation in fundamental statistical conceptsImplement different statistical analysis in R and interpret the resultsBuild intuitive data visualizationsCarry out formalized hypothesis testingImplement linear modelling techniques such multiple regressions and GLMsImplement advanced regression analysis and multivariate analysisCourse contentMODULE ONE:Basics of Applied Statistical ModellingIntroduction to the Instructor and CourseData & Code Used in the CourseStatistics in the Real WorldDesigning Studies & Collecting Good Quality DataDifferent Types of DataMODULE TWO: Essentials of the R Programming Rationale for this sectionIntroduction to the R Statistical Software & R StudioDifferent Data Structures in RReading in Data from Different SourcesIndexing and Subletting of DataData Cleaning: Removing Missing ValuesExploratory Data Analysis in RMODULE THREE: Statistical Tools Quantitative DataMeasures of CenterMeasures of VariationCharting & Graphing Continuous DataCharting & Graphing Discrete DataDeriving Insights from Qualitative/Nominal DataMODULE FOUR: Probability DistributionsData Distribution: Normal DistributionChecking For Normal DistributionStandard Normal Distribution and Z-scoresConfidence Interval-TheoryConfidence Interval-Computation in RMODULE FIVE: Statistical Inference Hypothesis TestingT-tests: Application in RNon-Parametric Alternatives to T-TestsOne-way ANOVANon-parametric version of One-way ANOVATwo-way ANOVAPower Test for Detecting EffectMODULE SIX: Relationship between Two Different Quantitative VariablesExplore the Relationship Between Two Quantitative VariablesCorrelationLinear Regression-TheoryLinear Regression-Implementation in RConditions of Linear RegressionMulti-collinearityLinear Regression and ANOVALinear Regression With Categorical Variables and Interaction TermsAnalysis of Covariance (ANCOVA)Selecting the Most Suitable Regression ModelViolation of Linear Regression Conditions: Transform VariablesOther Regression Techniques When Conditions of OLS Are Not MetRegression: Standardized Major Axis (SMA) RegressionPolynomial and Non-linear regressionLinear Mixed Effect ModelsGeneralized Regression Model (GLM)Logistic Regression in RPoisson Regression in RGoodness of fit testingMODULE SEVEN: Multivariate AnalysisIntroduction Multivariate AnalysisCluster Analysis/Unsupervised LearningPrincipal Component Analysis (PCA)Linear Discriminant Analysis (LDA)Correspondence AnalysisSimilarity & Dissimilarity Across SitesNon-metric multi-dimensional scaling (NMDS)Multivariate Analysis of Variance (MANOVA)General NotesAll our courses can be Tailor-made to participants needsThe participant must be conversant with EnglishPresentations are well guided, practical exercise, web based tutorials and group work. Our facilitators are expert with more than 10years of experience.Upon completion of training the participant will be issued with Foscore development center certificate (FDC-K)Training will be done at Foscore development center (FDC-K) center in Nairobi Kenya. We also offer more than five participants training at requested location within Kenya, more than ten participant within east Africa and more than twenty participant all over the world.Course duration is flexible and the contents can be modified to fit any number of days. Learn MoreFoscore Development Centre (FDC-K)Foscore Development Center Kenya (FDC-K) is a global training and consulting firm that has been assisting organizations and individuals to achieve their objectives and goals. We specialize in monitoring and evaluation, impact assessment of institutional, human capacity deve
