UID:
almahu_9949576280302882
Format:
1 online resource (386 pages)
Edition:
1st ed.
ISBN:
9783031286742
Note:
Intro -- Foreword -- Preface -- Contents -- Getting Started -- Guiding Principles -- Pro-Employee Thinking -- Quality -- Prioritization -- Tooling -- Data Sets -- Employees -- Turnover Trends -- Survey Responses -- 4D Framework -- Introduction to R -- Getting Started -- Installing R -- Installing R Studio -- Installing Packages -- Loading Data -- Case Sensitivity -- Help -- Objects -- Comments -- Testing Early and Often -- Vectors -- Vectorized Operations -- Matrices -- Factors -- Data Frames -- Lists -- Loops -- User-Defined Functions (UDFs) -- Graphics -- Review Questions -- Introduction to SQL -- Basics -- Aggregate Functions -- Joins -- Subqueries -- Virtual Tables -- Window Functions -- Common Table Expressions (CTEs) -- Review Questions -- Research Design -- Research Questions -- Research Hypotheses -- Internal and External Validity -- Research Methods -- Research Designs -- Experimental Research -- Quasi-Experimental Research -- Non-Experimental Research -- Review Questions -- Measurement and Sampling -- Variable Types -- Independent Variables (IV) -- Dependent Variables (DV) -- Control Variables (CV) -- Moderating Variables -- Mediating Variables -- Endogenous vs. Exogenous Variables -- Measurement Scales -- Discrete Variables -- Nominal -- Ordinal -- Continuous Variables -- Interval -- Ratio -- Sampling Methods -- Probability Sampling -- Simple Random Sampling -- Stratified Random Sampling -- Cluster Sampling -- Systematic Sampling -- Non-Probability Sampling -- Convenience (Accidental) Sampling -- Quota Sampling -- Purposive (Judgmental) Sampling -- Sampling and Nonsampling Error -- Sampling Error -- Selection Bias -- Nonsampling Error -- Nonresponse Bias -- Nontruthful Responses -- Measurement Error -- Scale Reliability and Validity -- Reliability -- Validity -- Face validity -- Content Validity -- Construct Validity.
,
Criterion-Related Validity -- Review Questions -- Data Preparation -- Data Extraction -- Data Architecture -- Data Lake -- Data Warehouse -- Data Mart -- Database Normalization -- Modern Data Infrastructure -- Data Screening and Cleaning -- Missingness -- Outliers -- Low Variability -- Inconsistent Categories -- Data Binning -- One-Hot Encoding -- Feature Engineering -- Review Questions -- Descriptive Statistics -- Univariate Analysis -- Measures of Central Tendency -- Mean -- Median -- Mode -- Range -- Measures of Spread -- Variance -- Standard Deviation -- Quartiles -- Skewness -- Kurtosis -- Bivariate Analysis -- Covariance -- Correlation -- Review Questions -- Statistical Inference -- Introduction to Probability -- Probability Distributions -- Discrete Probability Distributions -- Continuous Probability Distributions -- Conditional Probability -- Central Limit Theorem -- Confidence Intervals -- Hypothesis Testing -- Alpha -- Type I & -- II Errors -- p-Values -- Bonferroni Correction -- Statistical Power -- Review Questions -- Analysis of Differences -- Parametric vs. Nonparametric Tests -- Differences in Discrete Data -- Chi-Square Test -- Fisher's Exact Test -- Differences in Continuous Data -- Independent Samples t-Test -- Mann-Whitney U Test -- Paired Samples t-Test -- Wilcoxon Signed-Rank Test -- Analysis of Variance (ANOVA) -- Factorial ANOVA -- Review Questions -- Linear Regression -- Assumptions and Diagnostics -- Sample Size -- Simple Linear Regression -- Multiple Linear Regression -- Collinearity Diagnostics -- Variable Selection -- Moderation -- Mediation -- Review Questions -- Linear Model Extensions -- Model Comparisons -- Hierarchical Regression -- Multilevel Models -- Polynomial Regression -- Review Questions -- Logistic Regression -- Binomial Logistic Regression -- Multinomial Logistic Regression -- Ordinal Logistic Regression.
,
Review Questions -- Predictive Modeling -- Cross-Validation -- Validation Set Approach -- Leave-One-Out -- k-Fold -- Model Performance -- Classification -- Forecasting -- Bias-Variance Tradeoff -- Tree-Based Algorithms -- Decision Trees -- Random Forests -- Predictive Modeling -- Classification -- Forecasting -- Review Questions -- Unsupervised Learning -- Factor Analysis -- Exploratory Factor Analysis (EFA) -- Confirmatory Factor Analysis (CFA) -- Clustering -- K-Means Clustering -- Hierarchical Clustering -- Review Questions -- Data Visualization -- Best Practices -- Color Palette -- Chart Borders -- Zero Baseline -- Intuitive Layout -- Preattentive Attributes -- Step-by-Step Visual Upgrade -- Step 1: Build Bar Chart with Defaults -- Step 2: Remove Legend -- Step 3: Assign Colors Strategically -- Step 4: Add Axis Titles and Margins -- Step 5: Add Left-Justified Title -- Step 6: Remove Background -- Step 7: Remove Axis Ticks -- Step 8: Mute Titles -- Step 9: Flip Axes -- Step 10: Sort Data -- Visualization Types -- Tables -- Heatmaps -- Scatterplots -- Line Graphs -- Slopegraphs -- Bar Charts -- Combination Charts -- Waterfall Charts -- Waffle Charts -- Sankey Diagrams -- Pie Charts -- 3D Visuals -- Elegant Data Visualization -- Review Questions -- Data Storytelling -- Know Your Audience -- Production Status -- Structural Elements -- TL -- DR -- Purpose -- Methodology -- Results -- Limitations -- Next Steps -- The Appendix -- Q& -- A -- Review Questions -- Appendix -- Appendix -- 4D Framework -- Discover -- Client -- Primary Objective -- Problem Statement -- Guiding Theories -- Research Questions -- Research Hypotheses -- Assumptions -- Cadence -- Aggregation -- Deliverable -- Filters and Dimensions -- Design -- Data Privacy -- Data Sources and Elements -- Data Quality -- Variables -- Analysis Method -- Dependencies -- Change Management.
,
Sign-Off -- Develop -- Development Patterns -- Productionalizable Code -- Unit Testing -- User Acceptance Testing (UAT) -- Deliver -- Data Visualization -- Step-by-Step Visual Upgrade -- Tables -- Heatmaps -- Scatterplots -- Line Charts -- Slopegraphs -- Bar Charts -- Combination Charts -- Waterfall Charts -- Waffle Charts -- Sankey Diagrams -- Pie Charts -- -- Bibliography -- Index.
Additional Edition:
Print version: Starbuck, Craig The Fundamentals of People Analytics Cham : Springer International Publishing AG,c2023 ISBN 9783031286735
Language:
English
Keywords:
Electronic books.
