Kooperativer Bibliotheksverbund

UID:

Umfang: 1 Online-Ressource.

ISBN: 978-3-031-28674-2

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-28673-5

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-28676-6

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften

DOI: 10.1007/978-3-031-28674-2

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Umfang: 1 Online-Ressource.

ISBN: 978-3-031-28674-2

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-28673-5

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-28676-6

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften

DOI: 10.1007/978-3-031-28674-2

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Umfang: 1 Online-Ressource.

ISBN: 978-3-031-28674-2

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-28673-5

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-28676-6

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften

DOI: 10.1007/978-3-031-28674-2

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Umfang: 1 Online-Ressource

ISBN: 9783031286742

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-28673-5

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-28676-6

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften

DOI: 10.1007/978-3-031-28674-2

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Umfang: 1 online resource (XVI, 380 p. 1 illus.)

Ausgabe: 1st ed. 2023.

Inhalt: Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. This book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholders This is an open access book. This comprehensive volume combines relevant people analytics examples, comprehensive treatment of statistical and analytical considerations and approaches, and useful sample code into a readable and relevant package. This is an essential guide for those in the People Analytics space looking to build their analytical capabilities. - Alexis Fink, PhD, VP of People Analytics & Workforce Strategy @ Meta.

Anmerkung: 1. Getting Started -- 2. Introduction to R -- 3. Introduction to SQL -- 4. Research Design -- 5. Measurement & Sampling -- 6. Data Preparation -- 7. Descriptive Statistics -- 8. Statistical Inference -- 9. Analysis of Differences -- 10. Linear Regression -- 11. Linear Model Extensions -- 12. Logistic Regression -- 13. Predictive Modeling -- 14. Unsupervised Learning -- 15. Data Visualization -- 16. Data Storytelling.

Weitere Ausg.: ISBN 3-031-28673-1

Sprache: Englisch

Schlagwort(e): Llibres electrònics

DOI: 10.1007/978-3-031-28674-2

UID:

Umfang: 1 online resource (XVI, 380 p. 1 illus.)

Ausgabe: 1st ed. 2023.

Inhalt: Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. This book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholders This is an open access book. This comprehensive volume combines relevant people analytics examples, comprehensive treatment of statistical and analytical considerations and approaches, and useful sample code into a readable and relevant package. This is an essential guide for those in the People Analytics space looking to build their analytical capabilities. - Alexis Fink, PhD, VP of People Analytics & Workforce Strategy @ Meta.

Anmerkung: 1. Getting Started -- 2. Introduction to R -- 3. Introduction to SQL -- 4. Research Design -- 5. Measurement & Sampling -- 6. Data Preparation -- 7. Descriptive Statistics -- 8. Statistical Inference -- 9. Analysis of Differences -- 10. Linear Regression -- 11. Linear Model Extensions -- 12. Logistic Regression -- 13. Predictive Modeling -- 14. Unsupervised Learning -- 15. Data Visualization -- 16. Data Storytelling.

Weitere Ausg.: ISBN 3-031-28673-1

Sprache: Englisch

Schlagwort(e): Llibres electrònics

DOI: 10.1007/978-3-031-28674-2

URL: https://doi.org/10.1007/978-3-031-28674-2

URL: kostenfrei

UID:

Umfang: 1 online resource (386 pages)

Ausgabe: 1st ed.

ISBN: 9783031286742

Anmerkung: 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.

Weitere Ausg.: Print version: Starbuck, Craig The Fundamentals of People Analytics Cham : Springer International Publishing AG,c2023 ISBN 9783031286735

Sprache: Englisch

Schlagwort(e): Electronic books.

URL: Click to View

URL: lizenzpflichtig

UID:

Umfang: 1 Online-Ressource (380 p.)

ISBN: 9783031286742 , 9783031286735

Inhalt: This open access book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations. Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholders

Anmerkung: English

Sprache: Unbestimmte Sprache

URL: kostenfrei

UID:

Umfang: 1 online resource (xvi, 380 pages)

ISBN: 9783031286742 , 303128674X

Inhalt: Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. This book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholders This is an open access book. This comprehensive volume combines relevant people analytics examples, comprehensive treatment of statistical and analytical considerations and approaches, and useful sample code into a readable and relevant package. This is an essential guide for those in the People Analytics space looking to build their analytical capabilities. - Alexis Fink, PhD, VP of People Analytics & Workforce Strategy @ Meta.

Anmerkung: 1. Getting Started -- 2. Introduction to R -- 3. Introduction to SQL -- 4. Research Design -- 5. Measurement & Sampling -- 6. Data Preparation -- 7. Descriptive Statistics -- 8. Statistical Inference -- 9. Analysis of Differences -- 10. Linear Regression -- 11. Linear Model Extensions -- 12. Logistic Regression -- 13. Predictive Modeling -- 14. Unsupervised Learning -- 15. Data Visualization -- 16. Data Storytelling.

Weitere Ausg.: ISBN 3031286766

Weitere Ausg.: ISBN 9783031286766

Weitere Ausg.: ISBN 3031286731

Weitere Ausg.: ISBN 9783031286735

Sprache: Englisch

Schlagwort(e): Electronic books.

DOI: 10.1007/978-3-031-28674-2

URL: SpringerLink

URL: Click here to view book

UID:

Umfang: 1 online resource (XVI, 380 p. 1 illus.)

Ausgabe: 1st ed. 2023.

Inhalt: Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. This book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholders This is an open access book. This comprehensive volume combines relevant people analytics examples, comprehensive treatment of statistical and analytical considerations and approaches, and useful sample code into a readable and relevant package. This is an essential guide for those in the People Analytics space looking to build their analytical capabilities. - Alexis Fink, PhD, VP of People Analytics & Workforce Strategy @ Meta.

Anmerkung: 1. Getting Started -- 2. Introduction to R -- 3. Introduction to SQL -- 4. Research Design -- 5. Measurement & Sampling -- 6. Data Preparation -- 7. Descriptive Statistics -- 8. Statistical Inference -- 9. Analysis of Differences -- 10. Linear Regression -- 11. Linear Model Extensions -- 12. Logistic Regression -- 13. Predictive Modeling -- 14. Unsupervised Learning -- 15. Data Visualization -- 16. Data Storytelling.

Weitere Ausg.: ISBN 3-031-28673-1

Sprache: Englisch

Schlagwort(e): Llibres electrònics

DOI: 10.1007/978-3-031-28674-2