Business analytics : data science for business problems / Walter R. Paczkowski.

Format:

• Book

Author/Creator:

• Paczkowski, Walter R., author.

Series:

• Mathematics and Statistics Series

Language:

English

Subjects (All):

• Business information services.
• Mathematical statistics.

Physical Description:

1 online resource (416 pages)

Edition:

1st ed.

Place of Publication:

Cham, Switzerland : Springer International Publishing, [2022]

Summary:

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems.These are a foundational understanding of: 1.statistical, econometric, and machine learning techniques; 2.data handling capabilities; 3.at least one programming language.

Contents:

• Intro
• Preface
• The Book's Focus
• The Target Audience
• The Book's Competitive Comparison
• The Book's Structure
• Acknowledgments
• Contents
• List of Figures
• List of Tables
• Part I Beginning Analytics
• 1 Introduction to Business Data Analytics: Setting the Stage
• 1.1 Types of Business Problems
• 1.2 The Role of Information in Business Decision Making
• 1.3 Uncertainty vs. Risk
• 1.4 The Data-Information Nexus
• 1.4.1 Data and Information Confusion
• 1.4.2 The Data Component
• 1.4.3 The Extractor Component
• 1.4.3.1 Text Data
• 1.4.3.2 Numeric Data
• 1.4.3.3 Data: A Combined View
• 1.4.4 The Information Component
• 1.5 Analytics Requirements
• 1.5.1 Theoretical Framework
• 1.5.2 Data Handling
• 1.5.3 Programming Literacy
• 1.5.4 Component Interconnections
• 2 Data Sources, Organization, and Structures
• 2.1 Data Dimensions: A Taxonomy for Defining Data
• 2.1.1 Taxonomy Component #1: Source
• 2.1.2 Taxonomy Component #2: Domain
• 2.1.3 Taxonomy Component #3: Levels
• 2.1.4 Taxonomy Component #4: Continuity
• 2.1.5 Taxonomy Component #5: Measurement Scale
• 2.2 Data Organization
• 2.2.1 External Database Structures
• 2.2.2 Internal Database Structures
• 2.3 Data Dictionary
• 3 Basic Data Handling
• 3.1 Case Studies
• 3.1.1 Case Study 1: Customer Transactions Data
• 3.1.2 Case Study 2: Measures of Order Fulfillment
• 3.2 Importing Your Data
• 3.2.1 Data Formats
• 3.2.2 Importing a CSV Text File into Pandas
• 3.2.3 Importing Large Files in Chunks
• 3.2.4 Checking Your Imported Data
• 3.2.4.1 Check #1: Display the First Few Records
• 3.2.4.2 Check #2: Check the Shape of the DataFrame
• 3.2.4.3 Check #3: Check Column Names
• 3.2.4.4 Check #4: Check for Missing Values
• 3.2.4.5 Check #5: Check the Data Types
• 3.3 Merging or Joining DataFrames
• 3.4 Reshaping DataFrames.
• 3.5 Sorting a DataFrame
• 3.6 Querying a DataFrame
• 3.6.1 Boolean Operators and Indicator Functions
• 3.6.2 Pandas Query Method
• 4 Data Visualization: The Basics
• 4.1 Background for Data Visualization
• 4.2 Gestalt Principles of Visual Design
• 4.3 Issues Complicating Data Visualization
• 4.3.1 Human Visual Limitations
• 4.3.2 Data Visualization Tools
• 4.3.3 Types of Visuals
• 4.3.4 What to Look for in a Graph
• 4.3.4.1 Feature #1: Distributions
• 4.3.4.2 Feature #2: Relationships
• 4.3.4.3 Feature #3: Patterns
• 4.3.4.4 Feature #4: Trends
• 4.3.4.5 Feature #5: Anomalies
• 4.4 Visualizing Spatial Data
• 4.4.1 Data Preparation
• 4.4.2 Visualizing Continuous Spatial Data
• 4.4.3 Visualizing Categorical Spatial Data
• 4.4.4 Visualizing Continuous and Categorical Spatial Data
• 4.5 Visualizing Temporal (Time Series) Data
• 4.5.1 Properties of Temporal (Time Series) Data
• 4.5.2 Visualizing Time Series Data
• 4.5.3 Times Series Complications
• 4.6 Faceted Plots
• 4.7 Appendix
• 4.7.1 Taylor Series Expansion for Growth Rates
• 5 Advanced Data Handling: Preprocessing Methods
• 5.1 Transformations
• 5.1.1 Linear Transformations
• 5.1.2 Nonlinear Transformations
• 5.1.3 A Family of Transformations
• 5.2 Encoding
• 5.2.1 Dummy or One-Hot Encoding
• 5.2.1.1 Pandas Dummy Encoding
• 5.2.1.2 sklearn Dummy Encoding
• 5.2.2 Patsy Encoding
• 5.2.3 Label Encoding
• 5.2.4 Binarizing Data
• 5.3 Dimension Reduction
• 5.4 Handling Missing Data
• 5.5 Appendix
• 5.5.1 Mean and Variance of Standardized Variable
• 5.5.2 Mean and Variance of Adjusted Standardized Variable
• 5.5.3 Unbiased Estimators of μ and σ2
• Part II Intermediate Analytics
• 6 OLS Regression: The Basics
• 6.1 Basic OLS Concept
• 6.1.1 The Disturbance Term and the Residual
• 6.1.2 OLS Estimation
• 6.1.3 The Gauss-Markov Theorem.
• 6.2 Analysis of Variance
• 6.3 Case Study
• 6.3.1 Basic OLS Regression
• 6.3.2 The Log-Log Model
• 6.3.3 Model Set-up
• 6.3.4 Estimation Summary
• 6.3.5 ANOVA for Basic Regression
• 6.3.6 Elasticities
• 6.4 Basic Multiple Regression
• 6.4.1 ANOVA for Multiple Regression
• 6.4.2 Alternative Measures of Fit: AIC and BIC
• 6.5 Case Study: Expanded Analysis
• 6.6 Model Portfolio
• 6.7 Predictive Analysis: Introduction
• 6.7.1 Predicting vs. Forecasting
• 6.7.2 Developing a Prediction
• 6.7.3 Simulation Tool for Prediction Application
• 7 Time Series Analysis
• 7.1 Time Series Basics
• 7.1.1 Time Series Definition
• 7.1.2 Time Series Concepts
• 7.2 Importing a Date/Time Variable
• 7.3 The Data Cube and Time Series Data
• 7.4 Handling Dates and Times in Python and Pandas
• 7.4.1 Datetimes vs. Periods
• 7.4.2 Aggregating Datetime Measures
• 7.4.3 Converting Time Periods in Pandas
• 7.4.4 Date-Time Mini-Language
• 7.5 Some Calendrical Calculations
• 7.6 Time Series Generation Process: AR(1) Model
• 7.7 Visualization for AR(1) Detection
• 7.8 Durbin-Watson Test Statistic
• 7.9 Lagged Dependent and Independent Variables
• 7.9.1 Lagged Independent Variable: ARDL(0, 1)
• 7.9.2 Lagged Dependent Variable: ARDL(1, 0)
• 7.9.3 Lagged Dependent and Independent Variables:ARDL(1, 1)
• 7.10 Further Exploration of Time Series Analysis
• 7.10.1 Step 1: Identification of a Model
• 7.10.1.1 AR(p) Model
• 7.10.1.2 MA(q) Model
• 7.10.1.3 ARMA(p, q) Model
• 7.10.1.4 ARIMA(p, d, q) Model
• 7.10.1.5 Digression: Time Series Stationarity-An Overview
• 7.10.2 Step 2: Estimation of the Model
• 7.10.3 Step 3: Validation of the Model
• 7.10.4 Step 4: Forecasting with the Model
• 7.11 Appendix
• 7.11.1 Backshift Operator
• 7.11.2 Useful Algebra Results
• 7.11.3 Mean and Variance of Yt
• 7.11.4 Demeaned Data
• 7.11.5 Time Trend Addition.
• 8 Statistical Tables
• 8.1 Data Preprocessing
• 8.2 Categorical Data
• 8.3 Creating a Frequency Table
• 8.4 Hypothesis Testing: A First Step
• 8.5 Cross-tabs and Hypothesis Tests
• 8.5.1 Hypothesis Testing
• 8.5.2 Plotting a Frequency Table
• 8.6 Extending the Cross-tab
• 8.7 Pivot Tables
• 8.8 Appendix
• 8.8.1 Pearson Chi-Square Statistic
• Part III Advanced Analytics
• 9 Advanced Data Handling for Business Data Analytics
• 9.1 Supervised and Unsupervised Learning
• 9.2 Working with the Data Cube
• 9.3 The Data Cube and DataFrame Indexing
• 9.4 Sampling From a DataFrame
• 9.4.1 Simple Random Sampling (SRS)
• 9.4.2 Stratified Random Sampling
• 9.4.3 Cluster Random Sampling
• 9.5 Index Sorting of a DataFrame
• 9.6 Splitting a DataFrame: The Train-Test Splits
• 9.6.1 Model Tuning of Hyperparameters
• 9.6.2 Incorrect Use of Testing Data
• 9.6.3 Creating the Training/Testing Data Sets
• 9.6.3.1 Comment on Strategy
• 9.6.3.2 Handling Cross-Sectional Data
• 9.6.3.3 Handling Time Series Data
• 9.6.3.4 Handling Panel Data
• 9.6.4 Recombining the Data Sets
• 9.7 Appendix
• 9.7.1 Primer on Random Numbers
• 10 Advanced OLS for Business Data Analytics
• 10.1 Link Functions: An Introduction
• 10.2 Data Preprocessing
• 10.2.1 Data Standardization for Regression Analysis
• 10.2.2 One-Hot and Effects (or Sum) Encoding
• 10.3 Case Study Application
• 10.4 Heteroskedasticity Issues and Tests
• 10.4.1 Heteroskedasticity Problem
• 10.4.2 Heteroskedasticity Detection
• 10.4.3 Heteroskedasticity Remedy
• 10.5 Multicollinearity
• 10.5.1 Digression on Multicollinearity
• 10.5.2 Detection with VIF and the Condition Index
• 10.5.3 Principal Component Regression and High-Dimensional Data
• 10.6 Predictions and Scenario Analysis
• 10.6.1 Making Predictions
• 10.6.2 Scenario Analysis
• 10.6.3 Prediction Error Analysis (PEA).
• 10.6.3.1 LOOCV Approach
• 10.6.3.2 k-Fold Approach
• 10.6.3.3 Score Measures
• 10.6.3.4 Variations on Validation Methods
• 10.6.3.5 Complexity of Testing
• 10.6.3.6 Examples of k-Fold Split
• 10.7 Panel Data Models
• 11 Classification with Supervised Learning Methods
• 11.1 Case Study: Background
• 11.2 Logistic Regression
• 11.2.1 A Choice Interpretation
• 11.2.2 Properties of this Problem
• 11.2.3 A Model for the Binary Problem
• 11.2.4 Case Study: Train-Test Data Split
• 11.2.5 Case Study: Logit Model Training
• 11.2.6 Making and Assessing Predictions
• 11.2.7 Classification with a Logit Model
• 11.3 K-Nearest Neighbor (KNN)
• 11.3.1 Case Study: Predicting
• 11.4 Naive Bayes
• 11.4.1 Background: Bayes Theorem
• 11.4.2 A General Statement
• 11.4.3 The Naive Adjective: A Simplifying Assumption
• 11.4.4 Distribution Assumptions
• 11.4.5 Case Study: Naive Bayes Training
• 11.5 Decision Trees for Classification
• 11.5.1 Partitioning by Constants
• 11.5.2 Gini Index and Entropy
• 11.5.3 Case Study: Growing a Tree
• 11.5.4 Case Study: Predicting with a Tree
• 11.5.5 Random Forests
• 11.6 Support Vector Machines
• 11.6.1 Case Study: SVC Application
• 11.6.2 Case Study: Prediction
• 11.7 Classifier Accuracy Comparison
• 12 Grouping with Unsupervised Learning Methods
• 12.1 Training and Testing Data Sets
• 12.2 Hierarchical Clustering
• 12.2.1 Forms of Hierarchical Clustering
• 12.2.2 Agglomerative Algorithm Description
• 12.2.3 Metrics and Linkages
• 12.2.4 Preprocessing Data
• 12.2.5 Case Study Application
• 12.2.6 Examining More than One Solution
• 12.3 K-Means Clustering
• 12.3.1 Algorithm Description
• 12.3.2 Case Study Application
• 12.4 Mixture Model Clustering
• Bibliography
• Index.

Notes:

• Description based on print version record.
• Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Paczkowski, Walter R. Business Analytics

ISBN:

9783030870232

OCLC:

1295273876