Business analytics: data science for business problems
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
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Cham, Switzerland
Springer
[2021]
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 375-380 |
| Beschreibung: | xxxviii,, 387 Seiten Diagramme |
| ISBN: | 9783030870225 9783030870256 |
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| adam_text | Contents 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.................................................. 1.4.4 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.............................................. 3 4 5 7 9 10 10 15 21 24 25 27 28 30 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.................................................................................. 31 32 32 38 38 39 40 42 42 45 55 xiii
xiv 3 Contents Basic Data Handling .................................................................................... 3.1 CaseStudies........................................................................................ 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........................................... 3.3 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......................................................... 57 58 58 59 61 61 63 65 67 77 79 80 81 81 83 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.............................................. 4.4 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....................... 85 85 86 87 87 89 92 92 97 98 98 109 112 115 117 118 119 124 126 126 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........................................... 5.2.2 Patsy Encoding................................................................... 127 128 129 136 138 141 142 146
Contents 5.3 5.4 5.5 Part II XV 5.2.3 Label Encoding................................................................... 5.2.4 Binarizing Data................................................................... Dimension Reduction....................................................................... Handling Missing Data...................................................................... 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...................................... 147 147 150 151 153 154 154 155 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..................... 161 162 162 163 167 167 170 170 170 172 173 173 173 175 176 178 180 184 185 186 186 187 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........................................................ 189 189 190 191 193 193 194 195 196 196 198 200
xvi Contents 7.6 7.7 7.8 7.9 Time Series Generation Process: AR(1) Model. ............................ Visualization for AR( 1) Detection..................................................... Durbin-Watson Test Statistic ............................................................. Lagged Dependent and Independent Variables............................... 7.9.1 Lagged Independent Variable: ARDL(0, 1)....................... 7.9.2 Lagged Dependent Variable: ARDUI, 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......................................... 7.10.2 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.............................................. 200 203 204 210 211 211 211 211 214 219 221 222 223 223 224 224 225 225 227 227 228 229 231 233 237 238 245 247 249 249 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..................................... 253 253 255 256 261 262 263 264 264 265 266
Contents 9.6.2 Incorrect Use of Testing Data............................................ 9.6.3 Creating the Training/Testing Data Sets........................... 9.6.4 Recombining the Data Sets ............................................... Appendix.......................................................................................... 9.7.1 Primer on Random Numbers............................................. 268 269 275 276 276 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)....................................... 10.7 Panel Data Models............................................................................ 279 279 280 280 282 284 289 291 292 294 296 297 299 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.................................................. ! 1.2.3 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 К-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.................................... 313 314 314 315 315 316 319 320 322 328 330 333 333 333 336 337 337 339 9.7 10 11 xvii 300 301 301 302 303 309
Contents xviii 12 11.5 Decision Trees for Classification................... 11.5.1 Partitioning by Constants................. ! 1.5.2 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 337998 Classifier Accuracy Comparison ................. 11.7 339 343 344 348 350 351 351 353 353 355 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................................ 357 358 359 359 360 361 362 362 367 368 368 369 371 Bibliography......................................................................................................... 375 Index....................................................................................................................... 381
|
| adam_txt |
Contents 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. 1.4.4 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. 3 4 5 7 9 10 10 15 21 24 25 27 28 30 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. 31 32 32 38 38 39 40 42 42 45 55 xiii
xiv 3 Contents Basic Data Handling . 3.1 CaseStudies. 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. 3.3 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. 57 58 58 59 61 61 63 65 67 77 79 80 81 81 83 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. 4.4 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. 85 85 86 87 87 89 92 92 97 98 98 109 112 115 117 118 119 124 126 126 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. 5.2.2 Patsy Encoding. 127 128 129 136 138 141 142 146
Contents 5.3 5.4 5.5 Part II XV 5.2.3 Label Encoding. 5.2.4 Binarizing Data. Dimension Reduction. Handling Missing Data. 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. 147 147 150 151 153 154 154 155 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. 161 162 162 163 167 167 170 170 170 172 173 173 173 175 176 178 180 184 185 186 186 187 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. 189 189 190 191 193 193 194 195 196 196 198 200
xvi Contents 7.6 7.7 7.8 7.9 Time Series Generation Process: AR(1) Model. . Visualization for AR( 1) Detection. Durbin-Watson Test Statistic . Lagged Dependent and Independent Variables. 7.9.1 Lagged Independent Variable: ARDL(0, 1). 7.9.2 Lagged Dependent Variable: ARDUI, 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. 7.10.2 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. 200 203 204 210 211 211 211 211 214 219 221 222 223 223 224 224 225 225 227 227 228 229 231 233 237 238 245 247 249 249 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. 253 253 255 256 261 262 263 264 264 265 266
Contents 9.6.2 Incorrect Use of Testing Data. 9.6.3 Creating the Training/Testing Data Sets. 9.6.4 Recombining the Data Sets . Appendix. 9.7.1 Primer on Random Numbers. 268 269 275 276 276 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). 10.7 Panel Data Models. 279 279 280 280 282 284 289 291 292 294 296 297 299 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. ! 1.2.3 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 К-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. 313 314 314 315 315 316 319 320 322 328 330 333 333 333 336 337 337 339 9.7 10 11 xvii 300 301 301 302 303 309
Contents xviii 12 11.5 Decision Trees for Classification. 11.5.1 Partitioning by Constants. ! 1.5.2 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 337998 Classifier Accuracy Comparison . 11.7 339 343 344 348 350 351 351 353 353 355 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. 357 358 359 359 360 361 362 362 367 368 368 369 371 Bibliography. 375 Index. 381 |
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| id | DE-604.BV047710865 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:00:38Z |
| indexdate | 2024-07-10T09:19:49Z |
| institution | BVB |
| isbn | 9783030870225 9783030870256 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033094634 |
| oclc_num | 1302312613 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-573 DE-521 DE-92 |
| owner_facet | DE-355 DE-BY-UBR DE-573 DE-521 DE-92 |
| physical | xxxviii,, 387 Seiten Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Springer |
| record_format | marc |
| spelling | Paczkowski, Walter R. ca. 20./21. Jh. Verfasser (DE-588)116780757X aut Business analytics data science for business problems Walter R. Paczkowski Cham, Switzerland Springer [2021] © 2021 xxxviii,, 387 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis Seite 375-380 Datenanalyse (DE-588)4123037-1 gnd rswk-swf Datenmanagement (DE-588)4213132-7 gnd rswk-swf Betriebsdaten (DE-588)4145038-3 gnd rswk-swf Betriebliches Informationssystem (DE-588)4069386-7 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf Unternehmen (DE-588)4061963-1 s Betriebsdaten (DE-588)4145038-3 s Datenanalyse (DE-588)4123037-1 s DE-604 Betriebliches Informationssystem (DE-588)4069386-7 s Datenmanagement (DE-588)4213132-7 s Erscheint auch als Online-Ausgabe 978-3-030-87023-2 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033094634&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Paczkowski, Walter R. ca. 20./21. Jh Business analytics data science for business problems Datenanalyse (DE-588)4123037-1 gnd Datenmanagement (DE-588)4213132-7 gnd Betriebsdaten (DE-588)4145038-3 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Unternehmen (DE-588)4061963-1 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4213132-7 (DE-588)4145038-3 (DE-588)4069386-7 (DE-588)4061963-1 |
| title | Business analytics data science for business problems |
| title_auth | Business analytics data science for business problems |
| title_exact_search | Business analytics data science for business problems |
| title_exact_search_txtP | Business analytics data science for business problems |
| title_full | Business analytics data science for business problems Walter R. Paczkowski |
| title_fullStr | Business analytics data science for business problems Walter R. Paczkowski |
| title_full_unstemmed | Business analytics data science for business problems Walter R. Paczkowski |
| title_short | Business analytics |
| title_sort | business analytics data science for business problems |
| title_sub | data science for business problems |
| topic | Datenanalyse (DE-588)4123037-1 gnd Datenmanagement (DE-588)4213132-7 gnd Betriebsdaten (DE-588)4145038-3 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Unternehmen (DE-588)4061963-1 gnd |
| topic_facet | Datenanalyse Datenmanagement Betriebsdaten Betriebliches Informationssystem Unternehmen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033094634&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT paczkowskiwalterr businessanalyticsdatascienceforbusinessproblems |