Modern survey analytics: using python for deeper insights
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2022]
|
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxvi, 347 Seiten Illustrationen |
| ISBN: | 9783030762667 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047482432 | ||
| 003 | DE-604 | ||
| 005 | 20221021 | ||
| 007 | t | ||
| 008 | 210924s2022 a||| |||| 00||| eng d | ||
| 020 | |a 9783030762667 |9 978-3-030-76266-7 | ||
| 035 | |a (OCoLC)1349538940 | ||
| 035 | |a (DE-599)BVBBV047482432 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-355 | ||
| 084 | |a QH 244 |0 (DE-625)141558: |2 rvk | ||
| 100 | 1 | |a Paczkowski, Walter R. |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)116780757X |4 aut | |
| 245 | 1 | 0 | |a Modern survey analytics |b using python for deeper insights |c Walter R. Paczkowski |
| 264 | 1 | |a Cham |b Springer |c [2022] | |
| 300 | |a xxvi, 347 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-030-76267-4 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032883898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032883898 | ||
Datensatz im Suchindex
| _version_ | 1804182801995530240 |
|---|---|
| adam_text | Contents 1 Introduction to Modem Survey Analytics ................................................ 1.1 Information and Survey Data................................................................. 1.2 Demystifying Surveys ............................................................................ 1.2.1 Survey Objectives...................................................................... 1.2.2 Target Audience and Sample Size............................................ 1.2.2.1 Key Parameters to Estimate...................................... 1.2.2.2 Sample Design to Use............................................... 1.2.2.3 Population Size.......................................................... 1.2.2.4 Alpha.......................................................................... 1.2.2.5 Margin of Error.......................................................... 1.2.2.6 Additional Information............................................. 1.2.3 Screener and Questionnaire Design.......................................... 1.2.4 Fielding the Study...................................................................... 1.2.5 Data Analysis.............................................................................. 1.2.6 Report Writing and Presentation .............................................. 1.3 Sample Representativeness.................................................................... 1.3.1 Digression on Indicator Variables............................................. 1.3.2 Calculating the Population Parameters..................................... 1.4 Estimating Population
Parameters ......................................................... 1.5 Case Studies............................................................................................. 1.5.1 Consumer Study: Yogurt Consumption.................................... 1.5.2 Public Sector Study: VA Benefits Survey ................................. 1.5.3 Public Opinion Study: Toronto Casino Opinion Survey.......... 1.5.4 Public Opinion Study: San Francisco Airport Customer Satisfaction Survey .................................................. 1.6 Why Use Python for Survey Data Analysis?....................................... 1.7 Why Use Jupyter for Survey Data Analysis?....................................... 1 3 4 5 7 9 9 10 10 10 10 12 14 14 16 16 20 21 22 25 25 27 28 30 30 32 xiii
xiv 2 Contents First Step: Working with Survey Data........................................................ 2.1 Best Practices: First Steps to Analysis.................................................... 2.1.1 Installing and Importing Python Packages.............................. 2.1.2 Organizing Routinely Used Packages, Functions, and Formats..................................................................... 39 2.1.3 Defining Data Paths and File Names........................................ 2.1.4 Defining Your Functions and Formatting Statements............. 2.1.5 Documenting Your Data with a Dictionary............................. 2.2 Importing Your Data with Pandas........................................................... 2.3 Handling Missing Values......................................................................... 2.3.1 Identifying Missing Values ....................................................... 2.3.2 Reporting Missing Values........................................................... 2.3.3 Reasons for Missing Values........................................................ 2.3.4 Dealing with Missing Values..................................................... 2.3.4.1 Use the fillna( ) Method............................................. 2.3.4.2 Use the Interpolation ) Method............................... 2.3.4.3 An Even More Sophisticated Method....................... 2.4 Handling Special Types of Survey Data................................................ 2.4.1 CATA Questions.......................................................................... 2.4.1.1
Multiple Responses..................................................... 2.4.1.2 Multiple Responses by ID.......................................... 2.4.1.3 Multiple Responses Delimited .................................. 2.4.1.4 Indicator Variable........................................................ 2.4.1.5 Frequencies ................................................................. 2.4.2 Categorical Questions................................................................ 2.5 Creating New Variables, Binning, and Rescaling ................................ 2.5.1 Creating Summary Variables .................................................... 2.5.2 Rescaling .................................................................................... 2.5.3 Other Forms of Preprocessing.................................................. 2.6 Knowing the Structure of the Data Using Simple Statistics ............... 2.6.1 Descriptive Statistics and DataFrame Checks.......................... 2.6.2 Obtaining Value Counts............................................................. 2.6.3 Styling Your DataFrame Display.............................................. 2.7 Weight Calculations................................................................................ 2.7.1 Complex Weight Calculation: Raking...................................... 2.7.2 Types of Weights........................................................................ 2.8 Querying Data......................................................................................... 3 35 36 36 41 42 42 43 48 49 49 50 51 51 51 52 52 52
53 53 54 54 54 54 56 58 62 64 67 68 69 69 70 73 75 80 Shallow Survey Analysis............................................................................... 83 3.1 Frequency Summaries............................................................................ 84 3.1.1 Ordinal-Based Summaries......................................................... 85 3.1.2 Nominal-Based Summaries....................................................... 86 3.2 Basic Descriptive Statistics.................................................................... 86 3.3 Cross-Tabulations.................................................................................... 89
Contents 3.4 3.5 4 XV Data Visualization................................................................................... 3.4.1 Visuals Best Practice.................................................................. 3.4.2 Data Visualization Background................................................. 3.4.3 Pie Charts.................................................................................... 3.4.4 BarCharts .................................................................................. 3.4.5 Other Charts and Graphs........................................................... 3.4.5.1 Histograms and Boxplots for Distributions.............. 3.4.5.2 Mosaic Charts............................................................ 3.4.5.3 Heatmaps.................................................................... Weighted Summaries: Crosstabs and Descriptive Statistics ............... Beginning Deep Survey Analysis.................................................................... 113 Hypothesis Testing.................................................................................. 4.1.1 Hypothesis Testing Background............................................... 4.1.2 Examples of Hypotheses........................................................... 4.1.3 A Formal Framework for Statistical Tests .............................. 4.1.4 A Less Formal Framework for Statistical Tests....................... 4.1.5 Types of Tests to Use................................................................. 4.2 Quantitative Data: Tests of Means
........................................................ 4.2.1 Test of One Mean....................................................................... 4.2.2 Test of Two Means for Two Populations................................. 4.2.2.1 Standard Errors: Independent Populations .............. 4.2.2.2 Standard Errors: Dependent Populations................. 4.2.3 Test of More Than Two Means................................................. 4.3 Categorical Data: Tests of Proportions.................................................. 4.3.1 Single Proportions...................................................................... 4.3.2 Comparing Proportions: Two IndependentPopulations........... 4.3.3 Comparing Proportions: Paired Populations............................ 4.3.4 Comparing Multiple Proportions.............................................. 4.4 Advanced Tabulations ............................................................................ 4.5 Advanced Visualization........................................................................... 4.5.1 Extended Visualizations............................................................. 4.5.2 Geographic Maps........................................................................ 4.5.3 Dynamic Graphs......................................................................... Appendix .......................................................................................................... 4.1 5 94 95 95 98 99 101 105 105 109 Ill 114 115 118 118 119 120 122 122 126 126 129 131 142 143 144 146 147 153 158 159 162 165 166 Advanced Deep
Survey Analysis: The Regression Family ...................... 177 5.1 The Regression Family and Link Functions......................................... 5.2 The Identity Link: Introduction to OLS Regression............................. 5.2.1 OLS Regression Background.................................................... 5.2.2 The Classical Assumptions....................................................... 5.2.3 Example of Application............................................................. 5.2.4 Steps for Estimating an OLS Regression.................................. 5.2.5 Predicting with the OLS Model................................................. 178 179 180 180 181 182 186
Contents xvi The Logit Link: Introduction to Logistic Regression .......................... 5.3.1 Logistic Regression Background.............................................. 5.3.2 Example of Application.............................................................. 5.3.3 Steps for Estimating a Logistic Regression............................. 5.3.4 Predicting with the Logistic Regression Model....................... 5.4 The Poisson Link: Introduction to Poisson Regression....................... 5.4.1 Poisson Regression Background................................................ 5.4.2 Example of Application.............................................................. 5.4.3 Steps for Estimating a Poisson Regression............................... 5.4.4 Predicting with the Poisson Regression Model....................... Appendix........................................................................................................... 5.3 6 Sample of Specialized Survey Analyses ........................................................ 209 Conjoint Analysis.................................................................................... 6.1.1 CaseStudy................................................................................... 6.1.2 Analysis Steps............................................................................. 6.1.3 Creating the Design Matrix....................................................... 6.1.4 Fielding the Conjoint Study....................................................... 6.1.5 Estimating a Conjoint
Model.................................................... 6.1.6 Attribute Importance Analysis................................................... 6.2 Net Promoter Score................................................................................ 6.3 Correspondence Analysis....................................................................... 6.4 Text Analysis............................................................................................ 6.1 7 210 210 210 211 212 214 215 217 224 228 Complex Surveys ............................................................................................... 237 7.1 7.2 7.3 7.4 7.5 8 187 189 192 194 200 200 200 201 201 202 203 Complex Sample Survey Estimation Effects....................................... Sample Size Calculation......................................................................... Parameter Estimation............................................................................. Tabulation............................................................................................... 7.4.1 Tabulation.................................................................................... 7.4.2 CrossTabulation.......................................................................... Hypothesis Testing.................................................................................. 7.5.1 One-Sample Test: Hypothesized Mean..................................... 7.5.2 Two-Sample Test: Independence Case..................................... 7.5.3 Two-Sample Test: Paired Case
.................................................. 239 240 241 244 245 245 246 247 248 248 Bayesian Survey Analysis: Introduction ...................................................... 251 8.1 8.2 Frequentisi vs Bayesian Statistical Approaches................................... Digression on Bayes’ Rule.................................................................... 8.2.1 Bayes’ Rule Derivation.............................................................. 8.2.2 Bayes ’ Rule Reexpressions ...................................................... 8.2.3 The Prior Distribution............................................................... 8.2.4 The Likelihood Function........................................................... 8.2.5 The Marginal Probability Function.......................................... 8.2.6 The Posterior Distribution ........................................................ 8.2.7 Hyperparameters of the Distributions...................................... 253 259 259 261 262 263 263 264 264
Contents xvii 8.3 Computational Method: MCMC............................................................ 8.3.1 Digression on Markov Chain Monte Carlo Simulation........... 8.3.2 Sampling from a Markov Chain Monte Carlo Simulation .... 8.4 Python Package руМСЗ՛. Overview...................................................... 8.5 Case Study.............................................................................................. 8.5.1 Basic Data Analysis.................................................................. 8.6 Benchmark OĹS Regression Estimation.............................................. 8.7 Using руМСЗ.......................................................................................... 8.7.1 руМСЗ Bayesian Regression Setup ......................................... 8.7.2 Bayesian Estimation Results..................................................... 8.7.2.1 The MAP Estimate..................................................... 8.7.2.2 The Visualization Output.......................................... 8.8 Extensions to Other Analyses............................................................... 8.8.1 Sample Mean Analysis............................................................. 8.8.2 Sample Proportion Analysis.................................................... 8.8.3 Contingency Table Analysis.................................................... 8.8.4 Logit Model for Contingency Table........................................ 8.8.5 Poisson Model for Count Data................................................. 8.9
Appendix................................................................................................ 8.9.1 Beta Distribution...................................................................... 8.9.2 Half-Normal Distribution......................................................... 8.9.3 Bernoulli Distribution............................................................... 9 265 265 269 269 270 272 273 274 274 280 280 282 289 290 290 291 295 297 300 300 300 301 Bayesian Survey Analysis: Multilevel Extension....................................... 303 9.1 9.2 9.3 9.4 9.5 9.6 Multilevel Modeling: An introduction.................................................. 9.1.1 Omitted Variable Bias............................................................... 9.1.2 Simple Handling of Data Structure.......................................... 9.1.3 Nested Market Structures......................................................... Multilevel Modeling: Some Observations............................................ 9.2.1 Aggregation and Disaggregation Issues.................................. 9.2.2 Two Fallacies............................................................................ 9.2.3 Terminology.............................................................................. 9.2.4 Ubiquity of Hierarchical Structures......................................... Data Visualization of Multilevel Data................................................... 9.3.1 Basic Data Visualization and Regression Analysis................ Case Study
Modeling............................................................................. 9.4.1 Pooled Regression Model......................................................... 9.4.2 Unpooled (Dummy Variable) Regression Model .................... 9.4.3 Multilevel Regression Model.................................................... Multilevel Modeling Using руМСЗ՛. Introduction................................ 9.5.1 Multilevel Model Notation......................................................... 9.5.2 Multilevel Model Formulation................................................... 9.5.3 Example Multilevel Estimation Set-up.................................... 9.5.4 Example Multilevel Estimation Analyses................................ Multilevel Modeling with Level Explanatory Variables ..................... 304 305 307 307 308 309 310 311 311 312 313 318 318 319 321 323 324 324 325 328 328
Contents xviii 9.7 Extensions of Multilevel Models............................................................ 9.7.1 Logistic Regression Model ....................................................... 9.7.2 Possion Model............................................................................ 9.7.3 Panel Data.................................................................................. Appendix .......................................................................................................... 328 330 332 332 333 References............................................................................................................... 337 Index........................................................................................................................ 343
|
| adam_txt |
Contents 1 Introduction to Modem Survey Analytics . 1.1 Information and Survey Data. 1.2 Demystifying Surveys . 1.2.1 Survey Objectives. 1.2.2 Target Audience and Sample Size. 1.2.2.1 Key Parameters to Estimate. 1.2.2.2 Sample Design to Use. 1.2.2.3 Population Size. 1.2.2.4 Alpha. 1.2.2.5 Margin of Error. 1.2.2.6 Additional Information. 1.2.3 Screener and Questionnaire Design. 1.2.4 Fielding the Study. 1.2.5 Data Analysis. 1.2.6 Report Writing and Presentation . 1.3 Sample Representativeness. 1.3.1 Digression on Indicator Variables. 1.3.2 Calculating the Population Parameters. 1.4 Estimating Population
Parameters . 1.5 Case Studies. 1.5.1 Consumer Study: Yogurt Consumption. 1.5.2 Public Sector Study: VA Benefits Survey . 1.5.3 Public Opinion Study: Toronto Casino Opinion Survey. 1.5.4 Public Opinion Study: San Francisco Airport Customer Satisfaction Survey . 1.6 Why Use Python for Survey Data Analysis?. 1.7 Why Use Jupyter for Survey Data Analysis?. 1 3 4 5 7 9 9 10 10 10 10 12 14 14 16 16 20 21 22 25 25 27 28 30 30 32 xiii
xiv 2 Contents First Step: Working with Survey Data. 2.1 Best Practices: First Steps to Analysis. 2.1.1 Installing and Importing Python Packages. 2.1.2 Organizing Routinely Used Packages, Functions, and Formats. 39 2.1.3 Defining Data Paths and File Names. 2.1.4 Defining Your Functions and Formatting Statements. 2.1.5 Documenting Your Data with a Dictionary. 2.2 Importing Your Data with Pandas. 2.3 Handling Missing Values. 2.3.1 Identifying Missing Values . 2.3.2 Reporting Missing Values. 2.3.3 Reasons for Missing Values. 2.3.4 Dealing with Missing Values. 2.3.4.1 Use the fillna( ) Method. 2.3.4.2 Use the Interpolation ) Method. 2.3.4.3 An Even More Sophisticated Method. 2.4 Handling Special Types of Survey Data. 2.4.1 CATA Questions. 2.4.1.1
Multiple Responses. 2.4.1.2 Multiple Responses by ID. 2.4.1.3 Multiple Responses Delimited . 2.4.1.4 Indicator Variable. 2.4.1.5 Frequencies . 2.4.2 Categorical Questions. 2.5 Creating New Variables, Binning, and Rescaling . 2.5.1 Creating Summary Variables . 2.5.2 Rescaling . 2.5.3 Other Forms of Preprocessing. 2.6 Knowing the Structure of the Data Using Simple Statistics . 2.6.1 Descriptive Statistics and DataFrame Checks. 2.6.2 Obtaining Value Counts. 2.6.3 Styling Your DataFrame Display. 2.7 Weight Calculations. 2.7.1 Complex Weight Calculation: Raking. 2.7.2 Types of Weights. 2.8 Querying Data. 3 35 36 36 41 42 42 43 48 49 49 50 51 51 51 52 52 52
53 53 54 54 54 54 56 58 62 64 67 68 69 69 70 73 75 80 Shallow Survey Analysis. 83 3.1 Frequency Summaries. 84 3.1.1 Ordinal-Based Summaries. 85 3.1.2 Nominal-Based Summaries. 86 3.2 Basic Descriptive Statistics. 86 3.3 Cross-Tabulations. 89
Contents 3.4 3.5 4 XV Data Visualization. 3.4.1 Visuals Best Practice. 3.4.2 Data Visualization Background. 3.4.3 Pie Charts. 3.4.4 BarCharts . 3.4.5 Other Charts and Graphs. 3.4.5.1 Histograms and Boxplots for Distributions. 3.4.5.2 Mosaic Charts. 3.4.5.3 Heatmaps. Weighted Summaries: Crosstabs and Descriptive Statistics . Beginning Deep Survey Analysis. 113 Hypothesis Testing. 4.1.1 Hypothesis Testing Background. 4.1.2 Examples of Hypotheses. 4.1.3 A Formal Framework for Statistical Tests . 4.1.4 A Less Formal Framework for Statistical Tests. 4.1.5 Types of Tests to Use. 4.2 Quantitative Data: Tests of Means
. 4.2.1 Test of One Mean. 4.2.2 Test of Two Means for Two Populations. 4.2.2.1 Standard Errors: Independent Populations . 4.2.2.2 Standard Errors: Dependent Populations. 4.2.3 Test of More Than Two Means. 4.3 Categorical Data: Tests of Proportions. 4.3.1 Single Proportions. 4.3.2 Comparing Proportions: Two IndependentPopulations. 4.3.3 Comparing Proportions: Paired Populations. 4.3.4 Comparing Multiple Proportions. 4.4 Advanced Tabulations . 4.5 Advanced Visualization. 4.5.1 Extended Visualizations. 4.5.2 Geographic Maps. 4.5.3 Dynamic Graphs. Appendix . 4.1 5 94 95 95 98 99 101 105 105 109 Ill 114 115 118 118 119 120 122 122 126 126 129 131 142 143 144 146 147 153 158 159 162 165 166 Advanced Deep
Survey Analysis: The Regression Family . 177 5.1 The Regression Family and Link Functions. 5.2 The Identity Link: Introduction to OLS Regression. 5.2.1 OLS Regression Background. 5.2.2 The Classical Assumptions. 5.2.3 Example of Application. 5.2.4 Steps for Estimating an OLS Regression. 5.2.5 Predicting with the OLS Model. 178 179 180 180 181 182 186
Contents xvi The Logit Link: Introduction to Logistic Regression . 5.3.1 Logistic Regression Background. 5.3.2 Example of Application. 5.3.3 Steps for Estimating a Logistic Regression. 5.3.4 Predicting with the Logistic Regression Model. 5.4 The Poisson Link: Introduction to Poisson Regression. 5.4.1 Poisson Regression Background. 5.4.2 Example of Application. 5.4.3 Steps for Estimating a Poisson Regression. 5.4.4 Predicting with the Poisson Regression Model. Appendix. 5.3 6 Sample of Specialized Survey Analyses . 209 Conjoint Analysis. 6.1.1 CaseStudy. 6.1.2 Analysis Steps. 6.1.3 Creating the Design Matrix. 6.1.4 Fielding the Conjoint Study. 6.1.5 Estimating a Conjoint
Model. 6.1.6 Attribute Importance Analysis. 6.2 Net Promoter Score. 6.3 Correspondence Analysis. 6.4 Text Analysis. 6.1 7 210 210 210 211 212 214 215 217 224 228 Complex Surveys . 237 7.1 7.2 7.3 7.4 7.5 8 187 189 192 194 200 200 200 201 201 202 203 Complex Sample Survey Estimation Effects. Sample Size Calculation. Parameter Estimation. Tabulation. 7.4.1 Tabulation. 7.4.2 CrossTabulation. Hypothesis Testing. 7.5.1 One-Sample Test: Hypothesized Mean. 7.5.2 Two-Sample Test: Independence Case. 7.5.3 Two-Sample Test: Paired Case
. 239 240 241 244 245 245 246 247 248 248 Bayesian Survey Analysis: Introduction . 251 8.1 8.2 Frequentisi vs Bayesian Statistical Approaches. Digression on Bayes’ Rule. 8.2.1 Bayes’ Rule Derivation. 8.2.2 Bayes ’ Rule Reexpressions . 8.2.3 The Prior Distribution. 8.2.4 The Likelihood Function. 8.2.5 The Marginal Probability Function. 8.2.6 The Posterior Distribution . 8.2.7 Hyperparameters of the Distributions. 253 259 259 261 262 263 263 264 264
Contents xvii 8.3 Computational Method: MCMC. 8.3.1 Digression on Markov Chain Monte Carlo Simulation. 8.3.2 Sampling from a Markov Chain Monte Carlo Simulation . 8.4 Python Package руМСЗ՛. Overview. 8.5 Case Study. 8.5.1 Basic Data Analysis. 8.6 Benchmark OĹS Regression Estimation. 8.7 Using руМСЗ. 8.7.1 руМСЗ Bayesian Regression Setup . 8.7.2 Bayesian Estimation Results. 8.7.2.1 The MAP Estimate. 8.7.2.2 The Visualization Output. 8.8 Extensions to Other Analyses. 8.8.1 Sample Mean Analysis. 8.8.2 Sample Proportion Analysis. 8.8.3 Contingency Table Analysis. 8.8.4 Logit Model for Contingency Table. 8.8.5 Poisson Model for Count Data. 8.9
Appendix. 8.9.1 Beta Distribution. 8.9.2 Half-Normal Distribution. 8.9.3 Bernoulli Distribution. 9 265 265 269 269 270 272 273 274 274 280 280 282 289 290 290 291 295 297 300 300 300 301 Bayesian Survey Analysis: Multilevel Extension. 303 9.1 9.2 9.3 9.4 9.5 9.6 Multilevel Modeling: An introduction. 9.1.1 Omitted Variable Bias. 9.1.2 Simple Handling of Data Structure. 9.1.3 Nested Market Structures. Multilevel Modeling: Some Observations. 9.2.1 Aggregation and Disaggregation Issues. 9.2.2 Two Fallacies. 9.2.3 Terminology. 9.2.4 Ubiquity of Hierarchical Structures. Data Visualization of Multilevel Data. 9.3.1 Basic Data Visualization and Regression Analysis. Case Study
Modeling. 9.4.1 Pooled Regression Model. 9.4.2 Unpooled (Dummy Variable) Regression Model . 9.4.3 Multilevel Regression Model. Multilevel Modeling Using руМСЗ՛. Introduction. 9.5.1 Multilevel Model Notation. 9.5.2 Multilevel Model Formulation. 9.5.3 Example Multilevel Estimation Set-up. 9.5.4 Example Multilevel Estimation Analyses. Multilevel Modeling with Level Explanatory Variables . 304 305 307 307 308 309 310 311 311 312 313 318 318 319 321 323 324 324 325 328 328
Contents xviii 9.7 Extensions of Multilevel Models. 9.7.1 Logistic Regression Model . 9.7.2 Possion Model. 9.7.3 Panel Data. Appendix . 328 330 332 332 333 References. 337 Index. 343 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Paczkowski, Walter R. ca. 20./21. Jh |
| author_GND | (DE-588)116780757X |
| author_facet | Paczkowski, Walter R. ca. 20./21. Jh |
| author_role | aut |
| author_sort | Paczkowski, Walter R. ca. 20./21. Jh |
| author_variant | w r p wr wrp |
| building | Verbundindex |
| bvnumber | BV047482432 |
| classification_rvk | QH 244 |
| ctrlnum | (OCoLC)1349538940 (DE-599)BVBBV047482432 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01181nam a2200289 c 4500</leader><controlfield tag="001">BV047482432</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20221021 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">210924s2022 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783030762667</subfield><subfield code="9">978-3-030-76266-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1349538940</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047482432</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-355</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 244</subfield><subfield code="0">(DE-625)141558:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Paczkowski, Walter R.</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)116780757X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modern survey analytics</subfield><subfield code="b">using python for deeper insights</subfield><subfield code="c">Walter R. Paczkowski</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham</subfield><subfield code="b">Springer</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xxvi, 347 Seiten</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-030-76267-4</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Regensburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032883898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032883898</subfield></datafield></record></collection> |
| id | DE-604.BV047482432 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:13:01Z |
| indexdate | 2024-07-10T09:13:20Z |
| institution | BVB |
| isbn | 9783030762667 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032883898 |
| oclc_num | 1349538940 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | xxvi, 347 Seiten Illustrationen |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| record_format | marc |
| spelling | Paczkowski, Walter R. ca. 20./21. Jh. Verfasser (DE-588)116780757X aut Modern survey analytics using python for deeper insights Walter R. Paczkowski Cham Springer [2022] xxvi, 347 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Erscheint auch als Online-Ausgabe 978-3-030-76267-4 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=032883898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Paczkowski, Walter R. ca. 20./21. Jh Modern survey analytics using python for deeper insights |
| title | Modern survey analytics using python for deeper insights |
| title_auth | Modern survey analytics using python for deeper insights |
| title_exact_search | Modern survey analytics using python for deeper insights |
| title_exact_search_txtP | Modern survey analytics using python for deeper insights |
| title_full | Modern survey analytics using python for deeper insights Walter R. Paczkowski |
| title_fullStr | Modern survey analytics using python for deeper insights Walter R. Paczkowski |
| title_full_unstemmed | Modern survey analytics using python for deeper insights Walter R. Paczkowski |
| title_short | Modern survey analytics |
| title_sort | modern survey analytics using python for deeper insights |
| title_sub | using python for deeper insights |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032883898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT paczkowskiwalterr modernsurveyanalyticsusingpythonfordeeperinsights |