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Statistics for data science and analytics (Record no. 10432)

MARC details
000 -LEADER
fixed length control field	06496nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20250924163108.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	250924b \|\|\|\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9781394253807
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.5
Item number	BRU
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Bruce, Peter C.
245 ## - TITLE STATEMENT
Title	Statistics for data science and analytics
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc.	New Jersey
Name of publisher, distributor, etc.	Wiley
Date of publication, distribution, etc.	2025
300 ## - PHYSICAL DESCRIPTION
Extent	xxv, 352 p.
365 ## - TRADE PRICE
Price type code	USD
Price amount	119.95
500 ## - GENERAL NOTE
General note	Table of Contents:<br/><br/><br/>About the Authors xvii<br/><br/>Acknowledgments xix<br/><br/>About the Companion Website xxi<br/><br/>Introduction xxiii<br/><br/>1 Statistics and Data Science 1<br/><br/>1.1 Big Data: Predicting Pregnancy 2<br/><br/>1.2 Phantom Protection from Vitamin E 2<br/><br/>1.3 Statistician, Heal Thyself 3<br/><br/>1.4 Identifying Terrorists in Airports 4<br/><br/>1.5 Looking Ahead 5<br/><br/>1.6 Big Data and Statisticians 5<br/><br/>2 Designing and Carrying Out a Statistical Study 9<br/><br/>2.1 Statistical Science 9<br/><br/>2.2 Big Data 10<br/><br/>2.3 Data Science 10<br/><br/>2.4 Example: Hospital Errors 11<br/><br/>2.5 Experiment 12<br/><br/>2.6 Designing an Experiment 13<br/><br/>2.7 The Data 19<br/><br/>2.8 Variables and Their Flavors 21<br/><br/>2.9 Python: Data Structures and Operations 25<br/><br/>2.10 Are We Sure We Made a Difference? 34<br/><br/>2.11 Is Chance Responsible? The Foundation of Hypothesis Testing 34<br/><br/>2.12 Probability 36<br/><br/>2.13 Significance or Alpha Level 38<br/><br/>2.14 Other Kinds of Studies 40<br/><br/>2.15 When to Use Hypothesis Tests 42<br/><br/>2.16 Experiments Falling Short of the Gold Standard 42<br/><br/>2.17 Summary 43<br/><br/>2.18 Python: Iterations and Conditional Execution 44<br/><br/>2.19 Python: Numpy, scipy, and pandas—The Workhorses of Data Science 50<br/><br/>Exercises 56<br/><br/>3 Exploring and Displaying the Data 61<br/><br/>3.1 Exploratory Data Analysis 61<br/><br/>3.2 What to Measure—Central Location 62<br/><br/>3.3 What to Measure—Variability 65<br/><br/>3.4 What to Measure—Distance (Nearness) 69<br/><br/>3.5 Test Statistic 71<br/><br/>3.6 Examining and Displaying the Data 72<br/><br/>3.7 Python: Exploratory Data Analysis/Data Visualization 80<br/><br/>Exercises 88<br/><br/>4 Accounting for Chance—Statistical Inference 91<br/><br/>4.1 Avoid Being Fooled by Chance 91<br/><br/>4.2 The Null Hypothesis 92<br/><br/>4.3 Repeating the Experiment 93<br/><br/>4.4 Statistical Significance 99<br/><br/>4.5 Power 103<br/><br/>4.6 The Normal Distribution 103<br/><br/>4.7 Summary 105<br/><br/>4.8 Python: Random Numbers 105<br/><br/>Exercises 115<br/><br/>5 Probability 121<br/><br/>5.1 What Is Probability 121<br/><br/>5.2 Simple Probability 122<br/><br/>5.3 Probability Distributions 126<br/><br/>5.4 From Binomial to Normal Distribution 129<br/><br/>5.5 Appendix: Binomial Formula and Normal Approximation 133<br/><br/>5.6 Python: Probability 134<br/><br/>Exercises 141<br/><br/>6 Categorical Variables 143<br/><br/>6.1 Two-way Tables 143<br/><br/>6.2 Conditional Probability 144<br/><br/>6.3 Bayesian Estimates 147<br/><br/>6.4 Independence 150<br/><br/>6.5 Multiplication Rule 154<br/><br/>6.6 Simpson’s Paradox 156<br/><br/>6.7 Python: Counting and Contingency Tables 157<br/><br/>Exercises 163<br/><br/>7 Surveys and Sampling 167<br/><br/>7.1 Literary Digest—Sampling Trumps “All Data” 167<br/><br/>7.2 Simple Random Samples 170<br/><br/>7.3 Margin of Error: Sampling Distribution for a Proportion 172<br/><br/>7.4 Sampling Distribution for a Mean 174<br/><br/>7.5 The Bootstrap 176<br/><br/>7.6 Rationale for the Bootstrap 177<br/><br/>7.7 Standard Error 188<br/><br/>7.8 Other Sampling Methods 188<br/><br/>7.9 Absolute vs. Relative Sample Size 192<br/><br/>7.10 Python: Random Sampling Strategies 192<br/><br/>Exercises 202<br/><br/>8 More than Two Samples or Categories 207<br/><br/>8.1 Count Data—R × C Tables 207<br/><br/>8.2 The Role of Experiments (Many Are Costly) 208<br/><br/>8.3 Chi-Square Test 210<br/><br/>8.4 Single Sample—Goodness-of-Fit 215<br/><br/>8.5 Numeric Data: ANOVA 217<br/><br/>8.6 Components of Variance 222<br/><br/>8.7 Factorial Design 224<br/><br/>8.8 The Problem of Multiple Inference 226<br/><br/>8.9 Continuous Testing 228<br/><br/>8.10 Bandit Algorithms 229<br/><br/>8.11 Appendix: ANOVA, the Factor Diagram, and the F-Statistic 230<br/><br/>8.12 More than One Factor or Variable—From ANOVA to Statistical Models 237<br/><br/>8.13 Python: Contingency Tables and Chi-square Test 237<br/><br/>8.14 Python: ANOVA 241<br/><br/>Exercises 246<br/><br/>9 Correlation 249<br/><br/>9.1 Example: Delta Wire 249<br/><br/>9.2 Example: Cotton Dust and Lung Disease 251<br/><br/>9.3 The Vector Product Sum Test 252<br/><br/>9.4 Correlation Coefficient 256<br/><br/>9.5 Correlation is not Causation 260<br/><br/>9.6 Other Forms of Association 261<br/><br/>9.7 Python: Correlation 262<br/><br/>Exercises 269<br/><br/>10 Regression 271<br/><br/>10.1 Finding the Regression Line by Eye 272<br/><br/>10.2 Finding the Regression Line by Minimizing Residuals 274<br/><br/>10.3 Linear Relationships 276<br/><br/>10.4 Prediction vs. Explanation 280<br/><br/>10.5 Python: Linear Regression 284<br/><br/>Exercises 293<br/><br/>11 Multiple Linear Regression 295<br/><br/>11.1 Terminology 295<br/><br/>11.2 Example—Housing Prices 296<br/><br/>11.3 Interaction 301<br/><br/>11.4 Regression Assumptions 304<br/><br/>11.5 Assessing Explanatory Regression Models 306<br/><br/>11.6 Assessing Regression for Prediction 314<br/><br/>11.7 Python: Multiple Linear Regression 324<br/><br/>Exercises 332<br/><br/>12 Predicting Binary Outcomes 337<br/><br/>12.1 K-Nearest-Neighbors 337<br/><br/>12.2 Python: Classification 343<br/><br/>Exercises 346<br/><br/>Index 349
520 ## - SUMMARY, ETC.
Summary, etc.	Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations.<br/><br/>A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction.<br/><br/>This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves.<br/><br/>(https://www.wiley.com/en-us/Statistics+for+Data+Science+and+Analytics-p-9781394253807)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Statistics--Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Data analytics
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Gedeck, Peter
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Dobbins, Janet
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Book
Source of classification or shelving scheme	Dewey Decimal Classification

Holdings
Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Not for loan	Collection code	Bill No	Bill Date	Home library	Current library	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Total Checkouts	Total Renewals	Full call number	Accession Number	Checked out	Date last seen	Date checked out	Copy number	Cost, replacement price	Price effective from	Koha item type
		Dewey Decimal Classification			Operations Management & Quantitative Techniques	COR/IN/26/5786	10-09-2025	Indian Institute of Management LRC	Indian Institute of Management LRC	General Stacks	09/24/2025	CBS Publishers & Distributors Pvt. Ltd.	6907.92	1	1	519.5 BRU	008984	10/25/2025	09/25/2025	09/25/2025	1	10627.57	09/24/2025	Book