MARC details
| 000 -LEADER |
|---|
| fixed length control field |
10501nam a22002537a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20221018152212.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220906b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789354246197 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658.5 |
| Item number |
KUM |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kumar, U. Dinesh |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Business analytics: the science of data-driven decision making |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
Wiley India Pvt. Ltd. |
| Place of publication, distribution, etc. |
New Delhi |
| Date of publication, distribution, etc. |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxii, 623 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
INR |
| Price amount |
909.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
1. Introduction to Business Analytics<br/><br/>1.1 Introduction to Business Analytics<br/><br/>1.2 Analytics Landscape<br/><br/>1.3 Why Analytics<br/><br/>1.4 Business Analytics: The Science of Data-Driven Decision Making<br/><br/>1.5 Descriptive Analytics<br/><br/>1.6 Predictive Analytics<br/><br/>1.7 Prescriptive Analytics<br/><br/>1.8 Descriptive, Predictive, and Prescriptive Analytics Techniques<br/><br/>1.9 Big Data Analytics<br/><br/> <br/><br/>2. Foundations of Data Science: Descriptive Analytics<br/><br/>2.1 Introduction to Descriptive Analytics<br/><br/>2.2 Data Types and Scales of Variable Measurement<br/><br/>2.3 Types of Variable Measurement Scales<br/><br/>2.4 Population and Sample<br/><br/>2.5 Measures of Central Tendency<br/><br/>2.6 Percentile, Decile and Quartile<br/><br/>2.7 Measures of Variation<br/><br/>2.8 Measures of Shape − Skewness and Kurtosis<br/><br/>2.9 Data Visualization<br/><br/>2.10 Feature Engineering Using Visualization<br/><br/> <br/><br/>3. Introduction to Probability<br/><br/>3.1 Introduction to Probability Theory<br/><br/>3.2 Probability Theory – Terminology<br/><br/>3.3 Fundamental Concepts in Probability – Axioms of Probability<br/><br/>3.4 Application of Simple Probability Rules – Association Rule Learning<br/><br/>3.5 Bayes’ Theorem<br/><br/>3.6 Random Variables<br/><br/>3.7 Probability Density Function and Cumulative Distribution Function of a Continuous Random Variable<br/><br/>3.8 Binomial Distribution<br/><br/>3.9 Poisson Distribution<br/><br/>3.10 Geometric Distribution<br/><br/>3.11 Parameters of Continuous Distributions<br/><br/>3.12 Uniform Distribution<br/><br/>3.13 Exponential Distribution<br/><br/>3.14 Normal Distribution<br/><br/>3.15 Chi-Square Distribution<br/><br/>3.16 Student’s t-Distribution<br/><br/>3.17 F-Distribution<br/><br/>4. Sampling and Estimation<br/><br/>4.1 Introduction to Sampling<br/><br/>4.2 Population Parameters and Sample Statistic<br/><br/>4.3 Sampling<br/><br/>4.4 Probabilistic Sampling<br/><br/>4.5 Non-probability Sampling<br/><br/>4.6 Sampling Distribution<br/><br/>4.7 Central Limit Theorem (CLT)<br/><br/>4.8 Sample Size Estimation for Mean of the population<br/><br/>4.9 Estimation of Population Parameters<br/><br/>4.10 Method of Moments<br/><br/>4.11 Estimation of Parameters Using Method of Moments<br/><br/>4.12 Estimation of Parameters Using Maximum Likelihood Estimation<br/><br/> <br/><br/>5. Confidence Intervals<br/><br/>5.1 Introduction to Confidence Interval<br/><br/>5.2 Confidence Interval for Population Mean<br/><br/>5.3 Confidence Interval for Population Proportion<br/><br/>5.4 Confidence Interval for Population Mean When Standard Deviation is Unknown<br/><br/>5.5 Confidence Interval for Population Variance<br/><br/> <br/><br/>6. Hypothesis Testing<br/><br/>6.1 Introduction to Hypothesis Testing<br/><br/>6.2 Setting up a Hypothesis Test<br/><br/>6.3 One-Tailed and Two-Tailed Test<br/><br/>6.4 Type I Error, Type II Error, and Power of the Hypothesis Test<br/><br/>6.5 Hypothesis Testing for Population Mean When Population Variance is Known: One-Sample Z-Test<br/><br/>6.6 Hypothesis Testing of Population Proportion: Z-Test for Proportion<br/><br/>6.7 Hypothesis Test for Population Mean When Population Variance is Unknown: One-Sample t-Test<br/><br/>6.8 Paired-Sample t-Test<br/><br/>6.9 Comparing Two Populations: Two-Sample Z- and t-Test<br/><br/>6.10 Hypothesis Test for Difference in Population Proportion Under Large Samples: Two-Sample Z-Test for Proportions<br/><br/>6.11 Effect Size: Cohen’s D<br/><br/>6.12 Hypothesis Test for Equality of Population Variances (F Test)<br/><br/>6.13 Non-Parametric Tests: Chi-Square Tests<br/><br/> <br/><br/>7. Analysis of Variance<br/><br/>7.1 Introduction to ANOVA<br/><br/>7.2 Multiple t-Tests for Comparing Several Means<br/><br/>7.3 One-Way ANOVA<br/><br/>7.4 Two-Way ANOVA<br/><br/> <br/><br/>8. Correlation Analysis<br/><br/>8.1 Introduction to Correlation<br/><br/>8.2 Pearson Correlation Coefficient<br/><br/>8.3 Spearman Rank Correlation<br/><br/>8.4 Point Bi-Serial Correlation<br/><br/>8.5 The Phi-Coefficient<br/><br/> <br/><br/>9. Simple Linear Regression<br/><br/>9.1 Introduction to Simple Linear Regression<br/><br/>9.2 History of Regression – Francis Galton’s Regression Model<br/><br/>9.3 SLR Model Building<br/><br/>9.4 Estimation of Parameters Using OLS<br/><br/>9.5 Interpretation of SLR Coefficients<br/><br/>9.6 Validation of the SLR Model<br/><br/>9.7 Outlier Analysis<br/><br/>9.8 Confidence Interval for Regression Coefficients β0 and β1<br/><br/>9.9 Confidence Interval for the Expected Value of Y for a Given X<br/><br/>9.10 Prediction Interval for the Value of Y for a Given X<br/><br/> <br/><br/>10. Multiple Linear Regression<br/><br/>10.1 Introduction<br/><br/>10.2 Ordinary Least Squares Estimation for MLR<br/><br/>10.3 MLR Model Building<br/><br/>10.4 Part (Semi-Partial) Correlation and Regression Model Building<br/><br/>10.5 Interpretation of MLR Coefficients – Partial Regression Coefficient<br/><br/>10.6 Standardized Regression Coefficient<br/><br/>10.7 Regression Models with Qualitative Variables<br/><br/>10.8 Validation of Multiple Regression Model<br/><br/>10.9 Coefficient of Multiple Determination (R-Square) and Adjusted R-Square<br/><br/>10.10 Statistical Significance of Individual Variables in MLR – t-Test<br/><br/>10.11 Validation of Overall Regression Model – F-test<br/><br/>10.12 Validation of Portions of an MLR Model – Partial F-Test<br/><br/>10.13 Residual Analysis in MLR<br/><br/>10.14 Multi-Collinearity and Variance Inflation Factor<br/><br/>10.15 Auto-Correlation<br/><br/>10.16 Distance Measures and Outliers Diagnostics<br/><br/>10.17 Feature Selection in Regression Model Building (Forward, Backward and Stepwise Regression)<br/><br/>10.18 Avoiding Overfitting – Mallows’s Cp<br/><br/>10.19 Transformations<br/><br/>10.20 Omitted Variable Bias<br/><br/>10.21 Regression Model Deployment<br/><br/> <br/><br/>11. Logistic Regression<br/><br/>11.1 Introduction – Classification Problems<br/><br/>11.2 Introduction to Binary Logistic Regression<br/><br/>11.3 Estimation of Parameters in Logistic Regression<br/><br/>11.4 Interpretation of Logistic Regression Parameters<br/><br/>11.5 Logistic Regression Model Diagnostics<br/><br/>11.6 Classification Table, Sensitivity and Specificity<br/><br/>11.7 Optimal Cut-off Probability<br/><br/>11.8 Feature (Variable) Selection in Logistic Regression<br/><br/>11.9 Application of Logistic Regression in Credit Scoring<br/><br/>11.10 Gain Chart and Lift Chart<br/><br/>11.11 Multinomial Logistic Regression<br/><br/> <br/><br/>12. Decision Trees<br/><br/>12.1 Decision Trees: Introduction<br/><br/>12.2 Chi-square Automatic Interaction Detection (CHAID)<br/><br/>12.3 Classification and Regression Tree<br/><br/>12.4 Cost-Based Splitting Criteria<br/><br/>12.5 Regression Tree<br/><br/>12.6 Error Matrix and AUC for<br/><br/> <br/><br/>13. Forecasting Techniques<br/><br/>13.1 Introduction to Forecasting<br/><br/>13.2 Time-Series Data and Components of Time-Series Data<br/><br/>13.3 Forecasting Techniques and Forecasting Accuracy<br/><br/>13.4 Moving Average Method<br/><br/>13.5 Single Exponential Smoothing (SES)<br/><br/>13.6 Double Exponential Smoothing – Holt’s Method<br/><br/>13.7 Triple Exponential Smoothing (Holt-Winter Model)<br/><br/>13.8 Croston’s Forecasting Method for Intermittent Demand<br/><br/>13.9 Regression Model for Forecasting<br/><br/>13.10 Auto-Regressive (AR), Moving Average (MA) and ARMA Models<br/><br/>13.11 Auto-Regressive (AR) Models<br/><br/>13.12 Moving Average Process MA(q)<br/><br/>13.13 Auto-Regressive Moving Average (ARMA) Process<br/><br/>13.14 Auto-Regressive Integrated Moving Average (ARIMA) Process<br/><br/>13.15 Power of Forecasting Model: Theil’s Coefficient<br/><br/> <br/><br/>14. Clustering<br/><br/>14.1 Introduction to Clustering<br/><br/>14.2 Distance and Similarity Measures Used in Clustering<br/><br/>14.3 Quality and Optimal Number of Clusters<br/><br/>14.4 Clustering Algorithms<br/><br/>14.5 K-Means Clustering<br/><br/>14.6 Hierarchical Clustering<br/><br/> <br/><br/>15. Prescriptive Analytics<br/><br/>15.1 Introduction to Prescriptive Analytics<br/><br/>15.2 Linear Programming<br/><br/>15.3 Linear Programming (LP) Model Building<br/><br/>15.4 Linear Programming Problem (LPP) Terminologies<br/><br/>15.5 Assumptions of Linear Programming<br/><br/>15.6 Sensitivity Analysis in LPP<br/><br/>15.7 Solving a Linear Programming Problem Using Graphical Method<br/><br/>15.8 Range of Optimality<br/><br/>15.9 Range of Shadow Price<br/><br/>15.10 Dual Linear Programming<br/><br/>15.11 Primal-Dual Relationships<br/><br/>15.12 Multi-Period (Stage) Models<br/><br/>15.13 Linear Integer Programming (ILP)<br/><br/>15.14 Multi-Criteria Decision-Making (MCDM) Problems<br/><br/> <br/><br/>16. Stochastic Models and Reinforcement Learning<br/><br/>16.1 Introduction Stochastic Process<br/><br/>16.2 Poisson Process<br/><br/>16.3 Compound Poisson Process<br/><br/>16.4 Markov Chains<br/><br/>16.5 Classification of States in a Markov Chain<br/><br/>16.6 Markov Chains with Absorbing States<br/><br/>16.7 Expected Duration to Reach a State from Other States<br/><br/>16.8 Calculation of Retention Probability and Customer Lifetime Value Using Markov Chains<br/><br/>16.9 Markov Decision Process (MDP) and Reinforcement Learning<br/><br/>16.10 Value Iteration Algorithm<br/><br/> <br/><br/>17. Ensemble Methods<br/><br/>17.1 Ensemble Methods: Introduction<br/><br/>17.2 Condorcet’s Jury Theorem<br/><br/>17.3 Random Forest<br/><br/>17.4 Choice of Hyper-parameter Values in Random Forest<br/><br/>17.5 Random Forest Model Development<br/><br/>17.6 Variable Importance<br/><br/>17.7 Sampling Procedures to Improve Accuracy in Random Forest Model<br/><br/>17.8 Boosting<br/><br/>17.9 Gradient Boosting<br/><br/> <br/><br/>18. Six Sigma<br/><br/>18.1 Introduction to Six Sigma<br/><br/>18.2 What is Six Sigma?<br/><br/>18.3 Origins of Six Sigma<br/><br/>18.4 Three-Sigma Versus Six-Sigma Process<br/><br/>18.5 Cost of Poor Quality<br/><br/>18.6 Sigma Score<br/><br/>18.7 Industrial Applications of Six Sigma<br/><br/>18.8 Six Sigma Measures<br/><br/>18.9 Defects Per Million Opportunities (DPMO)<br/><br/>18.10 Yield<br/><br/>18.11 Sigma Score (or Sigma Quality Level)<br/><br/>18.12 DMAIC Methodology<br/><br/>18.13 Six Sigma Project Selection for DMAIC Implementation<br/><br/>18.14 DMAIC Methodology – Case of Armoured Vehicle<br/><br/>18.15 Six Sigma Toolbox<br/><br/> <br/><br/>Summary<br/><br/>Multiple Choice Questions<br/><br/>Exercises<br/><br/>Case Study: Era of Quality at the Akshaya Patra Foundation<br/><br/>References<br/><br/>Appendix<br/><br/>Index<br/><br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Description<br/>Business Analytics has become one of the most important skills that every student of Management and Engineering should acquire to become successful in their career. The use of analytics across industries for decision making, problem solving, and driving organizational innovation makes it an essential skill to develop. Analytics is used as a competitive strategy by many successful companies.<br/><br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Mathematical statistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Programming languages (Electronic computers) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Business logistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer programming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |