Business analytics (Record no. 14434)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 21461nam a22002057a 4500 |
| 005 - DATE & TIME | |
| control field | 20260219133722.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260219b |||||||| |||| 00| 0 eng d |
| 020 ## - ISBN | |
| International Standard Book Number | 9789393330994 |
| Price | 1250.00 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | S.X.U.K |
| 041 ## - Language | |
| Language | English |
| 082 ## - DDC NUMBER | |
| Classification number | R 658.4 DAS(BUS) |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Das, Shubhbrata |
| 245 ## - TITLE STATEMENT | |
| Title | Business analytics |
| Sub Title | : data to decisions |
| Statement of responsibility | Shubhabrata Das, Soudeep Deb |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | University press |
| Name of publisher, distributor, etc | Kolkata |
| Date of publication, distribution, etc | c2026 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | xxxiii, 1098p. |
| Other Details | P.B. |
| 500 ## - GENERAL NOTE | |
| General note | About the Authors<br/>Testimonials<br/>Foreword<br/>Preface<br/>Acknowledgements<br/><br/>PART A: FOUNDATIONS<br/><br/>Chapter 1: Introduction and Overview<br/>1.1 Introduction to Business Data Analytics – Data to Decisions<br/>1.2 History and Interconnections Between Branches of Analytics<br/>1.3 An Overview of Statistical Methods<br/>1.4 Applications of Data-driven Analytics in Business<br/>Fast-Moving Consumer Goods (FMCG) | Aggregator Industry or Platform Economy | Banking and Financial Services | Retail and E-commerce | Energy (Oil and Natural Gas, Renewable Energy) | Pharmaceuticals and Healthcare | Sports and Entertainment | Automotive | Real Estate | Tourism | Information Technology | Agriculture | Manufacturing | Logistics and Transportation | Telecommunications | Education<br/>1.5 Role of Analytics in Management Disciplines<br/>1.6 Structure of the Book<br/>1.7 Software and Computational Aspects<br/>1.8 Description of Data Sets for Running Case Studies<br/>Exercise<br/><br/>Chapter 2: Data Representation and Descriptive Statistics<br/>2.1 Overview<br/>2.2 Types of Data<br/>2.3 Organising Data Using Arrays, Graphs, and Tables<br/>Stem and Leaf Display, Bar Charts, and Pie Charts | Frequency Distribution, Frequency Polygon, Histogram, and Ogive<br/>2.4 Data Summarisation<br/>Quantiles, Percentiles, and Quartiles | Measures of Central Tendency | Measures of Dispersion or Variability | Skewness and Kurtosis | Important Results | Outlier Detection<br/>2.5 Summary Statistics for Bivariate Data<br/>Cross-tabulation | Scatter Plot, Covariance, and Correlation | Measure of Dependency When One Variable is Quantitative and the Other Qualitative<br/>2.6 Pivot Table<br/>Case Study: Supermarket Sales Analysis<br/>2.7 Advanced Data Visualisation<br/>Time Series Data | Cross-Sectional Data | Spatial Data | Case Study: Spatial Analysis of Indian Weather<br/>2.8 Best Practices for Data Handling and Cleaning<br/>Planning for Data Collection | Why is Data Cleaning Necessary? | Dealing with Missing Data | Data Transformation | General Guidelines for Graphical Data Representations<br/>2.9 Case Study: Indian Start-up Funding<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 3: Vectors and Matrices<br/>3.1 Overview<br/>3.2 Sets, Relations, Functions<br/>3.3 Vector Space<br/>3.4 Matrices<br/>Introduction to Matrix Structure | Operations on Matrices | Matrices with Special Structures | Rank and Inverse | Determinants<br/>3.5 Inner Product and Orthogonality<br/>3.6 Eigenvalues<br/>Characteristic Roots, Eigenvalues, and Eigenvectors | Spectral Representation | Singular Value Decomposition<br/>3.7 Linear and Quadratic Forms<br/>Linear System of Equations | Quadratic Forms | Positive Definiteness<br/>3.8 Case Studies<br/>Unveiling Matrix Structures of Equal Correlation | Matrices in Action: Analysing Swiggy Data<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>PART B: PROBABILITY AND DISTRIBUTIONS<br/><br/>Chapter 4: Probability<br/>4.1 Introduction to Probability<br/>4.2 Formalising Probability Notions<br/>Random Experiment and Events – Union, Intersection, Complementation – Mutually Exclusive (Disjoint) and Exhaustive Set of Events | Approaches to Defining Probability | Axioms of Probability – Probability Rules<br/>4.3 Conditional Probability and the Notion of Independent Events<br/>Simpson’s Paradox<br/>4.4 Bayes’ Theorem<br/>4.5 Case Studies<br/>KJSS | A Tale of Two Mothers<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 5: Discrete Random Variables and Probability Distributions<br/>5.1 Random Variables and Probability Distributions<br/>Discrete Versus Continuous Random Variable<br/>5.2 General Discrete Distributions: Expected Value, Variance, and Other Characteristics<br/>Probability Mass/Density Function and Cumulative Distribution Function of a Discrete Random Variable | Expected Value (Mean) and Variance of a Discrete Random Variable | Higher Order Moments, Skewness, and Kurtosis | Mean (Expected Value) and Variance of a Linear Combination of Random Variables | Chebyshev’s Inequality | Simulation from a Discrete Probability Distribution | Percentiles and Other Measures of Central Tendency and Dispersion of a (Discrete) Probability Distribution<br/>5.3 Discrete Uniform Distribution<br/>5.4 Binomial Distribution<br/>5.5 Poisson Distribution<br/>Poisson Approximation to Binomial Distribution | Simulation from Poisson Distribution<br/>5.6 Joint and Conditional Distribution for Discrete Variables<br/>5.7 Other Popular Discrete Distributions<br/>Geometric Distribution | Negative Binomial Distribution | Hypergeometric Distribution | Multinomial Distribution<br/>5.8 Relationship between Different Discrete Distributions<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 6: Continuous Probability Distributions<br/>6.1 Overview<br/>6.2 General Continuous Probability Distributions and their Characteristics<br/>6.3 Uniform Distribution<br/>6.4 Normal (Gaussian) Distribution<br/>Standard Normal Distribution and Tables | Discussion: Stock-out at PAINTMART | Discussion: How Much to Pack? | Normal Approximation of Binomial and Poisson Distributions | Whither Approximation to Binomial/Poisson Distribution?<br/>6.5 Exponential Distribution<br/>Memoryless Property of Exponential Distribution<br/>6.6 Poisson Process: Inter-linkage between the Exponential and Poisson Distributions<br/>6.7 Distributions Related to Normal Distributions<br/>Lognormal Distribution | Chi-Square Distribution | (Student’s) t Distribution | F Distribution<br/>6.8 Joint and Conditional Distribution for Continuous Variables<br/>Joint Density Function of Continuous Variables | Marginal Density Function | Conditional Density | Independent Random Variables | Function of (Two) Random Variables and Its Expected Values | Covariance and Correlation | Illustrating the Computation in Example 6.24<br/>6.9 Other Continuous Probability Distributions<br/>Gamma Distribution | Beta Distribution | Weibull Distribution | Pareto Distribution | Logistic Distribution<br/>6.10 Interconnections between Different Probability Distributions<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 7: Direct Applications of Probability in Business Management<br/>7.1 Overview<br/>Introduction to Optimisation | Simulation<br/>7.2 Decision-making under Uncertainty in a Tree Structure<br/>Decision Analysis Without Probabilistic Assessment | Decision-Making with Expected Value Approach | Expected Value with Perfect Information and Expected Value of Perfect Information (EVPI) | Expected Value of Sample Information (EVSI) and Sampling Efficiency (SI)<br/>7.3 Portfolio Diversification and Asset Allocation<br/>7.4 The PERT-CPM Model<br/>7.5 Dream11 – Probability in Fantasy Sport<br/>Choosing the Best Possible Fantasy Eleven | Which Dream11 Tournament(s) Should One Participate In?<br/>7.6 Overbooking in Airlines<br/>Probabilistic Formulation of the Problem | Assuming Equal Chance of Cancellation at All Times | Assuming Chance of Cancellation Changes as a Function of Time<br/>7.7 Pricing and Promotions<br/>Pricing of Airline Seats | Assessing the Cost and Benefit of a Promotion for F&H | Retail Markdown<br/>7.8 Queuing Theory<br/>Probability and Distributions in Queuing | Categorisation of Queuing Models<br/>7.9 Actuarial Science<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>PART C: INFERENCE AND REGRESSION<br/>Chapter 8: Sampling<br/>8.1 Introduction to Sampling<br/>History and Terminology | Sampling Versus Complete Enumeration – Sampling and Non-Sampling Error | Merits of Sampling<br/>8.2 Random Sampling Methods<br/>Simple Random Sampling With Replacement (SRSWR) and Simple Random Sampling Without Replacement (SRSWOR) | Systematic Sampling | Stratified Sampling | Cluster Sampling | Other Random Sampling Methods<br/>8.3 Non-random Sampling Methods<br/>Convenient Sampling | Purposive or Judgemental Sampling | Quota Sampling | Snowball Sampling | Voluntary Sampling<br/>8.4 Questionnaire Design for Surveys<br/>8.5 Other Issues and Challenges Related to Sampling<br/>Sampling When the Population is Infinite and/or Hypothetical in Nature | Channel of Collecting Survey Information | Process Versus Outcome | Paired Sampling | What Should the Sample Size Be?<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 9: Point Estimation and Sampling Distribution<br/>9.1 Introduction to Estimation<br/>9.2 Basic Properties of (Point) Estimators<br/>Unbiased Estimator | Bias of an Estimator | Standard Error of an Estimator | Comparing Two Groups or Populations<br/>9.3 Other Desirable Properties of Estimators<br/>Minimum Variance Unbiased Estimator | Asymptotically Unbiased Estimators | Consistent Estimator<br/>9.4 Sampling Distribution and Central Limit Theorem<br/>What is a Sampling Distribution? | Sampling Distribution of Sample Mean When the Sample Size is Large | Sampling Distribution of Sample Proportion<br/>9.5 Estimating Population Variance and Sampling Distribution of Sample Variance<br/>9.6 Revisiting Student’s t Distribution: Distribution of (Standardised) Sample Mean while Sampling from a Normal Population<br/>9.7 Sampling Distribution of Difference in Sample Means based on Independent Samples Drawn from Two Populations<br/>Both Sample Sizes are Large with Known/Unknown Population Standard Deviations | Both Populations are Normal with Known Standard Deviation | Population Standard Deviations are Unknown and At Least One of Two Sample Sizes is /are Not Large – Normal Populations<br/>9.8 Sampling Distribution of Difference in Sample Proportions based on Independent Samples drawn from Two Populations<br/>9.9 Sampling Distribution of Ratio of Variances<br/>9.10 Understanding CLT and Sampling Distribution Concepts Using Simulation<br/>9.11 Case Studies<br/>Engagement and Job Fit of Retail Salespeople | Supermarket Sales Analysis | Placement of Students<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 10: Confidence Interval (CI) Estimation<br/>10.1 Introduction<br/>10.2 Confidence Interval Estimation for Population Proportion<br/>Appropriate Interpretation of Confidence Coefficient in Confidence Interval Estimation | Relationship Between Margin of Error, Sample Size, and Confidence Coefficient | Determining the Requisite Sample Size in the Context of CI Estimation of Population Proportion | Adjustment for SRSWOR Sampling from a Finite Population<br/>10.3 Confidence Interval Estimation for Population Mean<br/>MOE, Sample Size Determination, Finite Population Adjustment<br/>10.4 Confidence Interval Estimation for Standard Deviation<br/>10.5 Confidence Interval Estimation in Two-sample Problems<br/>Framework and Notation | CI for Difference in Proportion Between Two Populations | CI for Difference Between Mean of Two Populations | CI for Ratio of Standard Deviations of Two Populations<br/>10.6 Paired Sampling<br/>10.7 Ancillary Discussion on Confidence Interval Estimation<br/>How Large Do the Sample Sizes Need to be in Two-Sample CI Estimation Problems? | One-Sided Confidence Interval | Verify By Simulation that Confidence Interval Works<br/>10.8 Case Studies<br/>Supermarket Sales Analysis | Placement of Students | Indian Stock Market Data<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 11: Testing of Hypothesis<br/>11.1 Framework of Testing of Hypothesis<br/>Null and Alternative Hypotheses | Simple and Composite Hypotheses | Type I and Type II Errors | Which Hypothesis is Null and Which Hypothesis is Alternative? | Initial Discussion on Case Studies | Test Statistics, Critical Region, and Acceptance Region | p-value (Probability Value) of a Test | Steps in Conducting a Statistical Test | Drawing Parallels with the Judicial System<br/>11.2 One-sample Testing of Hypothesis for Population Mean<br/>Testing for l When r is Known | Testing for l When r is Unknown and n is Large | Testing l When r is Unknown and n is Small, Population is Normal<br/>11.3 One-sample Testing of Hypothesis for Population Proportion<br/>11.4 One-sample Testing of Hypothesis for Population Standard Deviation<br/>11.5 Computing Probability of Type II Error and Power of One-sample Tests<br/>Power of the One-Sample Mean Test when Population Standard Deviation is Known | Power of the One-Sample Test for Population Proportion | Power of the One-Sample Test for Standard Deviation<br/>11.6 Sample Size Determination in One-sample TOH Problems<br/>Testing for Mean | Testing for Proportion | Testing for Standard Deviation<br/>11.7 Two-sample Testing of Hypothesis<br/>Two-Sample Testing Comparing the Proportion of Two Populations | Two-Sample TOH Comparing the Mean of Two Populations | Two-Sample TOH Comparing Variances or SDs of Two Populations<br/>11.8 Paired Test for Mean<br/>11.9 Ancillary Discussion on Testing of Hypothesis<br/>Implementation in Excel and R | Conducting Testing of Hypothesis from Confidence Interval Estimation | Using Simulation in Testing of Hypothesis | Testing for Mean in Non-Normal Populations Based on Small Sizes<br/>11.10 Case Studies<br/>Placement of Students | Supermarket Sales Analysis | HR Analytics<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 12: Analysis of Variance (ANOVA)<br/>12.1 Overview<br/>12.2 One-way ANOVA<br/>Why Use ANOVA and Not Pairwise Comparisons? | The Model and Assumptions in ANOVA | The Sum of Squares and Algebraic Split | The Test Statistic and Null Distribution | The ANOVA Table | ANOVA: Comparison Between Three Estimates of s2 | Parameter Estimates | ANOVA as an Extension of the Two-sample T-test (Case k = 2) | Post-hoc Analysis: Paired Comparison | Case Study: Shuddho Chinton: Part I | Case Study: Shuddho Chinton: Part II<br/>12.3 Two-way ANOVA<br/>Two-way ANOVA without Replication | Two-way ANOVA with Replication and without Interaction Between Factors | Model in Two-way ANOVA with Interaction | Implementation of Two-way ANOVA Using Excel | Implementation of Two-way ANOVA Using R | Case Study: Shuddho Chinton: Part III<br/>12.4 Levene’s Test for Equality of Variance Across Multiple Populations<br/>Conducting Levene’s Test | Variations of Levene’s Test | Executing Levene’s Test Using Software<br/>12.5 Case Studies<br/>HR Analytics | Supermarket Sales Analysis<br/>12.6 Extending Anova to Multi-Way Analysis and Connections with Regression<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 13: Goodness-of-Fit Tests and Nonparametric Methods<br/>13.1 Overview<br/>13.2 Chi-square Goodness-of-Fit Tests<br/>For Distributions of Qualitative and Discrete Variables | For Continuous Distributions<br/>13.3 Chi-square Test of Independence in Contingency Tables<br/>Test of Independence or Homogeneity | Test of Independence versus Test of Homogeneity | Marascuilo Procedure and Multiple Comparison as Post-hoc Analysis to Chi-square Test of Independence<br/>13.4 Kolmogorov–Smirnov and Other Tests for Goodness of Fit<br/>How the K–S Test Works | Null Hypothesis and Test Statistic for One-Sample K–S Test | Two-Sample Kolmogorov–Smirnov Test | The Anderson–Darling Test | The Shapiro–Wilk Test<br/>13.5 Nonparametric Methods: Introduction and Overview<br/>13.6 Sign Test and Signed-rank Test<br/>Sign Test | Wilcoxon Signed-rank Test for Median and Other Percentiles<br/>13.7 Wilcoxon Rank-sum Test/Mann–Whitney Test<br/>13.8 Kruskal–Wallis Test<br/>13.9 Run Test for Independence<br/>13.10 Spearman’s Rank Correlation<br/>13.11 Nonparametric Density Estimation<br/>Kernel Density Estimation (KDE) | Nearest Neighbour Density Estimation (NNDE) | Spline-based Density Estimation (SDE) | Orthogonal Series-based Density Estimation (OSDE)<br/>13.12 Case Studies<br/>Supermarket Sales Analysis | HR Analytics<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 14: Correlation and Regression<br/>14.1 Overview<br/>14.2 Simple Linear Regression<br/>14.3 Multiple Linear Regression<br/>14.4 Inference in the Regression Problem<br/>Inference for Effects of Continuous Predictors | Inference for Effects of Categorical Predictors | Inference for Interaction Effects | Multiple R and R-squared | Analysis of Variance in Linear Regression | Prediction Interval<br/>14.5 Regression Diagnostics<br/>Variance Inflation Factor: A Check for Multicollinearity | Visualising Residuals: A Check for Error Assumptions | Detecting Unusual Observations<br/>14.6 Improving the Linear Regression Models<br/>Variable Selection | Outlier Management | Transformation of Variables | Advanced Modelling Techniques<br/>14.7 Case Studies<br/>Swiggy (What Impacts the Ratings)? | Finding a Good Model for Monthly Demand of WonderWidget<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>Chapter 15: Logistic Regression<br/>15.1 Overview<br/>15.2 Binomial Distribution and Odds<br/>15.3 The Logistic Regression Model<br/>Logistic Regression with a Single Predictor | Logistic Regression with Multiple Predictors<br/>15.4 Inference for Logistic Regression<br/>Inference for the Coefficients | Assessment of Model Fit | Prediction and Classification<br/>15.5 Diagnostics and Improvement<br/>Checking Model Assumptions | Variable Selection | Other Diagnostics<br/>15.6 Logistic Regression with Probit Link Function<br/>15.7 Multinomial Logistic Regression<br/>15.8 Case Studies<br/>Shark Tank India | T20 Cricket Matches<br/>Summary of Key Concepts and Formulae<br/>Practice Problems<br/><br/>PART D: ADVANCED ANALYTICS (Available on the Orient BlackSwan Smart App)<br/><br/>Chapter 16: Advanced Regression Models<br/>16.1 Overview<br/>16.2 Generalised Linear Model (GLM)<br/>Linear Regression as GLM | Logistic Regression as GLM | Poisson Regression as GLM | Other Types of GLM<br/>16.3 Improvements over Ordinary Least Squares Linear Regression<br/>Generalised Least Squares | Locally Weighted Regression | Non-linear Regression Models<br/>16.4 Shrinkage and Penalised Regression<br/>The Concept of Shrinkage | Ridge Regression | LASSO Regression | Other Types of Penalised Regression Models<br/>16.5 Case Study<br/>Analysis of Engineering Colleges in India | Analysis of Start-up Funding in India in 2015–2020<br/>Practice Problems<br/><br/>Chapter 17: Supervised Learning<br/>17.1 Overview<br/>17.2 Supervised versus Unsupervised Learning<br/>17.3 Tree-based Methods<br/>CART and CHAID | Ensemble Learning and Random Forests<br/>17.4 Some Classification Algorithms<br/>Logistic Regression in Classification | KNN Classification | Support Vector Machine (SVM) | Naïve Bayes method<br/>17.5 Discriminant Analysis<br/>Linear Discriminant Analysis | Quadratic Discriminant Analysis<br/>17.6 Case Studies<br/>Understanding the Musical Cure | Decoding Comments on a YouTube Channel<br/>Practice Problems<br/><br/>Chapter 18: Unsupervised Learning<br/>18.1 Dimensionality Reduction<br/>Principal Component Analysis | Principal Component Regression | t-Distributed Stochastic Neighbour Embedding<br/>18.2 Clustering<br/>Evaluating a Clustering Algorithm | k-means and k-medoids Algorithm | DBSCAN Algorithm | Hierarchical Clustering<br/>18.3 Anomaly Detection<br/>Simple Statistical Methods | Isolation Forest<br/>18.4 Case Studies<br/>Does Weather Affect the Demand for Bikes? | Understanding the Indian Stock Market<br/>Practice Problems<br/><br/>Chapter 19: Forecasting<br/>19.1 Introduction to Forecasting Problems<br/>19.2 Time Series Data and Forecasting<br/>Important Features of Time Series Data | Stationarity and Temporal Autocorrelation | Decomposition of Time Series<br/>19.3 Some Simple Forecasting Approaches<br/>Naïve and Seasonal Naïve Method | Mean Method | Drift Method | Decomposition Method<br/>19.4 Exponential Smoothing Technique<br/>19.5 The ARIMA and SARIMA Models<br/>19.6 Classification and Regression Models in Forecasting<br/>Case Study 1: The Ed-tech Story | Case Study 2: Where Should You Buy a House? | The SARIMAX Model | Case Study 3: Analysing the Sales of the French Bakery<br/>Practice Problems<br/><br/>Chapter 20: Comprehensive Data Analysis and the Way Forward<br/>20.1 ESG Insights for Policymakers – A Statistical Investigation<br/>Comprehensive Data Analysis | Scope of Advanced Analytics with GAM or Multilevel Models<br/>20.2 Data-Driven Insights at UrbanMart Superstore<br/>Comprehensive Data Analysis | Scope of Advanced Analytics with Dashboards, Neural Networks, and Causal Inference<br/>20.3 Air Pollution in Delhi: Can it be Managed with Analytical Tools?<br/>Comprehensive Data Analysis | Scope of Advanced Analytics with Spatio- Temporal Models and Extreme Value Theory<br/>20.4 Decoding NIFTY 50 through Risk, Return, and Portfolio Strategy<br/>Comprehensive Data Analysis | Scope of Advanced Analytics Using GARCH Models and Other Techniques<br/>20.5 How Can Victory Sports Increase their Sales?<br/>Comprehensive Data Analysis | Scope of Advanced Analytics with Digital Behaviour Data<br/>20.6 Concluding Remarks<br/><br/>Appendix Probability Tables<br/><br/>Table A.1 CDF of binomial distribution, that is, P(X ≤ x), where X follows binomial distribution for some choices of n and p<br/>Table A.2 CDF of Poisson distribution, that is, P(X ≤ x), where X follows Poisson distribution for some choices of m<br/>Table A.3 Percentiles (cut-off points corresponding to left-tail area) of chi-square distribution<br/>Table A.4 Percentiles (cut-off points corresponding to left-tail area) of T distribution<br/>Table A.5 Cut-off points from right-tail area (p = 1%, 2.5%, 5%, 10%) of F distribution with numerator d.f. n1 and denominator d.f. n2<br/>Table A.6 Mass/Density function of Mann–Whitney test statistic when m (larger sample size) is 2, 3, or 4 and n ≤ m<br/><br/>Index |
| 650 ## - Subject | |
| Subject | BUSINESS ANALYTICS |
| 700 ## - Added Entry Personal Name | |
| Relator Code | auth. |
| Added Entry Personal Name | Deb, Soudeep |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | COMMERCE Reference |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Koha collection | Location (home branch) | Sublocation or collection (holding branch) | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Serial Enumeration / chronology | Koha issues (times borrowed) | Koha full call number | Barcode (Accession No.) | Koha date last seen | Copy Number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Not For Loan | Reference | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Reference Section | 02/19/2026 | K.M. Enterprise | 1250.00 | S.X.U.K | R 658.4 DAS(BUS) | UL13925 | 02/19/2026 | 13925 | 02/19/2026 | COMMERCE Reference | ||||
| Dewey Decimal Classification | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Lending Section | 02/19/2026 | K.M. Enterprise | 1250.00 | S.X.U.K | 658.4 DAS(BUS) | L13926 | 02/19/2026 | 13926 | 02/19/2026 | Commerce Books | ||||||
| Dewey Decimal Classification | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Lending Section | 02/19/2026 | K.M. Enterprise | 1250.00 | S.X.U.K | 658.4 DAS(BUS)C1 | L13927 | 02/19/2026 | 13927 | 02/19/2026 | Commerce Books |