Introduction to data science: (Record no. 5005)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04956nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230322102109.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230322b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789354640506 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.312 |
| Item number | MAH |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Maheswari, B. Uma |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to data science: |
| Remainder of title | practical approach with R and Python |
| 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. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxvi, 557 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 729.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of content<br/>Chapter 1 Introduction to Data Science<br/><br/>1.1 Data Science<br/><br/>1.2 Brief History of Data Science<br/><br/>1.3 Increasing Attention to Data Science<br/><br/>1.4 Fundamental Fields of Study Related to Data Science<br/><br/>1.5 Data Science and Related Terminologies<br/><br/>1.6 Types of Analytics<br/><br/>1.7 Applications of Data Science<br/><br/>1.8 Data Science Process Model<br/><br/> <br/><br/>Chapter 2 Introduction to R and Python<br/><br/>2.1 Introduction<br/><br/>2.2 R and RStudio Environment<br/><br/>2.3 Basics of R<br/><br/>2.4 Python Language and Python Environment<br/><br/>2.5 Basics of Python<br/><br/> <br/><br/>Chapter 3 Exploratory Data Analysis<br/><br/>3.1 Introduction<br/><br/>3.2 Steps in Data Preprocessing<br/><br/>3.3 Understanding Data<br/><br/>3.3.1 Steps Involved in EDA Using R Programming<br/><br/>3.4 Looking at the Data<br/><br/>3.5 Visualizing Data<br/><br/>3.6 Dealing with Outliers<br/><br/>3.7 Dealing with Missing Values<br/><br/>3.8 Standardizing Data<br/><br/>3.9 Steps Involved in EDA Using Python Programming<br/><br/>3.10 Looking at the Data<br/><br/>3.11 Visualization the Data<br/><br/>3.12 Treatment of Outliers<br/><br/> <br/><br/>Chapter 4 Data Visualization<br/><br/>4.1 Introduction<br/><br/>4.2 Data Visualization for Machine Learning<br/><br/>4.3 Data Visualization Techniques<br/><br/>4.4 Simple Data Visualization Using R<br/><br/>4.5 Data Visualization Using Ggplots in R<br/><br/>4.6 Data Visualization Using Python<br/><br/>4.7 Matplotlib Library<br/><br/>4.8 Seaborn Library<br/><br/> <br/><br/>Chapter 5 Dimensionality Reduction Techniques<br/><br/>5.1 Dimensionality Reduction<br/><br/>5.2 Independent and Dependent Variables<br/><br/>5.3 Relationship between Variables: Correlation<br/><br/>5.5 Factor Analysis<br/><br/>5.5.4 Rotated Factor Matrix<br/><br/>5.6 Application of Factor Analysis Using Python Programming<br/><br/> <br/><br/>Chapter 6 Types of Machine Learning Algorithms<br/><br/>6.1 Introduction<br/><br/>6.2 Supervised and Unsupervised Learning Algorithms<br/><br/>6.3 Supervised Learning Algorithm<br/><br/>6.4 Unsupervised Learning Algorithm<br/><br/> <br/><br/>Chapter 7 Unsupervised Learning Algorithms<br/><br/>7.1 Introduction<br/><br/>7.2 Association Rule Mining<br/><br/>7.3 Conjoint Analysis<br/><br/>7.4 Clustering<br/><br/>7.5 K Means Clustering<br/><br/> <br/><br/>Chapter 8 Text Analytics<br/><br/>8.1 Introduction<br/><br/>8.2 Unstructured Data<br/><br/>8.3 Word Cloud<br/><br/>8.4 Sentiment Analysis<br/><br/>8.5 Web and Social Media Analytics<br/><br/> <br/><br/>Chapter 9 Supervised Learning Algorithms: Linear and Logistic Regression<br/><br/>9.1 Introduction<br/><br/>9.2 Simple Linear Regression<br/><br/>9.3 Multiple Linear Regression<br/><br/>9.4 Logistic Regression<br/><br/> <br/><br/>Chapter 10 Supervised Learning Algorithms: Decision Tree and Random Forest<br/><br/>10.1 Decision Tree<br/><br/>10.2 Classification and Regression Technique<br/><br/>10.3 Random Forest<br/><br/> <br/><br/>Chapter 11 Supervised Learning Algorithm: KNN, Naïve Bayes, and Linear Discriminant Analysis<br/><br/>11.1 K-Nearest Neighbors<br/><br/>11.2 Naïve Bayes Algorithm<br/><br/>11.3 Linear Discriminant Analysis<br/><br/> <br/><br/>Chapter 12 Support Vector Machines and Artificial Neural Networks<br/><br/>12.1 Support Vector Machines<br/><br/>12.2 Artificial Neural Networks<br/><br/> <br/><br/>Chapter 13 Time Series Forecasting<br/><br/>13.1 Introduction<br/><br/>13.2 Time Series Data<br/><br/>13.3 Visualizing the Time Series Data<br/><br/>13.4 Components of Time Series Data<br/><br/>13.5 Stationarity of the Data<br/><br/>13.6 Exponential Smoothening Model<br/><br/>13.7 Holt–Winters Model<br/><br/>13.8 ARIMA Model<br/><br/> <br/><br/>Chapter 14 Ensemble Methods<br/><br/>14.1 Introduction<br/><br/>14.2 Dealing with Imbalanced Data<br/><br/>14.3 Ensemble Methods<br/><br/>14.4 Bias Variance Tradeoff<br/><br/>14.5 Bagging<br/><br/>14.6 Boosting<br/><br/>14.7 Synthetic Minority over Sampling Technique (SMOTE)<br/><br/> <br/><br/>Chapter 15 Artificial Intelligence<br/><br/>15.1 Introduction<br/><br/>15.2 Artificial Intelligence<br/><br/>15.3 Deep Learning<br/><br/>15.4 Convolutional Neural Networks<br/><br/>15.5 Reinforcement Learning<br/><br/> <br/><br/>Chapter 16 Applications of Analytics<br/><br/>16.1 Introduction<br/><br/>16.2 Application of Analytics in Healthcare<br/><br/>16.3 Application of Analytics in Agriculture<br/><br/>16.4 Application of Analytics in Business<br/><br/>16.5 Application of Analytics in Sports<br/><br/>16.6 Application of Analytics in Governance<br/><br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Introduction to Data Science: Practical Approach with R and Python covers all the fundamental concepts of Data Science in a concise manner. It offers a mix of insights and golden rules which would be needed in analyzing data. This book serves as a practical guide for Science/Engineering/MBA students – both at the undergraduate and postgraduate level interested in Data Science domain. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Big data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sujatha, R. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| 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 | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB3162 | 16-02-2023 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/22/2023 | Technical Bureau India Pvt. Ltd. | 510.30 | 006.312 MAH | 004864 | 03/22/2023 | 1 | 729.00 | 03/22/2023 | Book |