Data science and analytics with python (Record no. 14340)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04926nam a2200205 4500 |
| 005 - DATE & TIME | |
| control field | 20260212155742.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260212b |||||||| |||| 00| 0 eng d |
| 020 ## - ISBN | |
| International Standard Book Number | 9789393330345 |
| Price | 750.00 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | S.X.U.K |
| 041 ## - Language | |
| Language | English |
| 082 ## - DDC NUMBER | |
| Classification number | R 006.312 ARO(DAT) |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Arora, Sandhya |
| 245 ## - TITLE STATEMENT | |
| Title | Data science and analytics with python |
| Statement of responsibility | Sandhya Arora & Latesh Malik |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Hyderabad |
| Name of publisher, distributor, etc | Universities Press |
| Date of publication, distribution, etc | c2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | x, 488 |
| Other Details | P.B |
| 500 ## - GENERAL NOTE | |
| General note | Preface<br/>Acknowledgements<br/>Chapter 1: Introduction to Data Science<br/>Introduction | Data Science | Data Science Stages | Data Science Ecosystem | Tools Used in Data Science | Data Science Workflow | Automated Methods for Data Collection | Overview of Data | Sources of Data | Big Data | Data Categorization<br/><br/>Chapter 2: Environment Set-up and Basics of Python<br/>Introduction to Python | Features of Python | Installation of Python | Python Identifiers | Python Indentation | Comments in Python | Basic Data | Operators and Expressions | Data Types | Sets and Frozen Sets | Loops and Conditions | Classes and Functions | Working with Files<br/><br/>Chapter 3: NumPy and pandas<br/>Arrays | NumPy | The pandas Package | Panels<br/><br/>Chapter 4: Data Visualization<br/>Introduction | Visualization Software and Tools | Interactive Visual Analysis | Text Visualization | Creating Graphs with Matplotlib | Creating Graphs with the plotly Package<br/>| Data Visualization with Matplotlib, Seaborn and pandas | Exploratory Data Analysis | Mapping and Cartopy<br/><br/>Chapter 5: Python scikit-learn<br/>Introduction | Features of scikit-learn | Installation | Regression and Classifiers in scikit-learn | Support Vector Machine (SVM) | K-Nearest Neighbor (K-NN) | Case Studies<br/><br/>Chapter 6: Environment Set-up: TensorFlow and Keras<br/>Introduction to TensorFlow | TensorFlow Features | Benefits of TensorFlow | Installation of TensorFlow | TensorFlow Architecture | Introduction to Keras | Installation of Keras |<br/>Features of Keras | Programming in Keras<br/><br/>Chapter 7: Probability<br/>Introduction to Probability | Probability and Statistics | Random Variables | Central Limit Theorem | Density Functions | Probability Distribution<br/><br/>Chapter 8: Machine Learning and Data Pre-processing<br/>Introduction to Machine Learning | Need for Machine Learning | Types of Machine Learning | Understanding Data | Data Set and Data Types | Data Pre-processing | Data<br/>Pre-processing in Python<br/><br/>Chapter 9: Statistical Analysis: Descriptive Statistics<br/>Introduction | One-dimensional Statistics | Multi-dimensional Statistics | Simpson’s Paradox<br/><br/>Chapter 10: Statistical Analysis: Inferential Statistics<br/>Introduction | Hypothesis Testing | Using the t-test | The t-test in Python | Chi-square Test | Wilcoxon Rank-Sum Test | Introduction to Analysis of Variance<br/><br/>Chapter 11: Classification<br/>Introduction | K-NN Classification | Decision Trees | Support Vector Machine (SVM) | Naive Bayes’ Classification | Metrics for Evaluating Classifier Performance | Cross-validation<br/>| Ensemble Methods: Techniques to Improve Classification Accuracy<br/><br/>Chapter 12: Prescriptive Analytics: Data Stream Mining<br/>Introduction to Stream Concepts | Mining Data Streams | Data Stream Management System (DSMS) | Data Stream Models | Data Stream Filtering | Sampling Data in a Stream<br/>| Concept Drift | Data Stream Classification | Rare Class Problem | Issues, Controversies and Problems | Applications of Data Mining | Implementation of Data Streams in Python<br/><br/>Chapter 13: Language Data Processing in Python<br/>Natural Language Processing | Text Processing in Python | CGI/Web Programming Using Python | Twitter Sentiment Analysis in Python | Twitter Sentiment Analysis for Film<br/>Reviews | Case Study: A Recommendation System for a Film Data Set | Case Study: Text Mining and Visualization in Word Clouds<br/><br/>Chapter 14: Clustering<br/>Introduction | Distance Measures | K-means Clustering | Hierarchical Clustering | DBSCAN Clustering<br/><br/>Chapter 15: Association Rule Mining<br/>Introduction | The Apriori Algorithm | An Example of an Apriori Algorithm | An Example Using Python: Transactions in a Grocery Store<br/><br/>Chapter 16: Time Series Analysis Using Python<br/>Introduction | Components of a Time Series | Additive and Multiplicative Time Series | Time Series Analysis | Case Study on Time Series Analysis<br/><br/>Chapter 17: Deep Neural Network and Convolutional Neural Network<br/>Overview of Feed Forward Neural Network | Overview of Deep Neural Network | Activation Function | Loss Functions | Regularization | Convolutional Neural Network |<br/>Implementation of CNN | Case Studies<br/><br/>Chapter 18: Case Studies<br/>Digit Recognition | Face and Eye Detection in Images | Correlation and Feature Selection | Fake News Detection | Detecting Duplicate Questions | Weather Prediction and Song<br/>Recommendation System | Spam Detection<br/><br/>Index |
| 650 ## - Subject | |
| Subject | PYTHON |
| -- | DATA SCIENCE |
| 700 ## - Added Entry Personal Name | |
| Relator Code | auth. |
| Added Entry Personal Name | Malik, Latesh |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | REFERENCE STATISTICS |
| 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 | Koha out on loan | Koha date last borrowed |
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| Dewey Decimal Classification | Not For Loan | Reference | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Reference Section | 02/12/2026 | K.M. Enterprise | 750.00 | S.X.U.K | R 006.312 ARO(DAT) | US13907 | 02/12/2026 | 13907 | 02/12/2026 | REFERENCE STATISTICS | ||||||
| Dewey Decimal Classification | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Lending Section | 02/12/2026 | K.M. Enterprise | 750.00 | S.X.U.K | 006.312 ARO(DAT) | S13908 | 02/12/2026 | 13908 | 02/12/2026 | STATISTICS | ||||||||
| Dewey Decimal Classification | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Lending Section | 02/12/2026 | K.M. Enterprise | 750.00 | S.X.U.K | 1 | 006.312 ARO(DAT)C1 | S13909 | 04/01/2026 | 13909 | 02/12/2026 | STATISTICS | 04/16/2026 | 04/01/2026 |