000			04926nam a2200205 4500
005			20260212155742.0
008			260212b |||||||| |||| 00| 0 eng d
020			_a9789393330345 _c750.00
040			_aS.X.U.K
041			_aEnglish
082			_aR 006.312 ARO(DAT)
100			_aArora, Sandhya
245			_aData science and analytics with python _cSandhya Arora & Latesh Malik
260			_aHyderabad _bUniversities Press _cc2023
300			_ax, 488 _bP.B
500			_aPreface Acknowledgements Chapter 1: Introduction to Data Science 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 Chapter 2: Environment Set-up and Basics of Python 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 Chapter 3: NumPy and pandas Arrays | NumPy | The pandas Package | Panels Chapter 4: Data Visualization Introduction | Visualization Software and Tools | Interactive Visual Analysis | Text Visualization | Creating Graphs with Matplotlib | Creating Graphs with the plotly Package | Data Visualization with Matplotlib, Seaborn and pandas | Exploratory Data Analysis | Mapping and Cartopy Chapter 5: Python scikit-learn Introduction | Features of scikit-learn | Installation | Regression and Classifiers in scikit-learn | Support Vector Machine (SVM) | K-Nearest Neighbor (K-NN) | Case Studies Chapter 6: Environment Set-up: TensorFlow and Keras Introduction to TensorFlow | TensorFlow Features | Benefits of TensorFlow | Installation of TensorFlow | TensorFlow Architecture | Introduction to Keras | Installation of Keras | Features of Keras | Programming in Keras Chapter 7: Probability Introduction to Probability | Probability and Statistics | Random Variables | Central Limit Theorem | Density Functions | Probability Distribution Chapter 8: Machine Learning and Data Pre-processing Introduction to Machine Learning | Need for Machine Learning | Types of Machine Learning | Understanding Data | Data Set and Data Types | Data Pre-processing | Data Pre-processing in Python Chapter 9: Statistical Analysis: Descriptive Statistics Introduction | One-dimensional Statistics | Multi-dimensional Statistics | Simpson’s Paradox Chapter 10: Statistical Analysis: Inferential Statistics Introduction | Hypothesis Testing | Using the t-test | The t-test in Python | Chi-square Test | Wilcoxon Rank-Sum Test | Introduction to Analysis of Variance Chapter 11: Classification Introduction | K-NN Classification | Decision Trees | Support Vector Machine (SVM) | Naive Bayes’ Classification | Metrics for Evaluating Classifier Performance | Cross-validation | Ensemble Methods: Techniques to Improve Classification Accuracy Chapter 12: Prescriptive Analytics: Data Stream Mining Introduction to Stream Concepts | Mining Data Streams | Data Stream Management System (DSMS) | Data Stream Models | Data Stream Filtering | Sampling Data in a Stream | Concept Drift | Data Stream Classification | Rare Class Problem | Issues, Controversies and Problems | Applications of Data Mining | Implementation of Data Streams in Python Chapter 13: Language Data Processing in Python Natural Language Processing | Text Processing in Python | CGI/Web Programming Using Python | Twitter Sentiment Analysis in Python | Twitter Sentiment Analysis for Film Reviews | Case Study: A Recommendation System for a Film Data Set | Case Study: Text Mining and Visualization in Word Clouds Chapter 14: Clustering Introduction | Distance Measures | K-means Clustering | Hierarchical Clustering | DBSCAN Clustering Chapter 15: Association Rule Mining Introduction | The Apriori Algorithm | An Example of an Apriori Algorithm | An Example Using Python: Transactions in a Grocery Store Chapter 16: Time Series Analysis Using Python Introduction | Components of a Time Series | Additive and Multiplicative Time Series | Time Series Analysis | Case Study on Time Series Analysis Chapter 17: Deep Neural Network and Convolutional Neural Network Overview of Feed Forward Neural Network | Overview of Deep Neural Network | Activation Function | Loss Functions | Regularization | Convolutional Neural Network | Implementation of CNN | Case Studies Chapter 18: Case Studies Digit Recognition | Face and Eye Detection in Images | Correlation and Feature Selection | Fake News Detection | Detecting Duplicate Questions | Weather Prediction and Song Recommendation System | Spam Detection Index
650			_aPYTHON _aDATA SCIENCE
700			_4auth. _a Malik, Latesh
942			_cUS
999			_c14340 _d14340