Data science and analytics with python
Arora, Sandhya
Data science and analytics with python Sandhya Arora & Latesh Malik - Hyderabad Universities Press c2023 - x, 488 P.B
Preface
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
9789393330345 750.00
PYTHON
DATA SCIENCE
R 006.312 ARO(DAT)
Data science and analytics with python Sandhya Arora & Latesh Malik - Hyderabad Universities Press c2023 - x, 488 P.B
Preface
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
9789393330345 750.00
PYTHON
DATA SCIENCE
R 006.312 ARO(DAT)