Data Science and Machine Learning are the leading buzzwords of today.
This book covers all aspects of these subjects, from data definition and categorization, classification techniques, clustering and ML algorithms to data stream and association rule mining, language data processing and neural networks. It explains descriptive and inferential statistical analysis, probability distribution and density functions as well as time series. It also describes the fundamentals of Python programming, the Python environment and libraries such as scikit-learn, NumPy and pandas, and takes a deep dive into data visualization modules and tools.
Mastery of these areas will enable students to become proficient and effective data scientists.
Salient features

• Ideal for undergraduate courses on Data Science and Analytics
• Provides step-by-step instructions for setting up the Python environment and executing various libraries and packages
• All chapters include relevant case studies, their Python code and output; the last chapter is dedicated to case studies
• Over 300 exercise questions comprising MCQs, programming exercises and concept-based questions, with answers provided for quick reference
• Bibliography at the end of every chapter for further reading
• Android app with chapter-wise PowerPoint slides and job interview questions

Chapter-wise PowerPoint slides are available at: www.universitiespress.com/DataScienceandAnalyticswithPython

Sandhya Arora is Professor, Department of Computer Engineering, MKSSS’s Cummins College of Engineering, Pune, Maharashtra.

Latesh Malik is Associate Professor, Department of Computer Science and Engineering, Government College of Engineering, Nagpur, Maharashtra.

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