Introduction to Statistical and Machine Learning Methods for Data Science
- Length: 230 pages
- Edition: 1
- Language: English
- Publisher: SAS Institute
- Publication Date: 2021-08-06
- ISBN-10: B09C2DVZHR
- Sales Rank: #0 (See Top 100 Books)
Boost your understanding of data science techniques to solve real-world problems
Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need.
No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
About This Book About These Authors Acknowledgments Foreword Chapter 1: Introduction to Data Science Chapter Overview Data Science Mathematics and Statistics Computer Science Domain Knowledge Communication and Visualization Hard and Soft Skills Data Science Applications Data Science Lifecycle and the Maturity Framework Understand the Question Collect the Data Explore the Data Model the Data Provide an Answer Advanced Analytics in Data Science Data Science Practical Examples Customer Experience Revenue Optimization Network Analytics Data Monetization Summary Additional Reading Chapter 2: Data Exploration and Preparation Chapter Overview Introduction to Data Exploration Nonlinearity High Cardinality Unstructured Data Sparse Data Outliers Mis-scaled Input Variables Introduction to Data Preparation Representative Sampling Event-based Sampling Partitioning Imputation Replacement Transformation Feature Extraction Feature Selection Model Selection Model Generalization Bias–Variance Tradeoff Summary Chapter 3: Supervised Models – Statistical Approach Chapter Overview Classification and Estimation Linear Regression Use Case: Customer Value Logistic Regression Use Case: Collecting Predictive Model Decision Tree Use Case: Subscription Fraud Summary Chapter 4: Supervised Models – Machine Learning Approach Chapter Overview Supervised Machine Learning Models Ensemble of Trees Random Forest Gradient Boosting Use Case: Usage Fraud Neural Network Use Case: Bad Debt Summary Chapter 5: Advanced Topics in Supervised Models Chapter Overview Advanced Machine Learning Models and Methods Support Vector Machines Use Case: Fraud in Prepaid Subscribers Factorization Machines Use Case: Recommender Systems Based on Customer Ratings in Retail Ensemble Models Use Case Study: Churn Model for Telecommunications Two-stage Models Use Case: Anti-attrition Summary Additional Reading Chapter 6: Unsupervised Models—Structured Data Chapter Overview Clustering Hierarchical Clustering Use Case: Product Segmentation Centroid-based Clustering (k-means Clustering) Use Case: Customer Segmentation Self-organizing Maps Use Case Study: Insolvent Behavior Cluster Evaluation Cluster Profiling Additional Topics Summary Additional Reading Chapter 7: Unsupervised Models—Semi Structured Data Chapter Overview Association Rules Analysis Market Basket Analysis Confidence and Support Measures Use Case: Product Bundle Example Expected Confidence and Lift Measures Association Rules Analysis Evaluation Use Case: Product Acquisition Sequence Analysis Use Case: Next Best Offer Link Analysis Use Case: Product Relationships Path Analysis Use Case Study: Online Experience Text Analytics Use Case Study: Call Center Categorization Summary Additional Reading Chapter 8: Advanced Topics in Unsupervised Models Chapter Overview Network Analysis Network Subgraphs Network Metrics Use Case: Social Network Analysis to Reduce Churn in Telecommunications Network Optimization Network Algorithms Use Case: Smart Cities – Improving Commuting Routes Summary Chapter 9: Model Assessment and Model Deployment Chapter Overview Methods to Evaluate Model Performance Speed of Training Speed of Scoring Business Knowledge Fit Statistics Data Splitting K-fold Cross-validation Goodness-of-fit Statistics Confusion Matrix ROC Curve Model Evaluation Model Deployment Challenger Models Monitoring Model Operationalization Summary
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.