Introduction to Statistical and Machine Learning Methods for Data Science
- 3h 14m
- Carlos Andre Reis Pinheiro, Mike Patetta
- SAS Institute
- 2021
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.
In this Book
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Introduction to Data Science
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Data Exploration and Preparation
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Supervised Models – Statistical Approach
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Supervised Models – Machine Learning Approach
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Advanced Topics in Supervised Models
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Unsupervised Models—Structured Data
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Unsupervised Models—Semi Structured Data
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Advanced Topics in Unsupervised Models
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Model Assessment and Model Deployment
