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Data Science for Public Policy 1st Edition by Jeffrey C Chen, Edward A Rubin, Gary J Cornwall ISBN 9783030713515 3030713512

Name: Data Science for Public Policy 1st Edition by Jeffrey C Chen, Edward A Rubin, Gary J Cornwall ISBN 9783030713515 3030713512
SKU: EB-34402284
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Data Science for Public Policy (Springer Series in the Data Sciences) Jeffrey C. Chen Digital Instant Download

Author(s): Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall

ISBN(s): 9783030713515, 3030713512

Edition: 1st ed. 2021

File Details: PDF, 19.56 MB

Year: 2021

Language: English

SKU: EB-34402284 Category: Mathematics Tags: Edward A. Rubin, Gary J. Cornwall, Jeffrey C. Chen

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Data Science for Public Policy 1st Edition by Jeffrey C Chen, Edward A Rubin, Gary J Cornwall – Ebook PDF Instant Download/Delivery: 9783030713515 ,3030713512
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Product details:

ISBN 10: 3030713512
ISBN 13: 9783030713515
Author: Jeffrey C Chen, Edward A Rubin, Gary J Cornwall

This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

Data Science for Public Policy 1st Edition Table of contents:

1. An Introduction

2. The Case for Programming

3. Elements of Programming

4. Transforming Data

5. Record Linkage

6. Exploratory Data Analysis

7. Regression Analysis

8. Framing Classification

9. Three Quantitative Perspectives

10. Prediction

11. Cluster Analysis

12. Spatial Data

13. Natural Language

14. The Ethics of Data Science

15. Developing Data Products

16. Building Data Teams

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Tags: Jeffrey C Chen, Edward A Rubin, Gary J Cornwall, Data Science, Public Policy