This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case…mehr
This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
Gábor Békés is an assistant professor at the Department of Economics and Business of the Central European University, and Director of the Business Analytics Program. He is a senior fellow at KRTK and a research affiliate at the Center for Economic Policy Research (CEPR). He has published in top economics journals on multinational firm activities and productivity, business clusters, and innovation spillovers. He has managed international data collection projects on firm performance and supply chains. He has done policy advising (the European Commission, ECB) as well as private-sector consultancy (in finance, business intelligence, and real estate). He has taught graduate-level data analysis and economic geography courses since 2012.
Inhaltsangabe
Part I. Data Exploration: 1. Origins of data; 2. Preparing data for analysis; 3. Exploratory data analysis; 4. Comparison and correlation; 5. Generalizing from data; 6. Testing hypotheses; Part II. Regression Analysis: 7. Simple regression; 8. Complicated patterns and messy data; 9. Generalizing results of a regression; 10. Multiple linear regression; 11. Modeling probabilities; 12. Regression with time series data; Part III. Prediction: 13. A framework for prediction; 14. Model building for prediction; 15. Regression trees; 16. Random forest and boosting; 17. Probability prediction and classification; 18. Forecasting from time series data; Part IV. Causal Analysis: 19. A framework for causal analysis; 20. Designing and analyzing experiments; 21. Regression and matching with observational data; 22. Difference-in-differences; 23. Methods for panel data; 24. Appropriate control groups for panel data; Bibliography; Index.
Part I. Data Exploration: 1. Origins of data 2. Preparing data for analysis 3. Exploratory data analysis 4. Comparison and correlation 5. Generalizing from data 6. Testing hypotheses Part II. Regression Analysis: 7. Simple regression 8. Complicated patterns and messy data 9. Generalizing results of a regression 10. Multiple linear regression 11. Modeling probabilities 12. Regression with time series data Part III. Prediction: 13. A framework for prediction 14. Model building for prediction 15. Regression trees 16. Random forest and boosting 17. Probability prediction and classification 18. Forecasting from time series data Part IV. Causal Analysis: 19. A framework for causal analysis 20. Designing and analyzing experiments 21. Regression and matching with observational data 22. Difference-in-differences 23. Methods for panel data 24. Appropriate control groups for panel data Bibliography Index.
Part I. Data Exploration: 1. Origins of data; 2. Preparing data for analysis; 3. Exploratory data analysis; 4. Comparison and correlation; 5. Generalizing from data; 6. Testing hypotheses; Part II. Regression Analysis: 7. Simple regression; 8. Complicated patterns and messy data; 9. Generalizing results of a regression; 10. Multiple linear regression; 11. Modeling probabilities; 12. Regression with time series data; Part III. Prediction: 13. A framework for prediction; 14. Model building for prediction; 15. Regression trees; 16. Random forest and boosting; 17. Probability prediction and classification; 18. Forecasting from time series data; Part IV. Causal Analysis: 19. A framework for causal analysis; 20. Designing and analyzing experiments; 21. Regression and matching with observational data; 22. Difference-in-differences; 23. Methods for panel data; 24. Appropriate control groups for panel data; Bibliography; Index.
Part I. Data Exploration: 1. Origins of data 2. Preparing data for analysis 3. Exploratory data analysis 4. Comparison and correlation 5. Generalizing from data 6. Testing hypotheses Part II. Regression Analysis: 7. Simple regression 8. Complicated patterns and messy data 9. Generalizing results of a regression 10. Multiple linear regression 11. Modeling probabilities 12. Regression with time series data Part III. Prediction: 13. A framework for prediction 14. Model building for prediction 15. Regression trees 16. Random forest and boosting 17. Probability prediction and classification 18. Forecasting from time series data Part IV. Causal Analysis: 19. A framework for causal analysis 20. Designing and analyzing experiments 21. Regression and matching with observational data 22. Difference-in-differences 23. Methods for panel data 24. Appropriate control groups for panel data Bibliography Index.
Rezensionen
'This exciting new text covers everything today's aspiring data scientist needs to know, managing to be comprehensive as well as accessible. Like a good confidence interval, the Gabors have got you almost completely covered!' Joshua Angrist, Massachusetts Institute of Technology
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