This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of "your turn" exercises.
At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inferenceis illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods--generalized additive models and random forests (an important and versatile machine learning method)--are introduced intuitively with applications.
The book will be of great interest to economists--students, teachers, and researchers alike--who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vikram Dayal is a Professor at the Institute of Economic Growth, Delhi. He has been using the R software in teaching quantitative economics to diverse audiences, and is the author of the Springer Brief titled An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing. He has published research on a range of environmental and developmental issues, from outdoor and indoor air pollution in Goa, India, to tigers and Prosopis juliflora in Ranthambore National Park. He studied economics in India and the USA, and received his doctoral degree from the Delhi School of Economics, University of Delhi.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Etat : Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9789811520372. N° de réf. du vendeur 3977820
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a contemporary treatment of quantitative economics, with a focus ondata science. The book introduces the readerto Rand RStudio, and uses expert Hadley Wickham's tidyverse packagefor different parts of the data analysis workflow. Aftera gentleintroductionto R code, the reader'sR skills are gradually honed, with the help of 'your turn' exercises.At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the readerwill beginusing the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operationsisalsocovered.The book uses Monte Carlo simulation tounderstandprobability and statistical inference, and the bootstrap isintroduced. Causal inferenceis illuminated using simulation, data graphs,and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models ispresented, before the book introduces the reader to time series data analysis with graphs, simulation,andexamples.Lastly, twocomputationally intensivemethods-generalized additive models andrandom forests (an important and versatile machine learning method)-are introduced intuitively with applications.The book will beof great interest to economists-students, teachers,and researchersalike-who want to learn R.It will help economics students gainan intuitive appreciation of applied economicsand enjoy engagingwith the material actively, while also equipping them with key data science skills. 344 pp. Englisch. N° de réf. du vendeur 9789811520372
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Employs a popular data science approach while discussing concepts and applications related to economicsExplains causal inferences with the aid of simulations, data graphs, and sample applicationsIntroduces readers to two versati. N° de réf. du vendeur 440126287
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of 'your turn' exercises.At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inferenceis illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods-generalized additive models and random forests (an important and versatile machine learning method)-are introduced intuitively with applications.The book will be of great interest to economists-students, teachers, and researchers alike-who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 344 pp. Englisch. N° de réf. du vendeur 9789811520372
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