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Abstract: Stata basics - Data management and graphics - Linear regression basics - Linear regression extensions - Simulation - Linear regression with correlated errors - Linear instrumental-variables regression - Linear panel-data models: basics - Linear panel-data, models: extensions Introduction to nonlinear regressin - Tests of hypotheses and model specification - Bootstrap methods - Nonlinear regression methods - Flexible regression: finite mixtures and nonparametric - Quantile regression.

Abstract: Nonlinear optimization methods - Binary outcome models - Multinomial models - Tobit selection models - Count-data models - survival analysis for duration data - Nonlinear panel models - Parametric models for heterogeneity and endogeneity - Randomized controls trials and exogenous treatment effects - Endogenous treatment effects - Spatial regression - Semiparametric regression - Machine learning for prediction and inference - Bayesian methods: basics - Bayesian methods: Markov chain Mon ...; [Read more...] Nonlinear optimization methods - Binary outcome models - Multinomial models - Tobit selection models - Count-data models - survival analysis for duration data - Nonlinear panel models - Parametric models for heterogeneity and endogeneity - Randomized controls trials and exogenous treatment effects - Endogenous treatment effects - Spatial regression - Semiparametric regression - Machine learning for prediction and inference - Bayesian methods: basics - Bayesian methods: Markov chain Monte Carlo algorithms.

Abstract: Introduction to the GRE - Multi-stage test mechanics - Introduction to verbal reasoning - Verbal foundations and content review - Quantitative reasoning - Analytical writing - Practice test - Test day and beyond - GRE resources.

Abstract: The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensi ...; [Read more...] The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

Abstract: Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects ...; [Read more...] Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.

Abstract: What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and ...; [Read more...] What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and value of your data and applications. You'll also explore pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data"--. Part 1. The basics. Mind the semantic gap -- Semantic modeling elements -- Semantic and linguistic phenomena -- Semantic model quality -- Semantic model development -- Part 2. The pitfalls. Bad descriptions -- Bad semantics -- Bad model specification and knowledge acquisition -- Bad quality management -- Bad application -- Bad strategy and organization -- Part 3. The dilemmas. Representation dilemmas -- Expressiveness and content dilemmas -- Evolution and governance dilemmas -- Looking ahead.

Abstract: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you s ...; [Read more...] Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.

Abstract: Forecasting with the linear regression model - Forecasting with time series models - TAR, Markov switching, and state space models - Mixed frequency, large datasets, and volatility.