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Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Analytics Modeling.
Classic textbook on statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Provides a comprehensive overview of predictive modeling techniques, including linear regression, logistic regression, decision trees, and neural networks. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Classic textbook on reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradients. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Classic textbook on econometrics. It covers a wide range of topics, including linear regression, logistic regression, and panel data analysis. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Comprehensive textbook on forecasting. It covers a wide range of topics, including time series analysis, exponential smoothing, and ARIMA models. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Comprehensive textbook on clinical data analysis. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Comprehensive textbook on deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Gentle introduction to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It is written in a clear and engaging style, and it includes numerous examples and exercises to help readers understand the concepts.
Gentle introduction to Bayesian modeling. It covers a wide range of topics, including Bayesian inference, Markov chain Monte Carlo, and hierarchical models. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Gentle introduction to causal inference. It covers a wide range of topics, including the do-calculus, the front-door criterion, and the back-door criterion. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Gentle introduction to statistical modeling. It covers a wide range of topics, including linear regression, logistic regression, and Bayesian modeling. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/0gookp/analytics
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