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    Essential Statistics for Data Science

Essential Statistics for Data Science

Mu Zhu
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      Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part text introduces readers to the basics of probability and random variables and guides them towards relatively advanced topics in both frequentist and Bayesian in a matter of weeks. Part I, Talking Probability explains the statistical approach to analysing data with a probability model to describe the data generating process. Part II, Doing Statistics demonstrates how the unknown quantities in data i.e. it's parameters is applicable in statistical interference. Part III, Facing Uncertainty explains the importance of explicity describing how much uncertainty is caused by parameters with intrinsic scientific meaning and how to take that intoaccount when making decisions. Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more focused than a typical undergraduate text, but still lighter and more accessible than an average graduate text.
      Format: CONTRIBUTORS: Mu Zhu EAN: 9780192867742 COUNTRY: United Kingdom PAGES: WEIGHT: 296 g HEIGHT: 235 cm
      PUBLISHED BY: Oxford University Press DATE PUBLISHED: 2023-04-04 CITY: GENRE: LAW / Criminal Law / General, MATHEMATICS / Probability & Statistics / General WIDTH: 157 cm SPINE:

      Book Themes:

      Mathematics, Probability and statistics, Bayesian inference

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      Mu Zhu is Professor in the Department of Statistics & Actuarial Science at the University of Waterloo, and Fellow of the American Statistical Association. He received his AB magna cum laude in applied mathematics from Harvard University, and his PhD in statistics from Stanford University. He is currently Director of the Graduate Data Science Program at Waterloo.
      Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part text introduces readers to the basics of probability and random variables and guides them towards relatively advanced topics in both frequentist and Bayesian in a matter of weeks. Part I, Talking Probability explains the statistical approach to analysing data with a probability model to describe the data generating process. Part II, Doing Statistics demonstrates how the unknown quantities in data i.e. it's parameters is applicable in statistical interference. Part III, Facing Uncertainty explains the importance of explicity describing how much uncertainty is caused by parameters with intrinsic scientific meaning and how to take that intoaccount when making decisions. Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more focused than a typical undergraduate text, but still lighter and more accessible than an average graduate text.
      Format: CONTRIBUTORS: Mu Zhu EAN: 9780192867742 COUNTRY: United Kingdom PAGES: WEIGHT: 296 g HEIGHT: 235 cm
      PUBLISHED BY: Oxford University Press DATE PUBLISHED: 2023-04-04 CITY: GENRE: LAW / Criminal Law / General, MATHEMATICS / Probability & Statistics / General WIDTH: 157 cm SPINE:

      Book Themes:

      Mathematics, Probability and statistics, Bayesian inference

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      Mu Zhu is Professor in the Department of Statistics & Actuarial Science at the University of Waterloo, and Fellow of the American Statistical Association. He received his AB magna cum laude in applied mathematics from Harvard University, and his PhD in statistics from Stanford University. He is currently Director of the Graduate Data Science Program at Waterloo.

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