Essential Statistics for Data Science (Paperback)

Mu Zhu

ISBN 10: 0192867741 / ISBN 13: 9780192867742

Published by Oxford University Press, Oxford, 2023

About this Item

Paperback. 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 starts from the basics of probability and random variables and guides readers towards relatively advanced topics in both frequentist and Bayesian approaches in a matter of weeks. Part I, Talking Probabilityexplains that the statistical approach to analysing data starts with a probability model to describe the data generating process. Part II, Doing Statistics explains that much of statistical inference is about learningunknown quantities in the model (e.g. its parameters) from the data it is presumed to have generated. Part III, Facing Uncertainty explains the importance of explicitly describing how much uncertainty we have about the model parameters, especially those with intrinsic scientific meaning, and of taking that into account when making decisions. Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners,while being more serious than a typical undergraduate text, but still lighter and more accessible than an average graduate text. Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program in data science without knowing enough statistics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780192867742

Bibliographic Details

• Title: Essential Statistics for Data Science (Paperback)
• Publisher: Oxford University Press, Oxford
• Publication Date: 2023
• Language: English
• ISBN 10: 0192867741
• ISBN 13: 9780192867742
• Binding: Paperback
• Condition: new

About this title

Synopsis

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 starts from the basics of probability and random variables and guides readers towards relatively advanced topics in both frequentist and Bayesian approaches in a matter of weeks.

Part I, Talking Probability explains that the statistical approach to analysing data starts with a probability model to describe the data generating process. Part II, Doing Statistics explains that much of statistical inference is about learning unknown quantities in the model (e.g. its parameters) from the data it is presumed to have generated. Part III, Facing Uncertainty explains the importance of explicitly describing how much uncertainty we have about the model parameters, especially those with intrinsic scientific meaning, and of taking that into account when making decisions.

Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more serious than a typical undergraduate text, but still lighter and more accessible than an average graduate text.

About the Author

Mu Zhu, Professor, University of Waterloo

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.