Statistical Inference for Data Science by Brian Caffo
Statistical Inference for Data Science by Brian Caffo - Table of Contents
1. Introduction
2. Probability
3. Conditional probability
4. Expected values
5. Variation
6. Some common distributions
7. Asymptopia
8. t Confidence intervals
9. Hypothesis testing
10. P-values
11. Power
12. The bootstrap and resampling
What You Will Learn in Statistical Inference for Data Science by Brian Caffo
"Statistical Inference for Data Science" by Brian Caffo is a practical introduction to the principles of statistical inference for modern data analysis. The book explains how analysts can use sample data to make reliable conclusions about larger populations. It is designed for students and beginners in data science who want to understand how statistical reasoning supports real-world decision making.
The book focuses on essential statistical ideas such as probability, variability, estimation, and testing hypotheses. It explains how techniques like "Statistical Inference", "Probability", and "Hypothesis Testing" help data scientists interpret data and measure uncertainty. Through clear explanations and simple examples, the author shows how statistical thinking is applied in everyday data analysis tasks.
Another important aspect of the book is its connection with modern data science tools and methods. It introduces concepts like "Confidence Intervals" and "Data Analysis" while encouraging readers to apply them using programming tools such as R. Overall, the book provides a strong conceptual foundation for learners who want to understand statistics and apply it effectively in the field of data science.
Book Details & Specifications
Title:
Statistical Inference for Data Science by Brian Caffo
Publisher:
Leanpub
Year:
2016
Pages:
124
Type:
PDF
Language:
English
ISBN-10 #:
B07GNKYXNV
ISBN-13 #:
978-1316946688
License:
CC BY-NC-SA 4.0
Amazon:
Amazon
About the Author: Brian Caffo
The author
Brian Caffo
is an American "biostatistician" and data science educator. He is a "Professor of Biostatistics" at the "Johns Hopkins Bloomberg School of Public Health". Caffo studied mathematics and statistics at the "University of Florida", where he earned his "PhD in Statistics". His academic career focuses on teaching modern statistical methods and applying them to complex scientific problems. His research interests include "statistical inference", "data science", "biostatistics", "machine learning", and "neuroimaging data analysis". Caffo is also known for helping develop the popular "Johns Hopkins Data Science program" that teaches practical analytics and computational statistics to learners worldwide.
Read or Downloadable Statistical Inference for Data Science by Brian Caffo
Free Regression Analysis Books PDF | Statistical Learning Resources