Advanced Data Analysis from an Elementary Point of View

A textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression.

**Tag(s):**
Probability
Statistics

**Publication date**: 18 Apr 2016

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
856 pages

**Views**: 5,236

Advanced Data Analysis from an Elementary Point of View

A textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression.

From the Introduction:

This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes for 36-402 at Carnegie Mellon University.

This book began as the notes for 36-402, Advanced Data Analysis, at Carnegie Mellon University. This is the methodological capstone of the core statistics sequence taken by our undergraduate majors (usually in their third year), and by undergraduate students from a range of other departments. By this point, students have taken classes in introductory statistics and data analysis, probability theory, mathematical statistics, and modern linear regression. This book does not presume that you once learned but have forgotten the material from the pre-requisites; it presumes that you know that material and can go beyond it. The book also presumes a firm grasp on linear algebra and multivariable calculus, and that you can read and write simple functions in R. If you are lacking in any of these areas, now would be an excellent time to leave.

Tweet

About The Author(s)

Cosma Shalizi is an Associate Professor in the Statistics Department at Carnegie Mellon University. Shalizi is co-author of the CSSR algorithm, which exploits entropy properties to efficiently extract Markov Models from time-series data without assuming a parametric form for the model.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors