Introduction to Python for Econometrics, Statistics and Numerical Analysis: Second Edition

These notes are designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research using Python.

**Tag(s):**
Python
Statistics

**Publication date**: 05 Aug 2014

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
405 pages

**Views**: 4,446

**Type**: Lecture Notes

**Publisher**:
n/a

**License**:
n/a

**Post time**: 29 Oct 2016 08:00:00

Introduction to Python for Econometrics, Statistics and Numerical Analysis: Second Edition

These notes are designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research using Python.

From the Introduction:

More Resources:

Kevin Sheppard wrote:These notes are designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research using Python. They should also be useful for students, researchers or practitioners who require a versatile platform for econometrics, statistics or general numerical analysis (e.g. numeric solutions to economic models or model simulation).

Python is a popular general purpose programming language which is well suited to a wide range of problems. Recent developments have extended Python’s range of applicability to econometrics, statistics and general numerical analysis. Python – with the right set of add-ons – is comparable to domain-specific languages such as R, MATLAB or Julia.

More Resources:

- The book webpage at kevinsheppard.com

Tweet

About The Author(s)

Professor Kevin Sheppard is Tutorial Fellow in Economics and Associate Professor in Financial Economics at Keble College, Oxford. His research focuses on issues, both theoretical and empirical, in financial econometrics. Specifically, He is interested in volatility and dependance modeling, market microstructure, and portfolio allocation.

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