Introduction to computation and programming using Python : with application to computational modeling and understanding data /
"The new edition of an introduction to the art of computational problem solving using Python. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotlib, random,...
| Main Authors: | , |
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| Corporate Author: | |
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Massachusetts :
The MIT Press,
[2021]
Cambridge, Massachusetts ; London, England The MIT Press [2021] |
| Edition: | Third edition |
| Subjects: |
Table of Contents:
- Getting started
- Introduction to Python
- Some simple numerical programs
- Functions, scoping, and abstraction
- Structured types and mutability
- Recursion and global variables
- Modules and files
- Testing and debugging
- Exceptions and assertions
- Classes and object-oriented programming
- A simplistic introduction to algorithmic complexity
- Some simple algorithms and data structures
- Plotting and more about classes
- Knapsack and graph optimization problems
- Dynamic programming
- Random walks and more about data visualization
- Stochastic programs, probability, and distributions
- Monte Carlo simulation
- Sampling and confidence
- Understanding experimental data
- Randomized trials and hypothesis checking
- Lies, damned lies, and statistics
- Exploring data with Pandas
- A quick look at machine learning
- Clustering
- Classification methods
- Getting started
- Introduction to Python
- Some simple numerical programs
- Functions, scoping, and abstraction
- Structured types and mutability
- Recursion and global variables
- Modules and files
- Testing and debugging
- Exceptions and assertions
- Classes and object-oriented programming
- A simplistic introduction to algorithmic complexity
- Some simple algorithms and data structures
- Plotting and more about classes
- Knapsack and graph optimization problems
- Dynamic programming
- Random walks and more about data visualization
- Stochastic programs, probability, and distributions
- Monte Carlo simulation
- Sampling and confidence
- Understanding experimental data
- Randomized trials and hypothesis checking
- Lies, damned lies, and statistics
- Exploring data with Pandas
- A quick look at machine learning
- Clustering
- Classification methods