Introduction to Computation and Programming Using Python, third edition
With Application to Computational Modeling
Published on 5. January 2021
Book
Paperback/Softback
496 pages
978-0-262-54236-4 (ISBN)
Description
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, pandas, and sklearn. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data as well as substantial material on machine learning.
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, pandas, and sklearn. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data as well as substantial material on machine learning.
More details
Language
English
Place of publication
Cambridge (Massachusetts)
United States
Publishing group
MIT Press Ltd
Illustrations
140
Dimensions
Height: 226 mm
Width: 176 mm
Thickness: 31 mm
Weight
1044 gr
ISBN-13
978-0-262-54236-4 (9780262542364)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
John V. Guttag
Introduction to Computation and Programming Using Python, third edition
With Application to Computational Modeling and Understanding Data
E-Book
01/2021
MIT Press
€73.49
Available for download
Previous edition
John V. Guttag
Introduction to Computation and Programming Using Python
With Application to Understanding Data
Book
08/2016
2nd Edition
MIT Press
€49.52
Article exhausted; check for reprint
Person
John V. Guttag
Content
1 GETTING STARTED
2 INTRODUCTION TO PYTHON
3 SOME SIMPLE NUMERICAL PROGRAMS
4 FUNCTIONS, SCOPING, AND ABSTRACTION
5 STRUCTURED TYPES and MUTABILITY
6 Recursion and Global variables
7 Modules and Files
8 TESTING AND DEBUGGING
9 EXCEPTIONS AND ASSERTIONS .
10 CLASSES AND OBJECT-ORIENTED PROGRAMMING
11 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY
12 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES .
13 PLOTTING AND MORE ABOUT CLASSES
14 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS
15 DYNAMIC PROGRAMMING
16 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION
17 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS
18 MONTE CARLO SIMULATION
19 SAMPLING AND CONFIDENCE .
20 UNDERSTANDING EXPERIMENTAL DATA
21 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING .
22 LIES, DAMNED LIES, AND STATISTICS
23 EXPLORING DATA WITH PANDAS
24 A QUICK LOOK AT MACHINE LEARNING
25 CLUSTERING
26 CLASSIFICATION METHODS
PYTHON 3.8 QUICK REFERENCE
INDEX
2 INTRODUCTION TO PYTHON
3 SOME SIMPLE NUMERICAL PROGRAMS
4 FUNCTIONS, SCOPING, AND ABSTRACTION
5 STRUCTURED TYPES and MUTABILITY
6 Recursion and Global variables
7 Modules and Files
8 TESTING AND DEBUGGING
9 EXCEPTIONS AND ASSERTIONS .
10 CLASSES AND OBJECT-ORIENTED PROGRAMMING
11 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY
12 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES .
13 PLOTTING AND MORE ABOUT CLASSES
14 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS
15 DYNAMIC PROGRAMMING
16 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION
17 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS
18 MONTE CARLO SIMULATION
19 SAMPLING AND CONFIDENCE .
20 UNDERSTANDING EXPERIMENTAL DATA
21 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING .
22 LIES, DAMNED LIES, AND STATISTICS
23 EXPLORING DATA WITH PANDAS
24 A QUICK LOOK AT MACHINE LEARNING
25 CLUSTERING
26 CLASSIFICATION METHODS
PYTHON 3.8 QUICK REFERENCE
INDEX