Introduction to Computation and Programming Using Python, third edition: With Application to Computational...

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John V. Guttag

Introduction to Computation and Programming Using Python, third edition

With Application to Computational Modeling

John V. Guttag

$170

Paperback

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English

MIT Press
16 June 2021

Computing: general

The new edition of an introduction to the art of computational problem solving using Python.

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.

All of the code in the book and an errata sheet are available on the book's web page on the MIT Press website.

By: John V. Guttag
Imprint: MIT Press
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm,
ISBN: 9780262542364
ISBN 10: 0262542366
Pages: 496
Publication Date: 16 June 2021
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

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

John V. Guttag is Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT.