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