HOME > Detail View

Detail View

Introduction to computation and programming using Python : with application to understanding data / 2nd ed

Introduction to computation and programming using Python : with application to understanding data / 2nd ed (Loan 4 times)

Material type
단행본
Personal Author
Guttag, John.
Title Statement
Introduction to computation and programming using Python : with application to understanding data / John V. Guttag.
판사항
2nd ed.
Publication, Distribution, etc
Cambridge, Massachusetts :   The MIT Press,   c2016.  
Physical Medium
xv, 447 p. : ill. ; 23 cm.
ISBN
9780262529624 (pbk. : alk. paper)
General Note
Includes index.  
Subject Added Entry-Topical Term
Python (Computer program language) --Textbooks. Computer programming --Textbooks.
000 00000cam u2200205 a 4500
001 000045965710
005 20181227153556
008 181224s2016 maua 001 0 eng d
010 ▼a 2016019367
020 ▼a 9780262529624 (pbk. : alk. paper)
035 ▼a (KERIS)REF000018054295
040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009
043 ▼a n-us-ma
050 0 0 ▼a QA76.73.P98 ▼b G88 2016
082 0 0 ▼a 005.13/3 ▼2 23
084 ▼a 005.133 ▼2 DDCK
090 ▼a 005.133 ▼b G985i2
100 1 ▼a Guttag, John.
245 1 0 ▼a Introduction to computation and programming using Python : ▼b with application to understanding data / ▼c John V. Guttag.
250 ▼a 2nd ed.
260 ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c c2016.
300 ▼a xv, 447 p. : ▼b ill. ; ▼c 23 cm.
500 ▼a Includes index.
650 0 ▼a Python (Computer program language) ▼v Textbooks.
650 0 ▼a Computer programming ▼v Textbooks.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 005.133 G985i2 Accession No. 111801252 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Machine generated contents note: 2.1. Basic Elements of Python
2.1.1. Objects, Expressions, and Numerical Types
2.1.2. Variables and Assignment
2.1.3. Python IDE''s
2.2. Branching Programs
2.3. Strings and Input
2.3.1. Input
2.3.2. Digression About Character Encoding
2.4. Iteration
3.1. Exhaustive Enumeration
3.2. For Loops
3.3. Approximate Solutions and Bisection Search
3.4. Few Words About Using Floats
3.5. Newton-Raphson
4.1. Functions and Scoping
4.1.1. Function Definitions
4.1.2. Keyword Arguments and Default Values
4.1.3. Scoping
4.2. Specifications
4.3. Recursion
4.3.1. Fibonacci Numbers
4.3.2. Palindromes
4.4. Global Variables
4.5. Modules
4.6. Files
5.1. Tuples
5.1.1. Sequences and Multiple Assignment
5.2. Ranges
5.3. Lists and Mutability
5.3.1. Cloning
5.3.2. List Comprehension
5.4. Functions as Objects
5.5. Strings, Tuples, Ranges, and Lists
5.6. Dictionaries
6.1. Testing
6.1.1. Black-Box Testing
6.1.2. Glass-box Testing
6.1.3. Conducting Tests
6.2. Debugging
6.2.1. Learning to Debug
6.2.2. Designing the Experiment
6.2.3. When the Going Gets Tough
6.2.4. When You Have Found "The" Bug
7.1. Handling Exceptions
7.2. Exceptions as a Control Flow Mechanism
7.3. Assertions
8.1. Abstract Data Types and Classes
8.1.1. Designing Programs Using Abstract Data Types
8.1.2. Using Classes to Keep Track of Students and Faculty
8.2. Inheritance
8.2.1. Multiple Levels of Inheritance
8.2.2. Substitution Principle
8.3. Encapsulation and Information Hiding
8.3.1. Generators
8.4. Mortgages, an Extended Example
9.1. Thinking About Computational Complexity
9.2. Asymptotic Notation
9.3. Some Important Complexity Classes
9.3.1. Constant Complexity
9.3.2. Logarithmic Complexity
9.3.3. Linear Complexity
9.3.4. Log-Linear Complexity
9.3.5. Polynomial Complexity
9.3.6. Exponential Complexity
9.3.7. Comparisons of Complexity Classes
10.1. Search Algorithms
10.1.1. Linear Search and Using Indirection to Access Elements
10.1.2. Binary Search and Exploiting Assumptions
10.2. Sorting Algorithms
10.2.1. Merge Sort
10.2.2. Exploiting Functions as Parameters
10.2.3. Sorting in Python
10.3. Hash Tables
11.1. Plotting Using PyLab
11.2. Plotting Mortgages, an Extended Example
12.1. Knapsack Problems
12.1.1. Greedy Algorithms
12.1.2. Optimal Solution to the 0/1 Knapsack Problem
12.2. Graph Optimization Problems
12.2.1. Some Classic Graph-Theoretic Problems
12.2.2. Shortest Path: Depth-First Search and Breadth-First Search
13.1. Fibonacci Sequences, Revisited
13.2. Dynamic Programming and the 0/1 Knapsack Problem
13.3. Dynamic Programming and Divide-and-Conquer
14.1. Random Walks
14.2. Drunkard''s Walk
14.3. Biased Random Walks
14.4. Treacherous Fields
15.1. Stochastic Programs
15.2. Calculating Simple Probabilities
15.3. Inferential Statistics
15.4. Distributions
15.4.1. Probability Distributions
15.4.2. Normal Distributions
15.4.3. Continuous and Discrete Uniform Distributions
15.4.4. Binomial and Multinomial Distributions
15.4.5. Exponential and Geometric Distributions
15.4.6. Benford''s Distribution
15.5. Hashing and Collisions
15.6. How Often Does the Better Team Win?
16.1. Pascal''s Problem
16.2. Pass or Don''t Pass?
16.3. Using Table Lookup to Improve Performance
16.4. Finding pi
16.5. Some Closing Remarks About Simulation Models
17.1. Sampling the Boston Marathon
17.2. Central Limit Theorem
17.3. Standard Error of the Mean
18.1. Behavior of Springs
18.1.1. Using Linear Regression to Find a Fit
18.2. Behavior of Projectiles
18.2.1. Coefficient of Determination
18.2.2. Using a Computational Model
18.3. Fitting Exponentially Distributed Data
18.4. When Theory Is Missing
19.1. Checking Significance
19.2. Beware of P-values
19.3. One-tail and One-sample Tests
19.4. Significant or Not?
19.5. Which N?
19.6. Multiple Hypotheses
20.1. Conditional Probabilities
20.2. Bayes'' Theorem
20.3. Bayesian Updating
21.1. Garbage In Garbage Out (GIGO)
21.2. Tests Are Imperfect
21.3. Pictures Can Be Deceiving
21.4. Cum Hoc Ergo Propter Hoc
21.5. Statistical Measures Don''t Tell the Whole Story
21.6. Sampling Bias
21.7. Context Matters
21.8. Beware of Extrapolation
21.9. Texas Sharpshooter Fallacy
21.10. Percentages Can Confuse
21.11. Statistically Significant Differences Can Be Insignificant
21.12. Regressive Fallacy
21.13. Just Beware
22.1. Feature Vectors
22.2. Distance Metrics
23.1. Class Cluster
23.2. K-means Clustering
23.3. Contrived Example
23.4. Less Contrived Example
24.1. Evaluating Classifiers
24.2. Predicting the Gender of Runners
24.3. K-nearest Neighbors
24.4. Regression-based Classifiers
24.5. Surviving the Titanic
24.6. Wrapping Up.

New Arrivals Books in Related Fields