Introduction to computation and programming using Python : with application to understanding data / John V. Guttag.
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TextPublisher: New Delhi : PHI Learning, 2016Edition: 2nd edDescription: xvii, 447 pages : 23 cm. illISBN: 9788120352926 (pbk)Subject(s): Python (Computer program language) -- | Computer programming -- TextbooksDDC classification: 005.133 | Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds |
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| 005.133 GOD-D 2012 3604 Data structures and algorithms in java / | 005.133 GOO-D 2016 10142 Data structures and algorithms in Python / | 005.133 GUT-I 2016 10189 Introduction to computation and programming using Python : with application to understanding data / | 005.133 GUT-I 2016 10190 Introduction to computation and programming using Python : with application to understanding data / | 005.133 GUT-I 2016 10192 Introduction to computation and programming using Python : with application to understanding data / | 005.133 GUT-I 2016 10193 Introduction to computation and programming using Python : with application to understanding data / | 005.133 GUT-I 2016 9913 Introduction to computation and programming using Python : with application to understanding data / |
Includes index.
1- Getting Started
2- Introduction to Python
3- Some Simple Numerical Problems
4- Functions, Scoping & Abstraction
5- Structured Types, Mutability & Higher-Order Functions
6- Testing & Debugging
7- Exceptions & Assertions
8- Classes & Object-Oriented Programming
9- A Simplistic Introduction to Algorithmic Complexity
10- Some Simple Algorithms & Data Structures
11- Plotting & More About Classes
12- Knapsack & Graph Optimization Problems
13- Dynamic Programming
14- Random Walks & More About Data Visualization
15- Stochastic Programs, Probability & Distributions
16- Monte Carlo Simulation
17- Sampling & Confidence Intervals
18- Understanding Experimental Data
19- Randomized Trials & Hypothesis Checking
20- Conditional Probability & Bayesian Statistics
21- Lies, Damned lies & Statistics
22- A Quick Look at Machine Learning
23- Clustering
24- lassification Methods
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