Introduction to Computation and Programming Using Python With Application to Understanding Data 2nd Edition by John Guttag ISBN 0262529629 9780262529624

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Product details:
ISBN 10: 0262529629
ISBN 13: 9780262529624
Author: John Guttag

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 PyLab. 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. The book is based on an MIT course (which became the most popular course offered through MIT’s OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.

Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.

Introduction to Computation and Programming Using Python With Application to Understanding Data 2nd Edition Table of contents:

Chapter 1: Introduction to Computation

• What is Computation?
• The Role of Algorithms and Data Structures
• Computational Thinking
• Using Python for Simple Computations
• Problem Solving with Python

Chapter 2: The Python Programming Language

• Introduction to Python
• Basic Syntax and Operations
• Variables and Data Types
• Writing Python Programs
• Control Flow: Conditionals and Loops
• Functions and Modularity in Python

Chapter 3: Simple Data Structures

• Introduction to Lists and Tuples
• Accessing and Modifying Elements
• Iterating Over Data Structures
• Stacks, Queues, and Deques
• Introduction to Dictionaries and Sets
• Applications of Data Structures in Problem Solving

Chapter 4: Functions and Recursion

• Understanding Functions in Python
• Function Definitions and Arguments
• Recursion and Recursive Functions
• Base Cases and Recursive Decomposition
• Applications of Recursion in Problem Solving

Chapter 5: Object-Oriented Programming

• Introduction to Object-Oriented Programming (OOP)
• Classes and Objects in Python
• Methods and Attributes
• Inheritance and Polymorphism
• Designing Programs Using OOP
• Case Studies of OOP Applications

Chapter 6: Computational Thinking and Problem Solving

• Defining Problems and Decomposition
• Algorithm Design and Analysis
• Using Python to Model Problems
• Steps for Problem Solving in Programming
• Analyzing Problem Complexity
• Efficiency Considerations in Algorithms

Chapter 7: Data Analysis and Visualization

• Introduction to Data Science
• Data Representation and Manipulation with Python
• Using Libraries like NumPy and Matplotlib
• Plotting and Visualizing Data
• Data Cleaning and Preparation for Analysis
• Interpreting Data and Drawing Conclusions

Chapter 8: Understanding Data Through Simulation

• The Role of Simulation in Understanding Data
• Monte Carlo Methods and Random Processes
• Generating Random Variables in Python
• Simulating Probabilistic Events
• Applications of Simulation in Problem Solving

Chapter 9: Analyzing Large Datasets

• Working with Large Data Sets in Python
• Efficient Data Processing Techniques
• Introduction to Pandas for Data Manipulation
• Handling Missing or Incomplete Data
• Analyzing Trends and Patterns in Large Datasets

Chapter 10: Algorithmic Complexity

• Understanding Time and Space Complexity
• Big O Notation and Performance Analysis
• Common Algorithms and Their Complexity
• Optimizing Algorithms for Efficiency
• Practical Examples of Algorithmic Design

Chapter 11: Data and Computation in the Real World

• The Role of Data in Modern Computing
• Case Studies in Data-Driven Decision Making
• Real-World Applications of Computational Models
• Ethical Considerations in Data Analysis and Computing

Chapter 12: Putting It All Together

• Integrating Computation, Programming, and Data Analysis
• Final Project: Analyzing Real-World Data
• Tools and Resources for Advanced Data Analysis
• Preparing for Further Study in Computer Science and Data Science

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