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Solution Manual for Intro to Python for Computer Science and Data Science: Learning to Program with AI Big Data and The Cloud Deitel

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Solution Manual for Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud By Paul J. Deitel, Harvey M. Deitel, ISBN-13: 9780135404669

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Solution Manual for Intro to Python for Computer Science and Data Science: Learning to Program with AI Big Data and The Cloud Deitel

Solution Manual for Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud By Paul J. Deitel, Harvey M. Deitel, ISBN-13: 9780135404669

Table of Contents

PART 1

CS: Python Fundamentals Quickstart

CS 1. Introduction to Computers and Python

DS Intro: AI–at the Intersection of CS and DS

CS 2. Introduction to Python Programming

DS Intro: Basic Descriptive Stats

CS 3. Control Statements and Program Development

DS Intro: Measures of Central Tendency—Mean, Median, Mode

CS 4. Functions

DS Intro: Basic Statistics— Measures of Dispersion

CS 5. Lists and Tuples

DS Intro: Simulation and Static Visualization

PART 2

CS: Python Data Structures, Strings and Files

CS 6. Dictionaries and Sets

DS Intro: Simulation and Dynamic Visualization

CS 7. Array-Oriented Programming with NumPy, High-Performance NumPy Arrays

DS Intro: Pandas Series and DataFrames

CS 8. Strings: A Deeper Look Includes Regular Expressions

DS Intro: Pandas, Regular Expressions and Data Wrangling

CS 9. Files and Exceptions

DS Intro: Loading Datasets from CSV Files into Pandas DataFrames

PART 3

CS: Python High-End Topics

CS 10. Object-Oriented Programming

DS Intro: Time Series and Simple Linear Regression

CS 11. Computer Science Thinking: Recursion, Searching, Sorting and Big O

CS and DS Other Topics Blog

PART 4

AI, Big Data and Cloud Case StudiesDS 12. Natural Language Processing (NLP), Web Scraping in the Exercises

DS 13. Data Mining Twitter®: Sentiment Analysis, JSON and Web Services

DS 14. IBM Watson® and Cognitive Computing

DS 15. Machine Learning: Classification, Regression and Clustering

DS 16. Deep Learning Convolutional and Recurrent Neural Networks; Reinforcement Learning in the Exercises

DS 17. Big Data: Hadoop®, Spark™, NoSQL and IoT