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

About

Features

Hallmark features of this title

Current real-world applications

• Hundreds of examples, exercises and projects (EEPs) offer a hands-on introduction to Python and data science.
• AI, big data and the Cloud are explored in 6 fully implemented data science case studies.
• Jupyter Notebooks supplements give students practice working in a live coding environment.
• Self-Check exercises with answers let students test their knowledge using short-answer questions and interactive iPython coding sessions.

Unique modular organization of computer and data science topics

• Content is divided into groups of related chapters. Python content and optional intros to data science are presented early. Later chapters dive deeper into data science.
• A chapter dependency chart helps instructors easily plan their syllabi.

Description

Introduction to Python for Computer Science and Data Science takes a unique, modular approach to teaching and learning introductory Python programming that is relevant for both computer science and data science audiences. The Deitels cover the most current topics and applications to prepare you for your career. Jupyter Notebooks supplements provide opportunities to test your programming skills. Fully implemented case studies in artificial intelligence technologies and big data let you apply your knowledge to interesting projects in the business, industry, government and academia sectors. Hundreds of hands-on examples, exercises and projects offer a challenging and entertaining introduction to Python and data science.

Sample Content

Table of Contents

PART 1

• CS: Python Fundamentals Quickstart
• CS 1. Introduction to Computers and Python
• DS Intro: AIat 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 TendencyMean, 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 Studies

• DS 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®, SparkTM, NoSQL and IoT

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