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

E-book Engels 2021 9781292364933
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Samenvatting

For introductory-level Python programming and/or data-science courses.

A ground-breaking, flexible approach to computer science and data science

The Deitels' Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs) and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science.

The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

Specificaties

ISBN13:9781292364933
Taal:Engels
Bindwijze:e-book

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Inhoudsopgave

<div class="c-section-headers-alternate-number-list_container"> <h3> PART 1 </h3> <ul> <li>CS: Python Fundamentals Quickstart</li> <li>CS 1. Introduction to Computers and Python</li> <li>DS Intro: AI–at the Intersection of CS and DS</li> <li>CS 2. Introduction to Python Programming</li> <li>DS Intro: Basic Descriptive Stats</li> <li>CS 3. Control Statements and Program Development</li> <li>DS Intro: Measures of Central Tendency—Mean, Median, Mode</li> <li>CS 4. Functions</li> <li>DS Intro: Basic Statistics—Measures of Dispersion</li> <li>CS 5. Lists and Tuples</li> <li>DS Intro: Simulation and Static Visualization</li> </ul> <h3> PART 2 </h3> <ul> <li>CS: Python Data Structures, Strings and Files</li> <li>CS 6. Dictionaries and Sets</li> <li>DS Intro: Simulation and Dynamic Visualization</li> <li>CS 7. Array-Oriented Programming with NumPy, High-Performance NumPy Arrays</li> <li>DS Intro: Pandas Series and DataFrames</li> <li>CS 8. Strings: A Deeper Look Includes Regular Expressions</li> <li>DS Intro: Pandas, Regular Expressions and Data Wrangling</li> <li>CS 9. Files and Exceptions</li> <li>DS Intro: Loading Datasets from CSV Files into PandasDataFrames </li> </ul> <h3> PART 3 </h3> <ul> <li>CS: Python High-End Topics</li> <li>CS 10. Object-Oriented Programming</li> <li>DS Intro: Time Series and Simple Linear Regression</li> <li>DS Intro: Time Series and Simple Linear Regression</li> <li>CS and DS Other Topics Blog</li> </ul> <h3> PART 4 </h3> <ul> <li>AI, Big Data and Cloud Case Studies</li> <li>DS 12. Natural Language Processing (NLP), Web Scraping in the Exercises</li> <li>DS 13. Data Mining Twitter®: Sentiment Analysis, JSON and Web Services</li> <li>DS 14. IBM Watson® and Cognitive Computing</li> <li>DS 15. Machine Learning: Classification, Regression and Clustering</li> <li>DS 16. Deep Learning Convolutional and Recurrent Neural Networks; Reinforcement Learning in the Exercises</li> <li>DS 17. Big Data: Hadoop®, Spark™, NoSQL and IoT</li> </ul> </div>

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