Intro to Python for Computer Science and Data Science
Learning to Program with AI, Big Data and The Cloud, Global Edition
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
Inhoudsopgave
-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 PandasDataFrames
PART 3
-CS: Python High-End Topics
-CS 10. Object-Oriented Programming
-DS Intro: Time Series and Simple Linear Regression
-DS Intro: Time Series and Simple Linear Regression
-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®, Spark™, NoSQL and IoT
Anderen die dit boek kochten, kochten ook
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Paul Deitel€ 92,14 -
Harvey Deitel€ 92,14
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Dieter Möckelmann€ 16,95
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Jan van Bon€ 21,26
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Job van den Berg€ 34,95
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Willem Leijnse€ 72,95
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