Deitel, P: Intro to Python for Computer Science and Data Sci
Autor Paul Deitelen Limba Engleză Paperback – 15 feb 2021
For introductory-level Python programming and/ordata-science courses.
A groundbreaking, flexible approach to computer science anddata science
The Deitels Introduction to Python for ComputerScience and Data Science: Learning to Program with AI, Big Data and the Cloudoffers a unique approach to teaching introductory Python programming,appropriate for both computer-science and data-science audiences. Providing themost current coverage of topics and applications, the book is paired withextensive traditional supplements as well as Jupyter Notebooks supplements.Real-world datasets and artificial-intelligence technologies allow students towork on projects making a difference in business, industry, government andacademia. Hundreds of examples, exercises, projects (EEPs) and implementationcase studies give students an engaging, challenging and entertainingintroduction to Python programming and hands-on data science.
The book's modular architecture enables instructors toconveniently adapt the text to a wide range of computer-science anddata-science courses offered to audiences drawn from many majors.Computer-science instructors can integrate as much or as little data-scienceand artificial-intelligence topics as they'd like, and data-science instructorscan integrate as much or as little Python as they'd like. The book aligns withthe latest ACM/IEEE CS-and-related computing curriculum initiatives and withthe Data Science Undergraduate Curriculum Proposal sponsored by the NationalScience Foundation.
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Specificații
ISBN-13: 9781292364902
ISBN-10: 1292364904
Pagini: 880
Dimensiuni: 177 x 232 x 33 mm
Greutate: 1.28 kg
Editura: Pearson Education
ISBN-10: 1292364904
Pagini: 880
Dimensiuni: 177 x 232 x 33 mm
Greutate: 1.28 kg
Editura: Pearson Education
Cuprins
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 PandasDataFrames
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 inthe Exercises
DS 13. Data Mining Twitter�: Sentiment Analysis, JSON andWeb Services
DS 14. IBM Watson� and Cognitive Computing
DS 15. Machine Learning: Classification, Regression andClustering
DS 16. Deep Learning Convolutional and Recurrent NeuralNetworks; Reinforcement Learning in the Exercises
DS 17. Big Data: Hadoop�, Spark, NoSQL and IoT
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 PandasDataFrames
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 inthe Exercises
DS 13. Data Mining Twitter�: Sentiment Analysis, JSON andWeb Services
DS 14. IBM Watson� and Cognitive Computing
DS 15. Machine Learning: Classification, Regression andClustering
DS 16. Deep Learning Convolutional and Recurrent NeuralNetworks; Reinforcement Learning in the Exercises
DS 17. Big Data: Hadoop�, Spark, NoSQL and IoT