Intro to Python for computer science and data science
Intro to Python for computer science and data science
- Pearson, 2022
- 879 pages : illustrations (chiefly color) ; 24 cm
learning to program with AI, big data and the cloud
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 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 (R): Sentiment Analysis, JSON andWeb Services DS 14. IBM Watson (R) and Cognitive Computing DS 15. Machine Learning: Classification, Regression and Clustering DS 16. Deep Learning Convolutional and Recurrent NeuralNetworks; Reinforcement Learning in the Exercises DS 17. Big Data: Hadoop (R), Spark (TM), NoSQL and IoT.
For introductory-level Python programming and/or data-science courses. A groundbreaking, 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.
9781292364902
QA MATHEMATICS
Python (Computer program language)
Computer science
Data Science
Cognitive Computing
Artificial Intelligence
Big Data
Cloud
Machine Learning
learning to program with AI, big data and the cloud
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 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 (R): Sentiment Analysis, JSON andWeb Services DS 14. IBM Watson (R) and Cognitive Computing DS 15. Machine Learning: Classification, Regression and Clustering DS 16. Deep Learning Convolutional and Recurrent NeuralNetworks; Reinforcement Learning in the Exercises DS 17. Big Data: Hadoop (R), Spark (TM), NoSQL and IoT.
For introductory-level Python programming and/or data-science courses. A groundbreaking, 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.
9781292364902
QA MATHEMATICS
Python (Computer program language)
Computer science
Data Science
Cognitive Computing
Artificial Intelligence
Big Data
Cloud
Machine Learning
