000			03396nam a22002057a 4500
008			250626b |||||||| |||| 00| 0 eng d
020			_a9789353949518
082			_a005.133 _bDEI
100			_aDeitel, Paul _93144
245			_aIntro to python for computer science and data science: learning to program with AI, Big Data and the Cloud
260			_aNoida _bPearson India Education Services Pvt. Ltd. _c2025
300			_axlviii, 831p. _bpbk.
520			_a"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. Table of Content 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 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®, Spark™, NoSQL and IoT"" Salient Features • Prepares students for future careers with the most current and relevant real-world applications • hands-on, real-world case studies • artificial-intelligence technologies • Helps instructors adapt to a range of computer-science and data-science courses with the flexible modular architecture"
650			_aComputer Science
650			_aData Science _95167
650			_aPython
700			_aDeitel, Harvey _93146
856			_uhttps://www.pearsoned.co.in/web/books/9789353949518_Intro-to-Python-for-Computer-Science-and-Data-Science_P-J-Deitel.aspx
942			_cBK
999			_c11153 _d11153