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.…mehr
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.
About our authors Paul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 43 years in computing. He is one of the world's most experienced programming-languages trainers, having taught professional courses to software developers since 1992. He has delivered hundreds of programming courses to academic, industry, government and military clients of Deitel & Associates, Inc. internationally, including UCLA, SLB (formerly Schlumberger), Cisco, IBM, Siemens, Sun Microsystems (now Oracle), Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, Puma, iRobot and many more. Dr. Harvey M. Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has 62 years of experience in computing. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University; he studied computing in each of these programs before they spun off Computer Science departments. He has extensive college and professional teaching experience, including earning tenure and serving as the Chairman of the Computer Science Department at Boston College before founding Deitel & Associates in 1991 with his son, Paul. The Deitels' publications have earned international recognition, with more than 100 translations published in Japanese, German, Russian, Spanish, French, Polish, Italian, Simplified Chinese, Traditional Chinese, Korean, Portuguese, Greek, Urdu and Turkish. Dr. Deitel has delivered hundreds of programming courses to academic, corporate, government and military clients.
Inhaltsangabe
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 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®, SparkTM, NoSQL and IoT
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 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®, SparkTM, NoSQL and IoT
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