Item specifics

Language: English ISBN:





About this product

Product Information
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.

Product Identifiers
Publisher Pearson Education
ISBN-10 0135404673
ISBN-13 9780135404676
eBay Product ID (ePID) 28038257079

Product Key Features
Format Trade Paperback
Publication Year 2019
Language English

Weight 42.7 Oz
Width 7in.
Height 1.4in.
Length 9.2in.

Additional Product Features
Dewey Edition 23
Table of Content To see a visual view of the unique Table of Contents, download the PDF. 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(tm), NoSQL and IoT
Dewey Decimal 005.13/3
Target Audience College Audience
Copyright Date 2020
Author Harvey Deitel, Paul Deitel
Number of Pages 880 Pages
Lc Classification Number Qa76.73.P98d45 2020
Reviews “Strikes a good balance between teaching computer science fundamentals and putting data science techniques into practice. Designed to help students not only learn programming fundamentals but also leverage the large number of existing libraries to start accomplishing tasks with minimal code. Concepts are accompanied by rich Python examples that students can adapt to implement their own solutions to data science problems. I like that cloud services are used.” –David Koop, Assistant Professor, U-Mass Dartmouth “Fun, engaging real-world examples and exercises will encourage students to conduct meaningful data analyses. This book provides many of the best explanations of data science concepts I”ve encountered. Introduces the most useful starter machine learning models–does a good job explaining how to choose the best model and what “the best” means. Great overview of all the big data technologies with relevant examples.” –Jamie Whitacre, Data Science Consultant “Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science. A great introduction to IBM Watson and the services it provides!” –Shyamal Mitra, Senior Lecturer, University of Texas “The best designed Intro to Data Science/Python book I have seen.” –Roland DePratti, Central Connecticut State University “You”ll develop applications using industry standard libraries and cloud computing services.” –Daniel Chen, Data Scientist, Lander Analytics “The book”s applied approach should engage students. The examples involving the top-down, stepwise refinement of programs illustrate how programs are really developed. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” –Garrett Dancik, Associate Professor of Computer Science/Bioinformatics, Eastern Connecticut State University “Wonderful for first-time Python learners from all educational backgrounds and majors. My business analytics students had little to no coding experience when they began the course. In addition to loving the material, it was easy for them to follow along with the example exercises and by the end of the course were able to mine and analyze Twitter data using techniques learned from the book. The chapters are clearly written with detailed explanations of the example code, which makes it easy for students without a computer science background to understand. The modular structure, wide range of contemporary data science topics, and companion Jupyter notebooks make this a fantastic resource for instructors and students of a variety of Data Science, Business Analytics, and Computer Science courses. The “Self Checks” are great for students. Fabulous Big Data chapter-it covers all of the relevant programs and platforms. Great Watson chapter! This is the type of material that I look for as someone who teaches Business Analytics. The chapter provided a great overview of the Watson applications. Also, your translation examples are great for students because they provide an “instant reward”-it”s very satisfying for students to implement a task and receive results so quickly. Machine Learning is a huge topic and this chapter serves as a great introduction. I loved the housing data example-very relevant for business analytics students. The chapter was visually stunning.” -Alison Sanchez, Assistant Professor in Economics, University of San Diego “I like the new combination of topics from computer science, data science, and stats. A compelling feature is the integration of content that is typically considered in separate courses. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I th
Lccn 2019-003447

Price : 95.95

Ends on : Ended

Buy on eBay!

Bags Sales