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Intro to Python for ­Computer Science and Data ­Science
Learning to Program with AI, Big Data and The Cloud, Global Edition

Rating
Format
Paperback, 880 pages
Published
United Kingdom, 25 July 2021


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 Twitterï¿¿: Sentiment Analysis, JSON andWeb Services



DS 14. IBM Watsonï¿¿ 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: Hadoopï¿¿, Spark, NoSQL and IoT


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Product Description


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 Twitterï¿¿: Sentiment Analysis, JSON andWeb Services



DS 14. IBM Watsonï¿¿ 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: Hadoopï¿¿, Spark, NoSQL and IoT


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Product Details
EAN
9781292364902
ISBN
1292364904
Dimensions
3.5 x 23.1 x 23.1 centimeters (1.28 kg)

Table of Contents

  • 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
  • DS Intro: Time Series and Simple Linear Regression
  • 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

About the Author

Paul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 38 years of computing and corporate training experience and is an Oracle Java Champion and a Microsoft C# MVP (2012-2014). He is a best-selling programming-language textbook/professional book/video/e-learning author. Paul is one of the world's most experienced programming-languages trainers. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including 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, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. 

Dr. Harvey M. Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 55 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 just before they spun off Computer Science programs. He has extensive college teaching experience, including earning tenure and serving as the Chairman of the Computer Science Department at Boston College before founding Deitel & Associates, Inc., in 1991 with his son, Paul. The Deitels' publications have earned international recognition, with more than 100 translations published. Dr. Deitel has delivered hundreds of programming courses to academic, corporate, government and military clients.

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