PART 1 CS: Python Fundamentals Quickstart PART 2 CS: Python Data Structures, Strings and Files Introduction to Computers and Python DS Intro: Simulation and Dynamic Visualization Control Statements and Program DevelopmentĭS Intro: AI-at the Intersection of CS and DSĬS 1. Array-Oriented Programming with NumPy High-Performance NumPy ArraysĭS Intro: Time Series and Simple Linear RegressionĭS Intro: Basic Statistics- Measures of DispersionĭS Intro: Measures of Central Tendency-Mean, Median, ModeĬS 3. Strings: A Deeper Look Includes Regular ExpressionsĬS 7. PART 4 AI, Big Data and Cloud Case StudiesĭS Intro: Pandas, Regular Expressions and Data WranglingĬS 8. Natural Language Processing (NLP) Web Scraping in the Exercises Data Mining Twitter® Sentiment Analysis, JSON and Web ServicesĭS 12. Computer Science Thinking: Recursion, Searching, Sorting and Big OĭS 13. Preface explains the dependencies among the chapters. IBM Watson® and Cognitive ComputingĭS Intro: Simulation and Static Visualizationĥ. Machine Learning: Classification, Regression and ClusteringĭS 14. Deep Learning Convolutional and Recurrent Neural Networks Reinforcement Learning in the ExercisesĭS 15. Light-tinted bottom boxes in Chapters 1–10 marked DS Intro are brief, friendly introductions to data-science topics.ĭS 16. Chapters 1–11 marked CS are traditional Python programming and computer-science topics. Functional-style programming is integrated book wide.ġ. Chapters 12–17 marked DS are Python-based, AI, big data and cloud chapters, each containing several full-implementation studies. Questions? ĭS Intro: Loading Datasets from CSV Files into Pandas DataFramesģ. CS courses may cover more of the Python chapters and less of the DS content. Big Data: Hadoop®, Spark™, NoSQL and IoTħ.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |