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- 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 StatisticsMeasures of Dispersion
- CS 5. Lists and Tuples
- DS Intro: Simulation and Static Visualization
- 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
- 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
- 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
- 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 StatisticsMeasures of Dispersion
- CS 5. Lists and Tuples
- DS Intro: Simulation and Static Visualization
- 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
- 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
- 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
| Erscheinungsjahr: | 2021 |
|---|---|
| Medium: | Taschenbuch |
| Titel: | Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, Global Edition |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781292364902 |
| ISBN-10: | 1292364904 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Deitel, Paul |
| Hersteller: | Pearson Education Limited |
| Verantwortliche Person für die EU: | preigu GmbH & Co. KG, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de |
| Maße: | 232 x 177 x 33 mm |
| Von/Mit: | Paul Deitel |
| Erscheinungsdatum: | 15.02.2021 |
| Gewicht: | 1,284 kg |
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