Applied Data Science Using PySpark, Kakarla R., Krishnan S., Dhamodharan B., 2024

Applied Data Science Using PySpark, Kakarla R., Krishnan S., Dhamodharan B., 2024.
    
   This book is recommended for data science enthusiasts who want to unleash the power of parallel computing by working with big datasets. It’s also highly recommended for professionals who want to switch from traditional languages to open-source in a big data setting. It’s a value-add for students who want to work on big data as well.




Why Do We Need to Use Docker?
Imagine a scenario where you developed an application using Python 2.7. Suddenly, your college or company administration announces a switch to Python 3.11 or a later version. You are faced with a challenge to test your code’s compatibility with the new Python version and update your code when it errors out. Given the time required to ensure your application runs smoothly, this process can be frustrating and cumbersome.

In such situations, Docker proves to be incredibly useful. Once you create a Docker image, changes occurring in the external environment become irrelevant. Your application will function seamlessly wherever Docker is deployed. This feature makes Docker exceptionally powerful and a vital tool for data science professionals.

Contents.
About the Authors.
About the Technical Reviewer.
Acknowledgments.
Introduction.
Chapter 1: Setting Up the PySpark Environment.
Chapter 2: PySpark Basics.
Chapter 3: Utility Functions and Visualizations.
Chapter 4: Variable Selection.
Chapter 5: Supervised Learning Algorithms.
Chapter 6: Model Evaluation.
Chapter 7: Unsupervised Learning and Recommendation Algorithms.
Chapter 8: Machine Learning Flow and Automated Pipelines.
Chapter 9: Deploying Machine Learning Models.
Chapter 10: Experimentation and Optimization Strategies.
Chapter 11: Modeling Frameworks.
Index.



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Теги: учебник по программированию :: программирование :: Kakarla :: Krishnan :: Dhamodharan