Advanced Data Analytics Using Python: With Architectural Patterns, Text and Image Classification, and Optimization Techniques

This book covers advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark. It also covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.

Format: Paperback / softback
Length: 249 pages
Publication date: 26 November 2022
Publisher: APress

This comprehensive book delves into the realm of advanced data analytics, encompassing a wide range of concepts and techniques. It begins by introducing readers to essential topics such as time series analysis, principal component analysis (PCA), and ensemble modeling using tools like ETL (Extract, Transform, and Load), supervised learning, and PySpark.

The book then explores architectural patterns in data analytics, covering topics like data warehouses, data lakes, and cloud-based data processing. It discusses text and image classification, optimization techniques, natural language processing, and computer vision, all within the context of cloud environments.

Furthermore, the book delves into generic design patterns in Python programming, emphasizing architectural practices like hot potato anti-patterns. It provides a comprehensive review of recent advances in databases, including Neo4j, Elasticsearch, and MongoDB. The book also covers feature engineering in images and texts, emphasizing implementing business logic and building machine learning and deep learning models using transfer learning.

In addition to these core topics, the book includes a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics. It also explores reinforcement learning, a powerful technique for solving complex problems. Finally, the book provides an in-depth explanation of the recommender system in PySpark, demonstrating how to optimize models for specific applications.

Designed for data scientists and software developers with an interest in data analytics, this second edition of Advanced Analytics with Python offers a comprehensive and up-to-date guide to the field. It provides a solid foundation in data science and machine learning, enabling readers to build intelligent systems for enterprise and solve real-world problems effectively.

With its clear explanations, practical examples, and extensive coverage of advanced topics, this book is an invaluable resource for anyone looking to advance their skills in data analytics and stay ahead in the rapidly evolving field.

Weight: 412g
Dimension: 154 x 234 x 20 (mm)
ISBN-13: 9781484280041
Edition number: 2nd ed.