Overview
- Explains recommendation system, algorithm trading, and PySpark with use cases and hands-on coding
- Explains feature engineering in images and texts with Python
- Covers recent advances in databases such as Neo4j, Elasticsearch, and MongoDB
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About this book
Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.
Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.
What You'll Learn
Who This Book Is For
Data scientists and software developers interested in the field of data analytics.
Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.
Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticswith reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application.
What You'll Learn
- Build intelligent systems for enterprise
- Review time series analysis, classifications, regression, and clustering
- Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning
- Use cloud platforms like GCP and AWS in data analytics
- Understand Covers design patterns in Python
Who This Book Is For
Data scientists and software developers interested in the field of data analytics.
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Keywords
Table of contents (7 chapters)
Authors and Affiliations
About the authors
Sayan Mukhopadhyay is a data scientist with more than 13 years of experience. He has been associated with companies such as Credit-Suisse, PayPal, CA Technology, CSC, and Mphasis. He has a deep understanding of data analysis applications in domains such as investment banking, online payments, online advertising, IT infrastructure, and retail. His area of expertise is applied high-performance computing in distributed and data-driven environments such as real-time analysis and high-frequency trading.
Pratip Samanta is a Principal AI engineer/researcher having more than 11 years of experience. He worked in different software companies and research institutions. He has published conference papers and granted patents in AI and Natural Language Processing. He is also passionate about gardening and teaching.
Pratip Samanta is a Principal AI engineer/researcher having more than 11 years of experience. He worked in different software companies and research institutions. He has published conference papers and granted patents in AI and Natural Language Processing. He is also passionate about gardening and teaching.
Bibliographic Information
Book Title: Advanced Data Analytics Using Python
Book Subtitle: With Architectural Patterns, Text and Image Classification, and Optimization Techniques
Authors: Sayan Mukhopadhyay, Pratip Samanta
DOI: https://doi.org/10.1007/978-1-4842-8005-8
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Sayan Mukhopadhyay, Pratip Samanta 2023
Softcover ISBN: 978-1-4842-8004-1Published: 26 November 2022
eBook ISBN: 978-1-4842-8005-8Published: 25 November 2022
Edition Number: 2
Number of Pages: XVII, 249
Number of Illustrations: 32 b/w illustrations
Topics: Data Structures and Information Theory, Artificial Intelligence, Machine Learning, Python