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© 2018

Advanced Data Analytics Using Python

With Machine Learning, Deep Learning and NLP Examples

  • Contains practical real-world examples of data analytics

  • Covers a wide spectrum from basic statistics to ETL, deep learning and IoT

  • Gives an idea of every technical aspect of an industrial analytics project

Book

Table of contents

  1. Front Matter
    Pages i-xv
  2. Sayan Mukhopadhyay
    Pages 1-22
  3. Sayan Mukhopadhyay
    Pages 23-48
  4. Sayan Mukhopadhyay
    Pages 49-76
  5. Sayan Mukhopadhyay
    Pages 77-98
  6. Sayan Mukhopadhyay
    Pages 99-119
  7. Sayan Mukhopadhyay
    Pages 121-143
  8. Sayan Mukhopadhyay
    Pages 145-179
  9. Back Matter
    Pages 181-186

About this book

Introduction

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. 

After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects.

You will:
  • Work with data analysis techniques such as classification, clustering, regression, and forecasting
  • Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
  • Examine the different big data frameworks, including Hadoop and Spark
  • Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP

Keywords

Python Analytics Hadoop Storm Apache Spark Machine Learning Deep Learning Neo4j Time Series Elastic Search

Authors and affiliations

  1. 1.KolkataIndia

About the authors

Sayan Mukhopadhyay in his 13+ years industry experience has been associated with companies such as Credit-Suisse, PayPal, CA Technology, CSC, and Mphasis. He has a deep understanding of the applications of data analysis 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.

Bibliographic information

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