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Advanced Data Analytics Using Python

With Machine Learning, Deep Learning and NLP Examples

  • Book
  • © 2018

Overview

Authors:
  1. Sayan Mukhopadhyay

    1. Kolkata, India

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  • 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

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Sayan Mukhopadhyay
    Pages 1-22

  3. ETL with Python (Structured Data)

    • Sayan Mukhopadhyay
    Pages 23-48

  4. Supervised Learning Using Python

    • Sayan Mukhopadhyay
    Pages 49-76

  5. Unsupervised Learning: Clustering

    • Sayan Mukhopadhyay
    Pages 77-98

  6. Deep Learning and Neural Networks

    • Sayan Mukhopadhyay
    Pages 99-119

  7. Time Series

    • Sayan Mukhopadhyay
    Pages 121-143

  8. Analytics at Scale

    • Sayan Mukhopadhyay
    Pages 145-179

  9. Back Matter

    Pages 181-186
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Keywords

About this book

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.


What You Will Learn
  • 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



Who This Book Is For


Data scientists and software developers interested in the field of data analytics.









Authors and Affiliations

  • Kolkata, India

    Sayan Mukhopadhyay

About the author

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.


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