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Practical Data Analysis Cover

Practical Data Analysis

Pandas, MongoDB, Apache Spark, and more

Publisher:Packt Publishing Limited
By: Hector Cuesta and  Dr. Sampath Kumar  
Paid access
|Sep 2025
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A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark

Key Features

  • Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data
  • Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images
  • A hands-on guide to understanding the nature of data and how to turn it into insight

Book Description

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.
This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.

What you will learn

  • Acquire, format, and visualize your data
  • Build an image-similarity search engine
  • Generate meaningful visualizations anyone can understand
  • Get started with analyzing social network graphs
  • Find out how to implement sentiment text analysis
  • Install data analysis tools such as Pandas, MongoDB, and Apache Spark
  • Get to grips with Apache Spark
  • Implement machine learning algorithms such as classification or forecasting

Who this book is for

This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed.

Table of Contents

  1. Getting Started with Data Analysis
  2. Preprocessing the Data
  3. Getting to Grips with Visualization
  4. Text Classification
  5. Similarity-based Image Retrieval
  6. Simulation of Stock Prices
  7. Predicting Gold Prices
  8. Working with Support Vector Machines
  9. Modeling Infectious Disease with Cellular Automata
  10. Visualizing Social Network Graphs
  11. Sentiment Analysis of Twitter Data
  12. Data Processing and Aggregation with MongoDB
  13. Working with MapReduce
  14. On-line Data Analysis with Jupyter and Wakari
  15. Big Data Using Spark
PDF ISBN: 978-1-78528-666-7
Publisher: Packt Publishing Limited
Copyright owner: © 2016 Packt Publishing Limited
Publication date: 2025
Language: English
Pages: 338
Related subjects:
Computer sciences,
Programming and languages

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