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Java for Data Science

Java for Data Science

By : Richard M. Reese , Reese
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Java for Data Science

Java for Data Science

By: Richard M. Reese , Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
Table of Contents (19 chapters)
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Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
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Preface

Chapter 3. Data Cleaning

Real-world data is frequently dirty and unstructured, and must be reworked before it is usable. Data may contain errors, have duplicate entries, exist in the wrong format, or be inconsistent. The process of addressing these types of issues is called data cleaning. Data cleaning is also referred to as data wrangling, massaging, reshaping , or munging. Data merging, where data from multiple sources is combined, is often considered to be a data cleaning activity.

We need to clean data because any analysis based on inaccurate data can produce misleading results. We want to ensure that the data we work with is quality data. Data quality involves:

  • Validity: Ensuring that the data possesses the correct form or structure

  • Accuracy: The values within the data are truly representative of the dataset

  • Completeness: There are no missing elements

  • Consistency: Changes to data are in sync

  • Uniformity: The same units of measurement are used

There are several techniques and tools used...

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