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Java for Data Science
Java for Data Science

Java for Data Science: Examine the techniques and Java tools supporting the growing field of data science

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Profile Icon Richard M. Reese Profile Icon Reese
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€8.99 €32.99
eBook Jan 2017 386 pages 1st Edition
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€8.99 €32.99
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Arrow left icon
Profile Icon Richard M. Reese Profile Icon Reese
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€8.99 €32.99
eBook Jan 2017 386 pages 1st Edition
eBook
€8.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €32.99
Paperback
€41.99
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Renews at €18.99p/m

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Java for Data Science

Understanding the data formats used in data science applications


When we discuss data formats, we are referring to content format, as opposed to the underlying file format, which may not even be visible to most developers. We cannot examine all available formats due to the vast number of formats available. Instead, we will tackle several of the more common formats, providing adequate examples to address the most common data retrieval needs. Specifically, we will demonstrate how to retrieve data stored in the following formats:

  • HTML

  • PDF

  • CSV/TSV

  • Spreadsheets

  • Databases

  • JSON

  • XML

Some of these formats are well supported and documented elsewhere. For example, XML has been in use for years and there are several well-established techniques for accessing XML data in Java. For these types of data, we will outline the major techniques available and show a few examples to illustrate how they work. This will provide those readers who are not familiar with the technology some insight into their nature.

The most...

Data acquisition techniques


In this section, we will illustrate how to acquire data from web pages. Web pages contain a potential bounty of useful information. We will demonstrate how to access web pages using several technologies, starting with a low-level approach supported by the HttpUrlConnection class. To find pages, a web crawler application is often used. Once a useful page has been identified, then information needs to be extracted from the page. This is often performed using an HTML parser. Extracting this information is important because it is often buried amid a clutter of HTML tags and JavaScript code.

Using the HttpUrlConnection class

The contents of a web page can be accessed using the HttpUrlConnection class. This is a low-level approach that requires the developer to do a lot of footwork to extract relevant content. However, he or she is able to exercise greater control over how the content is handled. In some situations, this approach may be preferable to using other API libraries...

Summary


In this chapter, we discussed types of data that are useful for data science and readily accessible on the Internet. This discussion included details about file specifications and formats for the most common types of data sources.

We also examined Java APIs and other techniques for retrieving data, and illustrated this process with multiple sources. In particular, we focused on types of text-based document formats and multimedia files. We used web crawlers to access websites and then performed web scraping to retrieve data from the sites we encountered.

Finally, we extracted data from social media sites and examined the available Java support. We retrieved data from Twitter, Wikipedia, Flickr, and YouTube and examined the available API support.

Summary

In this chapter, we discussed types of data that are useful for data science and readily accessible on the Internet. This discussion included details about file specifications and formats for the most common types of data sources.

We also examined Java APIs and other techniques for retrieving data, and illustrated this process with multiple sources. In particular, we focused on types of text-based document formats and multimedia files. We used web crawlers to access websites and then performed web scraping to retrieve data from the sites we encountered.

Finally, we extracted data from social media sites and examined the available Java support. We retrieved data from Twitter, Wikipedia, Flickr, and YouTube and examined the available API support.

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Key benefits

  • • Your entry ticket to the world of data science with the stability and power of Java
  • • Explore, analyse, and visualize your data effectively using easy-to-follow examples
  • • Make your Java applications more capable using machine learning

Description

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

Who is this book for?

With its tutorial approach, this data science book has been written for experienced Java programmers who want to better understand the field of data science and learn how Java supports its underlying techniques. The step-by-step instructional style also makes Java for Data Science ideal for beginners, allowing you to get up and running quickly.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 10, 2017
Length: 386 pages
Edition : 1st
Language : English
ISBN-13 : 9781785281242
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Languages :
Concepts :
Tools :

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Product Details

Publication date : Jan 10, 2017
Length: 386 pages
Edition : 1st
Language : English
ISBN-13 : 9781785281242
Category :
Languages :
Concepts :
Tools :

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Table of Contents

12 Chapters
1. Getting Started with Data Science Chevron down icon Chevron up icon
2. Data Acquisition Chevron down icon Chevron up icon
3. Data Cleaning Chevron down icon Chevron up icon
4. Data Visualization Chevron down icon Chevron up icon
5. Statistical Data Analysis Techniques Chevron down icon Chevron up icon
6. Machine Learning Chevron down icon Chevron up icon
7. Neural Networks Chevron down icon Chevron up icon
8. Deep Learning Chevron down icon Chevron up icon
9. Text Analysis Chevron down icon Chevron up icon
10. Visual and Audio Analysis Chevron down icon Chevron up icon
11. Mathematical and Parallel Techniques for Data Analysis Chevron down icon Chevron up icon
12. Bringing It All Together Chevron down icon Chevron up icon
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