Learning Path: Java: Big Data Analysis with Java

Duration: 7h 0m | Video: .MP4, 1280×720 25 fps | Audio: AAC, 48kHz, 2ch | Size: 1.04 GB
Genre: eLearning | Language: English + Subtitles

Handle big data and perform visualization techniques to gain deeper insights of your data

What you’ll learn
Familiarize with various data pre-processing techniques
Get to know the basics of data analysis and explore how data changes state
Implement statistical data analysis techniques using Java APIs
Work with NoSQL databases
Find out how to clean and make datasets ready so you can acquire actual insights by removing noise and outliers
Develop the skills to use modern machine learning techniques to retrieve information and transform data to knowledge
Perform clustering and feature selection exercises using the Weka machine learning Workbench
Learn application of core Java and popular libraries, such as OpenNLP, Stanford CoreNLP, Mallet, and Weka
Familiarize yourself with the very basics of deep learning using the deep learning for Java (DL4j) library

Requirements
Basic programming knowledge of Java
Basic programming knowledge MySQL

Description
Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. If you’re interested to know the statistical data analysis techniques and implement them using the popular Java APIs and libraries, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

The highlights of this Learning Path are:

Get your basics on point to perform data analysis with Java
Solutions to help you overcome your data science hurdles using Java

Let’s take a quick look at your learning journey. This Learning Path starts by showing you the various techniques of pre-processing your data. You will get well-versed with the basics of data analysis with Java, how data changes state, and how Java fits into the analysis. You will then learn to apply the basic analysis to your business needs and create time-series predictions. You will also see how to implement statistical data analysis techniques using Java APIs. If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This Learning Path will help you to learn how you can retrieve data from data sources with different level of complexities. You will learn how you could handle big data to extract meaningful insights from data. Finally, you will dive into visualizing data to uncover trends and hidden relationships.
By the end of this Learning Path, you will be able to analyze your data, retrieve data from data sources with different level of complexities, and also write and modify applications that perform data analysis in a step-by-step manner.

Meet Your Experts:

We have combined the best works of the following esteemed author to ensure that your learning journey is smooth:

Erik Costlow was the principal product manager for Oracle’s launch of Java 8. His background is in software security analysis, dealing with the security issues that rose to the surface within Java 6 and Java 7. While working on the JDK, Erik applied different data analysis techniques to identify and mitigate ways that threats could propagate through the overall Java platform and overlying applications.

Rushdi Shams has a Ph.D. on application of machine learning in Natural Language Processing (NLP) problem areas from Western University, Canada. Before starting work as a machine learning and NLP specialist in the industry, he was engaged in teaching undergrad and grad courses. He has been successfully maintaining his YouTube channel named “Learn with Rushdi” for learning computer technologies.

Who this course is for:
This Learning Path is for those who are mid-level developers and architects who are familiar with Java programming and Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro.

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