Hands-On Data Science with Java [Video]

More Information
Learn
  • Perform data science tasks using a set of robust Java tools and libraries
  • Develop robust Java code that correctly executes in multiple environments
  • Load and analyze data from databases and flat files irrespective of the size and variety of the data
  • Clean and filter data and handle null values for data formatting
  • Visualize data to understanding its distribution and discover hidden patterns
  • Compare supervised and unsupervised machine learning models and their use cases
  • Implement machine learning and deep learning models with real-world data sets
  • Split training and testing datasets and determine the accuracy of models using different techniques
About

Building intensive data science projects is a long and tedious process. Analyzing large data sets requires knowledge of how to deal with all data structures. This means easy access, easier storage, and faster loading. Java provides an efficient way of doing these tasks to improve the efficiency of such data-intensive projects.
In this course, you will use efficient Java libraries to simplify your data analysis. You will perform essential tasks such as loading, cleaning, and visualizing your data. You'll connect your data with different frameworks, making it easier to analyze small and large data sets. Using the DeepLearning4j library makes training your ML models that much simpler.
By the end of the course, you will be able to build sophisticated and robust data science projects. You will simplify the integration challenges in production using Java. All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-on-Data-Science-with-Java

Style and Approach

In this hands-on course, the focus is on solving real-world problems. You will start with an introduction to loading data from different sources, and data cleaning, exploration, and visualization. You will implement different machine learning models and build Deep learning models compatible with JVM using Deeplearning4J. To ensure you understand everything from the beginning, we will use familiar data sets in the different scenarios to reduce the time taken to become familiar with the data set.

Features
  • Delve into data analytics and perform data science tasks using Java and its support libraries
  • Perform data science tasks such as data cleaning, exploration, and visualization and deploy ML models using JVM libraries
  • Learn data exploration and the implementation of machine learning models to build reliable and accurate models using different data sets.
Course Length 3 hours 5 minutes
ISBN 9781787125346
Date Of Publication 30 Mar 2019

Authors

Rahab Wangari

Rahab Wangari is a data science expert with over 4 years' experience in data analytics and software engineering. She is currently a data scientist at Hepta Analytics limited where she oversees the implementation of data analytics projects. She holds a Masters degree in Information Technology with a concentration in Data Science from Carnegie Mellon University and a Bachelor of Science degree in Computer Science (Cum Laude) from Ashesi University College. Her background is in diverse sectors: Academia, Banking, and Manufacturing. Her major interests are in data analytics, machine learning, cyber security management, and software engineering.