Code Repository for Hands-on Data Science with Java, published by Packt
This is the code repository for Hands-On Data Science with Java [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda
- Use the Conda package manager and discover, install, and use functionally efficient and scalable packages
- Get comfortable with heterogeneous data exploration using multiple languages within a project
- Perform distributed computing and use Anaconda Accelerate to optimize computational power
- Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
- Tackle advanced data prediction problems
To fully benefit from the coverage included in this course, you will need:
This course is for Java developers who want to perform data science activities without needing to learn other languages for analytics. Basic knowledge of Java is assumed. If you want to gain practical skills in analyzing data and performing data manipulation with Java, this course is for you!
This course has the following software requirements:
OS: Windows 10/Mac OS 10.6
Processor: Intel Pentium III/800 MHz or higher (or compatible)
Memory: 16GB
Storage: 15GB of available hard-disk space
Software Requirements
Libraries to be used: Tablesaw DeepLearning4j Libraries: Smile and Kumo