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Java for Data Science

  • Course Code: Data Science - Java for Data Science
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to use Java to create a diverse range of Data Science applications and bring Data Science into production.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Java for Data Science and Jupyter skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to use Java to create a diverse range of Data Science applications and bring Data Science into production.   
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This course will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the course by talking about the ways to deploy the model and evaluate it in production settings. 

Working in a hands-on learning environment, led by our Java for Data Science expert instructor, students will learn about and explore: 

  • An overview of modern Data Science and Machine Learning libraries available in Java 
  • Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. 
  • Easy-to-follow illustrations and the running example of building a search engine. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get a solid understanding of the data processing toolbox available in Java 
  • Explore the Data Science ecosystem available in Java 
  • Find out how to approach different Machine Learning problems with Java 
  • Process unstructured information such as natural language text or images 
  • Create your own search engine 
  • Get state-of-the-art performance with XGBoost 
  • Learn how to build deep neural networks with DeepLearning4j 
  • Build applications that scale and process large amounts of data 
  • Deploy data science models to production and evaluate their performance 

Audience & Pre-Requisites 

This course is designed for beginners who wants to use Java to create a diverse range of Data Science applications and bring Data Science into production 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Data Science Using Java 
  • Data Science Using Java 
  • Data science 
  • Data science process models 
  • Data science in Java 
  1. Data Processing Toolbox 
  • Data Processing Toolbox 
  • Standard Java library 
  • Extensions to the standard library 
  • Accessing data 
  • Search engine – preparing data 
  1. Exploratory Data Analysis 
  • Exploratory Data Analysis 
  • Exploratory data analysis in Java 
  • Interactive Exploratory Data Analysis in Java 
  1. Supervised Learning – Classification and Regression 
  • Supervised Learning – Classification and Regression 
  • Classification 
  • Case study – page prediction 
  • Regression 
  • Case study – hardware performance 
  1. Unsupervised Learning – Clustering and Dimensionality Reduction 
  • Unsupervised Learning – Clustering and Dimensionality Reduction 
  • Dimensionality reduction 
  • Cluster analysis 
  1. Working with Text – Natural Language Processing and Information Retrieval 
  • Working with Text – Natural Language Processing and Information Retrieval 
  • Natural Language Processing and information retrieval 
  • Machine learning for texts 
  1. Extreme Gradient Boosting 
  • Extreme Gradient Boosting 
  • Gradient Boosting Machines and XGBoost 
  • XGBoost in practice 
  1. Deep Learning with DeepLearning4J 
  • Deep Learning with DeepLearning4J 
  • Neural Networks and DeepLearning4J 
  • Deep learning for cats versus dogs 
  1. caling Data Science 
  • Scaling Data Science 
  • Apache Hadoop 
  • Apache Spark 
  • Link prediction 
  • Summary 
  • 10Deploying Data Science Models 
  • Deploying Data Science Models 
  • Microservices 
  • Online evaluation 

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