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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Beginners with basic Python skills

Who should attend & recommended skills

  • This course is designed for beginners who want to use Java to create a diverse range of Data Science applications and bring Data Science into production.
  • Skill-level: Foundation-level Java for Data Science and Jupyter skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • 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.
  • Getting a solid understanding of the data processing toolbox available in Java
  • The Data Science ecosystem available in Java
  • How to approach different Machine Learning problems with Java
  • Processing unstructured information such as natural language text or images
  • Creating your own search engine
  • Getting state-of-the-art performance with XGBoost
  • Learning how to build deep neural networks with DeepLearning4j
  • Building applications that scale and process large amounts of data
  • Deploying data science models to production and evaluate their performance

Course breakdown / modules

  • Data science
  • Data science process models
  • Data science in Java

  • Standard Java library
  • Extensions to the standard library
  • Accessing data
  • Search engine – preparing data

  • Exploratory data analysis in Java
  • Interactive Exploratory Data Analysis in Java

  • Classification
  • Case study – page prediction
  • Regression
  • Case study – hardware performance

  • Dimensionality reduction
  • Cluster analysis

  • Natural Language Processing and information retrieval
  • Machine learning for texts

  • Gradient Boosting Machines and XGBoost
  • XGBoost in practice

  • Neural Networks and DeepLearning4J
  • Deep learning for cats versus dogs

  • Apache Hadoop
  • Apache Spark
  • Link prediction
  • Summary
  • 10Deploying Data Science Models
  • Deploying Data Science Models
  • Microservices
  • Online evaluation