Data Science is the critical skill of the modern workforce. Data science skills allow for the extraction of key insights and relevant knowledge from large datasets. This data science training course is for Python developers who want to learn how to use the key libraries in the Python Data Science ecosystem. This course covers NumPy for numerical data processing, Pandas for data analysis, and Matplotlib and Seaborn for quick visualizations and statistical reporting. The training is all conducted in a modern Jupyter Notebook format, allowing students to read and write code directly within our training notebooks.

You’ll learn everything you need to know for the full data science tech stack required to work at the world’s top companies. This Python for Data Science course takes a structured approach that will guide you through understanding not just how to use data science, but why we use them by providing a balance between practical real world case studies and mathematical theory behind the data science algorithms.

Please note, this course is able to be offered in either 3 full day sessions or 5 evening sessions. See the schedule below.

Master the skills to use machine learning in your day-to-day work with this Python course. Create algorithms to predict classes, continuous values, and more. This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Machine Learning. With a focus on SciKit Learn, you’ll learn all aspects of Machine Learning ranging from a variety of regression types (Linear / Lasso /Ridge), Elastic Net, K Nearest Neighbors and Means Clustering, Hierarchal Clustering, DBSCAN, PCA, and Model Deployment.

As an experienced Java Standard Edition (Java SE) developer, this course introduces you to the world of Java Enterprise Edition (Java EE). You will learn about the various specifications that make up Java EE. Through hands-on labs, you will transform a simple Java SE command line application into a multi-tiered enterprise application using various Java EE specifications, including Enterprise Java Beans, Java Persistence API, Java Messaging Service, JAX-RS for REST services, Contexts and Dependency Injection (CDI), and JAAS for securing the application. This course is based on Red Hat® Enterprise Application Platform 7.0.

Develop microservice-based applications with Quarkus and OpenShift. Many enterprises are looking for a way to take advantage of cloud-native architectures, but many do not know the best approach. Quarkus is an exciting new technology that brings the reliability, familiarity, and maturity of Java Enterprise with a container-ready lightning fast deployment time. Red Hat Cloud-native Microservices Development with Quarkus (DO378) emphasizes learning architectural principles and implementing microservices based on Quarkus and OpenShift. You will build on application development fundamentals and focus on how to develop, monitor, test, and deploy modern microservices applications.. This course is based on OpenShift 4.5 and Quarkus 1.7L