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Apache Spark for Data Analytics

The ability to store, aggregate, and analyze large amounts of data has transformed nearly every industry. Whether finance, medicine, entertainment, government, or technology; the dream is the same: use enormous amounts of data to understand problems, predict outcomes, and take effective action. While many advances make the dream of “big data” possible, one of the most important components of the technical stack is the engine that provides the “distributed computing.” In many organizations, Apache Spark is the computational engine that powers big data. A general purpose unified analytics engine built to transform, aggregate, and analyze large amounts of information; Spark has become the de-facto brain behind large scale data processing, machine learning, and graph analysis. It has seen rapid adoption by companies such as Netflix, Google, eBay, and others to analyze at massive scale processing petabytes of data on clusters of thousands of nodes. In this course, we will explore how Apache Spark can be used for data processing. We will cover the fundamentals of Spark including the architecture and internals, the core APIs and data structures, and how Spark can be used for machine learning and analyzing streaming data sets. Throughout the course, you will:
  • Understand when and where to use Spark.
  • Leverage strategies to create data-driven questions that can provide scientific or business value.
  • Learn how to use Apache spark to load, summarize, query, and visualize structured and semi-structured data.
  • Introduce common machine learning techniques that can be used to solve supervised and unsupervised problems inside of Spark.
  • Learn how to analyze streaming data using Spark Streams.
  • Gain hands-on experience with techniques for deploying Spark as part of larger software system.

Big Data Infrastructure

In recent years, the amount of data produced, stored, and tracked has increased exponentially. What are the tools and techniques that are used to work with that information and how can it be brought to provide insight and inform decision making? This course will introduce the fundamental infrastructure of working with Big Data: containers, clouds, storage, compute, and search.

Data Science Technology Training Series for Leaders

Data Science Technology Series Introduction for the Leaders As a leader, how to leverage your data is crucial.  In these Instructor Led modules let us help you understand what Data Science is all about.  This course will help you learn the why, how, and where data science can make an impact in your organization.  Including how to have conversations with your team and to make better strategic decisions around data mining and how you implement it.

EXE101: Building a Data-Driven Strategy

This course is designed for individuals who don’t have a background in math or programming that want to receive a strong foundation in data science frameworks.  We will discuss how to use data to make decisions.

Goal: To become a Data Literate Manager.

Fundamentals of Azure

This course will provide foundational level knowledge of cloud services, and how cloud services are provided with Microsoft Azure.  The course can be taken as an optional first step in learning about cloud services and Microsoft Azure.

Modern DevOps Using Docker and Kubernetes

Containers are a powerful tool for developing and managing software. When combined with DevOps practices, it is possible for organizations to efficiently deliver applications and services at high velocity. This training course introduces containers and two of the most popular tools for their management and orchestration: Docker and Kubernetes. They will be introduced to the core components of both Docker and Kubernetes as they apply to the software development process. Topics covered include: containers, tools for local management, pods, labels, volumes, networking, replication controllers, services, and stateful sets. Students will get hands-on experience with how the technologies can be used to deliver software following microservices and DevOps strategies, and will create a complete continuous integration and deployment (CICD) pipeline showing how Docker and Kubernetes can be used to manage the software lifecycle. Components of the broader software ecosystem including Ansible, Jenkins, and Spinnaker will also be covered as they pertain to Docker/Kubernetes. At the end of the course, participants and teams will understand how to build secure, robust, highly available services that are resilient and able to adapt to rigorous application demands. They will also be able to show how components of the ecosystem can be combined to create larger systems addressing complex use-cases.

Practical Data Science and Machine Learning

Data is the residue of every action that takes place in a company, with customers, and in the marketplace. It is created when customers buy products, users interact with services, and colleagues collaborate. In an increasingly connected world, our ability to capture and leverage data has increased exponentially; but data in the wrong hands is useless, if not dangerous. In the right hands, data can drive new insights and powerfully informed decisions. This course teaches the fundamentals of Machine Learning using hands-on coding exercises in Python. Basic Python is reviewed and taught as needed throughout the course so no prior Python experience is required though some basic experience with programming general is assumed.

Python Programming – Advanced

Learn how to write, read and troubleshoot Python scripting. In all courses in this series each bulleted subject is accompanied by a live-coding demonstration, and followed by one or more hands-on exercises; Additionally, after each major section there will be one or more larger code challenges that combine the skills learned in the previous exercises.

Python Programming – Introduction

Learn how to write, read and troubleshoot Python scripting.  This 3 day course is recommended for an introduction to Python topics. In this course each bulleted subject is accompanied by a live-coding demonstration, and followed by one or more hands-on exercises; Additionally, after each major section there will be one or more larger code challenges that combine the skills learned in the previous exercises.


Ever used an app or been on a website that was completely dysfunctional?  In this class you will learn how important the user experience is and how to build functional apps and websites.  Our approach is a combination of lecture and hands practical exercises to help a student learn the full UI/UX development model.  The student will be able to summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps.