Matthew is a software and data science consultant with over 17 years of professional experience building large-scale data-driven desktop, server, and cloud-based applications. He is an international public speaker, an author for Pluralsight, a Microsoft MVP, an ASPInsider, and an open-source software contributor. His interests include data analytics, data visualization, and machine learning.
Session: Clean Architecture: Patterns, Practices, and Principles
As software grows more complex, we need to manage this complexity by using various architectural patterns, practices, and principles. In this session, we will learn how software experts keep their architecture clean using a new approach to software architecture. We’ll learn about domain-centric architectures, application layers, (CQRS) Command-Query Responsibility Separation, event sourcing, microservices, and more. You can expect to hear practical advice and see real-world examples from over 15 years of architectural experience.
Workshop: Practical Data Science with R
Data science is the practice of transforming data into knowledge. R is the most popular programming language used by data scientists. In our data-driven economy, this combination of skills is in extremely high demand, commanding significant increases in salary, as it is revolutionizing the world around us.
In this workshop, we’ll learn about the practice of data science, the R programming language, and how they can be used to transform data into actionable insight. In addition, we’ll learn how to transform and clean our data, create and interpret descriptive statistics, data visualizations, and statistical models. We’ll also learn how to handle Big Data, make predictions using machine learning algorithms, and deploy R to production.