From Renze Consulting
Matthew is a data science consultant with over 17 years of professional experience. 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.
R is a popular open-source programming language for data analysis. Its interactive programming environment and data visualization capabilities make R an ideal tool for creating professional data visualizations. This session will provide an introduction to the R programming language using RStudio. In addition, we will demonstrate how we can use R to create data visualizations to transform our data into actionable insight.
Who should attend your session?
This session is intended for developers who are interested in learning how to create data visualizations with the programming language R. We'll learn how to use R to transform raw data into actionable insight using a variety of data visualization techniques.
What are the 'next steps' for an attendee to take after attending your session?
I recommend that they download and install a free copy of R and use it to explore their data at work. Then, I encourage them to begin using R, instead of a tool like Excel, whenever they need to create data visualizations. With a bit of practice they will be able to leverage their data-visualization skills to provide new insight to their team and their organization.
Who is your favourite fictional British character?
Sherlock Holmes is, by far, my favorite fictional British character. I also love seeing his character archetype infused into other fictional characters like Dr. Who, Dr. House, and Rick Sanchez.
What does the language R offer that others don't?
R is the most powerful language I've seen for working with data, data analysis, data visualization, statistical modeling, and machine learning. If you work with data everyday, which most developers do, you really need to be learning R.
What is the most important principle when getting started with data analysis?
Always begin with a question. Starting with a question that you want to answer gives you direction and focus. In addition, it allows you to determine what data, tools, and processes will be necessary to answer your question and communicate this insight effectively to your audience.