R programmers are not necessary data scientists, but rather software engineers. We have an entirely new multitrack focus area that helps engineers learn AI skills – AI for Engineers. This focus area is designed specifically to help programmers get familiar with AI-driven software that utilizes deep learning and machine learning models to enable conversational AI, autonomous machines, machine vision, and other AI technologies that require serious engineering.
For example, some use R in reinforcement learning – a popular topic and arguably one of the best techniques for sequential decision making and control policies. At ODSC East, Leonardo De Marchi of Badoo will present “Introduction to Reinforcement Learning,” where he will go over the fundamentals of RL all the way to creating unique algorithms, and everything in between.
Well, what if you’re missing data? That will mess up your entire workflow – R or otherwise. In “Good, Fast, Cheap: How to do Data Science with Missing Data,” Matt Brems of General Assembly, you will start by visualizing missing data and identifying the three different types of missing data, which will allow you to see how they affect whether we should avoid, ignore, or account for the missing data. Matt will give you practical tips for working with missing data and recommendations for integrating it with your workflow.
How about getting some context? In “Intro to Technical Financial Evaluation with R” with Ted Kwartler of Liberty Mutual and Harvard Extension School, you will learn how to download and evaluate equities with the TTR (technical trading rules) package. You will evaluate an equity according to three basic indicators and introduce you to backtesting for more sophisticated analyses on your own.
There is a widespread belief that the twin modeling goals of prediction and explanation are in conflict. That is, if one desires superior predictive power, then by definition one must pay a price of having little insight into how the model made its predictions. In “Explaining XGBoost Models – Tools and Methods” with Brian Lucena, PhD, Consulting Data Scientist at Agentero you will work hands-on using XGBoost with real-world data sets to demonstrate how to approach data sets with the twin goals of prediction and understanding in a manner such that improvements in one area yield improvements in the other.
What about securing your deep learning frameworks? In “Adversarial Attacks on Deep Neural Networks” with Sihem Romdhani, Software Engineer at Veeva Systems, you will answer questions such as how do adversarial attacks pose a real-world security threat? How can these attacks be performed? What are the different types of attacks? What are the different defense techniques so far and how to make a system more robust against adversarial attacks? Get these answers and more here.
Data science is an art. In “Data Science + Design Thinking: A Perfect Blend to Achieve the Best User Experience,” Michael Radwin, VP of Data Science at Intuit, will offer a recipe for how to apply design thinking to the development of AI/ML products. You will learn to go broad to go narrow, focusing on what matters most to customers, and how to get customers involved in the development process by running rapid experiments and quick prototypes.
Lastly, your data and hard work mean nothing if you don’t do anything with it – that’s why the term “data storytelling” is more important than ever. In “Data Storytelling: The Essential Data Science Skill,” Isaac Reyes, TedEx speaker and founder of DataSeer, will discuss some of the most important of data storytelling and visualization, such as what data to highlight, how to use colors to bring attention to the right numbers, the best charts for each situation, and more.
Ready to apply all of your R skills to the above situations? Learn more techniques, applications, and use cases at ODSC East 2019 in Boston this April 30 to May 3! Save 10% off the public ticket price when you use the code RBLOG10 today. Register Here