At this point, most of us know the basics of using and deploying R—maybe you took a class on it, maybe you participated in a hackathon. That’s all important (and we have tracks for getting started with Python if you’re not there yet), but once you have those baseline skills, where do you go? You move from just knowing how to do things, to expanding and applying your skills to the real world.
[Related Article: Data-Driven Exploration of the R User Community Worldwide]
For example, in the talk “Introduction to RMarkdown in Shiny” you’ll go beyond the basic tools and techniques and be able to focus in on a framework that’s often overlooked. You’ll be taken through the course by Jared Lander, the chief data scientist at Lander Analytics, and the author of R for Everyone. If you wanted to delve into the use of a new tool, this talk will give you a great jumping-off point.
Or, you could learn to tackle one of data science’s most common issues: the black box problem. Presented by Rajiv Shah, Data Scientist at DataRobot, the workshop “Deciphering the Black Box: Latest Tools and Techniques for Interpretability” will guide you through use cases and actionable techniques to improve your models, such as feature importance and partial dependence.
If you want more use cases, you can be shown a whole spread of them and learn to understand the most important part of a data science practice: adaptability. The talk, “Adapting Machine Learning Algorithms to Novel Use Cases” by Kirk Borne, the Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, will explain some of the most interesting use cases out there today, and help you develop your own adaptability.
More and more often, businesses are looking for specific solutions to problems they’re facing—they don’t want to waste money on a project that doesn’t pan out. So maybe instead, you want to learn how to use R for common business problems. In the session “Building Recommendation Engines and Deep Learning Models using Python, R, and SAS,” Ari Zitin, an analytical training consultant at SAS, will take you through the steps to apply neural networks and convolutional neural networks to business issues, such as image classification, data analysis, and relationship modeling.
You can even move beyond the problems of your company and help solve a deep societal need, in the talk “Tackling Climate Change with Machine Learning.” Led by NSF Postdoctoral Fellow at the University of Pennsylvania, David Rolnick, you’ll see how ML can be a powerful tool in helping society adapt and manage climate change.
And if you’re keeping the focus on real-world applications, you’ll want to make sure you’re up-to-date on the ones that are the most useful. The workshop “Building Web Applications in R Using Shiny” by Dean Attali, Founder and Lead Consultant at AttaliTech, will show you a way to build a tangible, accessible web app that (by the end of the session) can be deployed for use online, all using R Shiny. It’ll give you a skill to offer employers, and provide you with a way to better leverage your own work.
Another buzz-worthy class that will keep you in the loop is the “Tutorial on Deep Reinforcement Learning” by Pieter Abbeel, a professor at UC Berkeley, the founder/president/chief scientist at covariant.ai, and the founder of Gradescope. He’ll cover the basics of deepRL, as well as some deeper insights on what’s currently successful and where the technology is heading. It’ll give you information on one of the most up-and-coming topics in data science.
After all that, you’ll want to make sure your data looks and feels good for presentation. Data visualization can make or break your funding proposal or your boss’s good nature, so it’s always an important skill to brush up on. Mark Schindler, co-founder and Managing Director of GroupVisual.io, will help you get there in his talk “Synthesizing Data Visualization and User Experience.” Because you can’t make a change in your company, in your personal work, or in the world, without being able to communicate your ideas.
[Related Article: Timing the Same Algorithm in R, Python, and C++]
Ready to apply all of your R skills to the above situations? Learn more techniques, applications, and use cases at ODSC West 2019 in San Francisco this October 29 to November 1! Register here.
Originally Posted Here