Tanya Cashorali is the founding partner of TCB Analytics, a Boston-based data consultancy. Prior to launching TCB Analytics, she worked as a data scientist at Biogen. Tanya started her career in bioinformatics and has applied her experience to other data-rich verticals such as telecom, finance, and sports. She brings over 10 years of experience using R in data scientist roles as well as managing and training data analysts, and she’s helped grow a handful of Boston startups.
Tools used: R, shiny
In this video, you will learn the benefits of rapid prototyping using Shiny. You will see some of the examples start conversations with users. It is explained the philosophy of building data products starting from rapid prototyping with R and Shiny, based on the following mantra: “Done is better than perfect”.
- Learn the benefits of rapid prototyping.
- Know several examples of prototyping with R and Shiny.
- Learn how to approach new data challenges and build data products.
0:00 - 4:18: Introduction and Overview
- Data first, technology later.
4:18 - 8:37: How to build data products: Rapid Prototyping
- Just build it, as Ben Barry said: “Done is better than perfect”.
- Use standards to start a data-driven culture.
8:37 - 11:18: Overwatch game example
- You have people interested when you present new data.
11:18 - 12:42: Drug manufacturing browser example
12:42 - 13:54: Hospital staffing management example
13:54 - 15:07: Visualize Infosec CISO growth example
15:07 - 15:55: Linear optimization for daily fantasy NBA lineups example
15:55 - 16:53: Visually discover new recipes example
16:53 - 18:20: Boston Open Data Challenge example
18:20 - 20:26: Recap
- Make sure you have good data. Forget the required documentation. Start building. Have conversations. Feedback your model. Get your data product.
20:26 - 25:40: Questions
- Where to start with Shiny. Some resources to learn R.
- Can Shiny be used within Docker?