Practical Data Science - An Overview of Data Science Best Practices for Common Business Problems

This talk will provide a framework to help you decide what data science techniques are appropriate for your business problem. A comparison of various data science algorithms will be covered, along with their trade-offs, and examples of business use cases. This talk with also cover the bias-variance trade-off error and how to account for this in your models. Additionally, topics on how to deal with common data problems - such as missing data and too many predictors - will be covered.

Kevin is currently a senior data scientist at Rock Central and has been a practicing data scientist since 2017. He has applied his data scientist expertise in numerous fields and applications such as in People Analytics, Healthcare, Market Research, and Customer Lifetime Value Modeling. He has also worked in numerous areas of data science, ranging from predictive analytics, A/B testing, NLP, and image processing. Outside of work he loves to play basketball, compose music, and play strategic card/board games while finding ways to incorporate data science and analytics to improve on these hobbies.