Introduction to Data Science & Regression Models in R
6:00 PM – Networking
6:30 PM – 8PM – Introduction to Data Science & Regression Models in R
Prerequisite: A basic familiarity with R. Laptop users should have R installed
Overview: Understanding statistical methods is key in the process of making sound business decisions. Linear regression is the most widely-used method for the statistical analysis of non-experimental (observational) data. In this seminar you will learn how to analyze data using regression methods, interpret results, and use them for prediction and hypothesis testing. We will also practice running regressions in the open-source statistical software program, R.
About Speaker: Victoria Liublinska received a PhD in Statistics from Harvard University. Her research was focused on developing methods for sensitivity analyses of conclusions obtained from studies with missing data. It resulted in several publications in leading peer-reviewed journals.
After finishing her degree, she stayed at Harvard and taught statistics for several years. This year she transitioned to a senior research position at Harvard University Institutional Research office.
Victoria has a plethora of consulting experience in many different fields, including business and marketing (interned at Google, worked with Concentric, Inc.), clinical (assisted on multiple studies at NYU School of Medicine), people analytics (worked with HR at Biogen, Inc.), biology (worked with researchers at the Arnold Arboretum), and litigation (provided expert opinion on multiple cases).