Statistics for Data Science
Statistics for Data Science

Abstract: 

Data science has enabled massive leaps in organizational decision-making in recent years, driven largely by bringing statistical methods to bear at greater scale and speed than ever before. This workshop will review basic techniques for statistical analysis (e.g., group comparison, regression analysis) and progress onto more advanced methods required when real world problems violate standard assumptions (e.g., Bayesian inference). This workshop assumes a working knowledge of standard statistical methods and will aim to connect theory to practice using real-world examples.

Bio: 

Jake Dailey is a Lead Data Scientist at Nielsen, where he applies extensive knowledge of statistics and machine learning as well as strong skills in Python, PySpark, PyTorch, Tensorflow, and more to build smart, scalable media measurement products. He has experience teaching these skills, most recently in his course 'Theoretical Foundations of Machine Learning' as part of Stanford University's Pre-Collegiate Summer Institute."

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