Providing crisp, clear, actionable points-of-view to senior executives is becoming an increasingly important role of data scientists and data professionals these days. Now, a point-of-view must represent a hypothesis, ideally backed by data. In this course, Experimental Design for Data Analysis, you will gain the ability to construct such hypotheses from data and use rigorous frameworks to test whether they hold true. First, you will learn how inferential statistics and hypothesis testing form the basis of data modeling and machine learning. Next, you will discover how the process of building machine learning models is akin to that of designing an experiment and how training and validation techniques help rigorously evaluate the results of such experiments. Then, you will round out the course by studying various forms of cross-validation, including both singular and iterative techniques to cope with independent, identically distributed data and grouped data. Finally, you will also learn how you can refine your models using these techniques with hyperparameter tuning. When you’re finished with this course, you will have the skills and knowledge to build and evaluate models, specifically including machine learning models, using rigorous cross-validation frameworks and hyperparameter tuning.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more