Experimental Design for Data Analysis

This course covers conceptual and practical aspects of building and evaluating machine learning models in a way that uses data judiciously, while also accounting for considerations such as ordering and relationships within data and other biases.
Course info
Level
Intermediate
Updated
Jun 20, 2019
Duration
2h 45m
Table of contents
Course Overview
Designing an Experiment for Data Analysis
Building and Training a Machine Learning Model
Understanding and Overcoming Common Problems in Data Modeling
Leveraging Different Validation Strategies in Data Modeling
Tuning Hyperparameters Using Cross Validation Scores
Description
Course info
Level
Intermediate
Updated
Jun 20, 2019
Duration
2h 45m
Description

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.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi, my name is Janani Ravi, and welcome to this course on Experimental Design for Data Analysis. A little about myself; I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. Providing crisp, clear, actionable points of view for 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, 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. You will then see how training and validation techniques help rigorously evaluate the results of such experiments. Finally, you'll 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 group data. You will also learn how you can refine your models using these techniques with hyper parameter 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 hyper parameter tuning.