Experimental Design for Data Analysis
The first best online experimental design course you should know is 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. Providing crisp, 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.
This course offers:
- Flexible deadlines: Reset deadlines based on your availability.
- Get a Certificate when you complete
- 100% online
- Beginner level
- Approximately 3 hours to complete
- Subtitles: English
Participants: 353
Enroll here: https://www.pluralsight.com/courses/experimental-design-data-analysis
