Get hands-on experience with the science and research aspects of data science work, from setting up a proper data study to making valid claims and inferences from data experiments.
Data scientists are often trained in the analysis of data. However, the goal of data science is to produce good understanding of some problem or idea and build useful models on this understanding. Because of the principle of "garbage in, garbage out," it is vital that the data scientist know how to evaluate the quality of information that comes into a data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).
In this course, you will learn the fundamentals of the research process--from developing a good question to designing good data collection strategies to putting results in context. Although the data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.
Developed as a language with statistical analysis and modeling in mind, R has become an essential tool for doing real-world Data Science. With this edition of Data Science Research Methods, all of the labs are done with R, while the videos are tool-agnostic. If you prefer your Data Science to be done with Python, please see Data Science Research Methods: Python Edition.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
Note: These courses will retire in June. Please enroll only if you are able to finish your coursework in time.
After completing this course, you will be familiar with the following concepts and techniques:
Register to leave a review