About this course
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.
Meet the instructors
Ben Olsen
Sr. Content Developer
Microsoft
Ben is a Sr. Content Developer for Microsoft's Learning and Readiness team, and is an analytics professional and educator with over 8 years of industry and managerial experience. Prior to joining Microsoft, Ben ran and directed multiple consulting firms, where he also held critical analytics roles in companies as diverse as Juniper Networks, Costco, and T-Mobile. He has taught Data Visualization at The University of Washington, and recently founded Seattle Pacific University's Analytics Certificate Program.
Tom Carpenter
Data Science and Research Consultant
Tom Carpenter is a freelance data science and research consultant and owner of Tom Carpenter PhD Consulting. Tom has a PhD in Social Psychology from Baylor University (doctoral minor in statistics). Tom has worked for several years as a freelance research consultant and statistician with several companies and research organizations, and he also teaches research and statistics. Tom’s passion is helping people hear the story in their data and to draw sound statistical inferences from that data.
