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.
To complete this course successfully, you should have:
- A basic knowledge of math
- Some programming experience – R is preferred.
- A willingness to learn through self-paced study.
What you will learn
After completing this course, you will be familiar with the following concepts and techniques:
- Data analysis and inference
- Data science research design
- Experimental data analysis and modeling
- The Research Process
- Planning for Analysis
- Research Claims
- Correlational and Experimental Design
Note: This syllabus is preliminary and subject to change.