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Data Science Research Methods: R Edition

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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.

What you'll 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


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.


The Research Process
The Research Process
The Psychology of Providing Data
Knowledge Check

Planning for Analysis
Planning for Analysis
Power and Sample Size Planning
Research Practices
Knowledge Check

Research Claims
Frequency Claims
Association Claims
Causal Claims
Knowledge Check

Survey Design and Measurement
Reliability and Validity
Knowledge Check

Correlational and Experimental Design
Bivariate and Multivariate Designs
Between and Within Groups Experimental Designs
Factorial Designs
Knowledge Check

Final Exam