About this courseSkip 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.
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.
What you'll learnSkip 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
- The Research Process
- Planning for Analysis
- Research Claims
- Correlational and Experimental Design
Note: This syllabus is preliminary and subject to change.
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Frequently asked questions
Q: The prerequisites include R Programming?
A: R is used extensively in the machine learning and artificial intelligence fields. The practical elements of this course involve writing code in R. For the most part, you'll be given the code you need to complete the exercises; but a basic knowledge of R syntax will improve your understanding of what's going on in the labs and demonstrations. Consider taking course DAT204x: Introduction to R for Data Science before taking this class.
Q: What hardware and software do I need to complete this class?
A: You will need a computer running Windows, Mac OSX, or Linux and a web browser. Optionally, you can install R - but you will be able to complete the labs using a free online environment, so this is not required.
Q: Will I need a Microsoft Azure subscription to complete this class?
A: No. You will be able to complete the labs using a local R installation or the Microsoft Azure Notebooks service, which is a free service that does not require an Azure subscription.