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edX Data Science Research Methods: R Edition

Microsoft via edX

  • Overview
  1. edX
    Platform:
    edX
    Provider:
    Microsoft
    Length:
    6 weeks
    Effort:
    2-3 hours/week
    Language:
    English
    Credentials:
    Paid Certificate Available
    Part of:
    Microsoft Professional Program Certificate in Data Science
    Overview
    This course is part of the Microsoft Professional Program Certificate in Data Science.

    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.

    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
    Syllabus
    • The Research Process
    • Planning for Analysis
    • Research Claims
    • Measurement
    • Correlational and Experimental Design
    Note: This syllabus is preliminary and subject to change.

    Taught by
    Ben Olsen and Tom Carpenter

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