Data Science Research Methods: Python Edition

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.

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Created by: Tom Carpenter

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Course Description

Data scientists are often trained in the analysis of data. However, the goal of data science is to produce a 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 thata 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. Althougha 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 powerful and flexible language used in everything from Data Science to cutting-edge and scalable Artificial Intelligence solutions, Python has become an essential tool for doing Data Science and Machine Learning. With this edition of Data Science Research Methods, all of the labs are done with Python, while the videos are language-agnostic. If you prefer your Data Science to be done with R, please see Data Science Research Methods: R 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.
The Research Process
Planning for Analysis
Research Claims
Measurement
Correlational and Experimental Design
Note: This syllabus is preliminary and subject to change.

Intermediate Level

Time: 6 Weeks - 2-3 hours per week

Instructor Details

Tom Carpenter

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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.