Duration
6 weeks (2-3 hours per week)
About this course
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 that a 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 a 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.
Length: 6 weeks
Effort: 2-3 hours per week
Level: Intermediate
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
Course Syllabus
- The Research Process
- Planning for Analysis
- Research Claims
- Measurement
- Correlational and Experimental Design
Note: This syllabus is preliminary and subject to change.
This is a version-specific reference outline. Confirm the current syllabus and delivery arrangements with EIEIO.