Framework for Data Collection and Analysis
This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products.
Watch on Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: Research Methods
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
The ABCs of reading medical research and review papers these days
Medium · LLM
#1 DevLog Meta-research: I Got Tired of Tab Chaos While Reading Research Papers.
Dev.to AI
How to Set Up a Karpathy-Style Wiki for Your Research Field
Medium · AI
The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
ArXiv cs.AI
🎓
Tutor Explanation
DeepCamp AI