Qualitative Methods for Quantitative People (with GenAI)

Vanderbilt University via Coursera

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Details

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

3 hours 32 minutes
Sessions

On-Demand
Level

Beginner
Subtitles

English

Found in

Overview

Class Central Tips

Qualitative Methods for Quantitative People (with GenAI) is a course designed for individuals with a strong quantitative background who want to explore the power of qualitative analysis through the lens of Generative AI (GenAI). This course introduces foundational concepts of qualitative analysis, guiding learners through the process of interpreting concepts, experiences, and nuanced data. It emphasizes how qualitative insights complement quantitative methods, enabling more comprehensive decision-making. Throughout the course, learners will gain hands-on experience in using Large Language Models (LLMs) for tasks such as data-driven decision-making, resource allocation, and qualitative interpretation of complex information. They will explore real-world scenarios, leveraging AI to extract insights from surveys, documents, and discussions, ultimately fostering better collaboration and a deeper understanding of diverse data sets. Key topics include qualitative versus quantitative analysis, leveraging AI for creating living documents, navigating biases, and maintaining human oversight in AI-driven processes. This course is ideal for those looking to enhance their analytical toolkit with qualitative methods supported by cutting-edge AI technologies. By the end of the course, learners will have the ability to integrate qualitative methods into their existing workflows, improving how they approach decision-making and resource management in dynamic environments.

Syllabus

Course Welcome

We explore the integration of qualitative and quantitative analysis with a focus on how large language models (LLMs) can enhance various aspects of decision-making, resource allocation, and data-driven processes. Starting with an understanding of qualitative analysis for those accustomed to quantitative methods, we move through the creation and maintenance of "living documents," the incorporation of qualitative data into decision-making, and the critical role of time management in resource allocation. We conclude with a thought experiment that challenges us to consider different perspectives in decision-making. Throughout, we emphasize the balance between human judgment and AI assistance, ensuring that our strategies are both efficient and informed by diverse insights. By the end of this course, learners will be able to effectively integrate qualitative and quantitative data, utilizing large language models (LLMs) to enhance decision-making and resource allocation while maintaining a balanced approach between human judgment and AI assistance.

Part 1: What is Qualitative Analysis

We explore the intersection of qualitative and quantitative analysis, particularly focusing on how large language models (LLMs) can enhance our ability to engage with qualitative data. We discuss the nuances of qualitative analysis, emphasizing its importance in understanding the 'why' and 'how' behind data. By contrasting it with quantitative methods, we highlight how LLMs provide us with new tools to analyze, interpret, and communicate complex qualitative insights efficiently.

Part 2: Chatting with Your Data

We delve into the concept of "Living Documents," focusing on how we can use large language models (LLMs) to create, interact with, and continually refine our data-driven narratives. We explore how LLMs differ from traditional search engines, emphasizing their ability to predict and generate content rather than simply retrieve information. By guiding us through practical applications such as coding, knowledge curation, and query optimization, we aim to demonstrate how LLMs can enhance our productivity and creativity, transforming static documents into dynamic, evolving resources that adapt to our needs.

Part 3: Qualitative Considerations

We explore how qualitative data, such as survey responses and human-in-the-loop interactions, can be systematically analyzed and synthesized to inform decisions that go beyond numerical insights. By examining case studies and applying practical tools like sentiment analysis and peer review, we aim to develop a nuanced understanding of how to balance quantitative metrics with qualitative feedback to achieve more holistic and effective decision-making outcomes.

Part 4: Time is Our Most Valuable Asset

We explore strategies for efficiently managing team dynamics, particularly in large and dynamic groups, where balancing skills, interests, and efforts is key. By leveraging large language models (LLMs), we can streamline tasks such as tracking progress, reviewing documents, and matching team members with appropriate roles. We also address the challenges of bias in data collection and interpretation, highlighting the need for careful, human oversight to ensure fairness and accuracy in our decision-making processes.

Part 5: A Tale of Three Hats

We examine how different perspectives can influence our decisions in daily activities, business, and life planning. By dissecting how we seek advice and make choices, we aim to understand the biases and influences that shape our decisions. Through interactive examples, we challenge ourselves to consider multiple viewpoints and recognize the complexity involved in even seemingly simple decisions.

Course Wrap-Up

As we conclude this module, it's time to apply what we've learned. The following test and project will challenge your understanding of the key concepts and your ability to integrate them into practical scenarios.