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.
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Expérience recommandée
Ce que vous apprendrez
Differentiate and apply qualitative and quantitative analysis in decision-making.
Use LLMs to enhance qualitative analysis, streamline data extraction, and compare with traditional search engines.
Develop multi-modal communication and assess the impact of context on decision-making.
Compétences que vous acquerrez
- Catégorie : Large Language Models
- Catégorie : Data Visualization
- Catégorie : Decision making
- Catégorie : Project Management
- Catégorie : Decision Making
- Catégorie : Qualitative Analysis
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septembre 2024
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Il y a 7 modules dans ce cours
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.
Inclus
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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.
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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.
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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.
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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.
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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.
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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.
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Instructeur
Offert par
Recommandé si vous êtes intéressé(e) par Data Analysis
University of California, Davis
University of Colorado System
Coursera Project Network
Coursera Instructor Network
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