Sometimes science fiction becomes science fact.
Maybe this evitable meeting of minds was bound to happen. In the movie “Bill & Ted’s Excellent Adventure,” Bill and Ted bring back several important historical figures (e.g., Napoleon, Abraham Lincoln, Joan of Arc, Sigmund Freud) as part of their high school history project. One notable historical figure that Bill and Ted brought back was Socrates.
That got me thinking…what would happen if Bill’s (Schmarzo) most excellent adventure – the “Art of Thinking Like a Data Scientist” adventure – would meet up with the Socratic Method. What would happen…
What is the Socratic Method
The Socratic method was created by Socrates, an ancient Greek philosopher, to encourage critical thinking by asking questions. Its purpose is to challenge assumptions, uncover inconsistencies, and arrive at a deeper understanding of a topic through collaboration. It helps individuals or groups to challenge their ideas and reach their own conclusions by revealing contradictions in their beliefs.
As we rely increasingly on AI (e.g., autonomous driving, GPS navigation, content distribution, facial recognization), the Socratic Method helps to strengthen critical human skills. These skills include:
- Critical thinking: Individuals are forced to think critically and examine their beliefs and assumptions by engaging in a dialogue that challenges preconceived notions.
- Active listening: By carefully listening to opposing viewpoints and asking probing questions, individuals can gain a deeper understanding of complex issues, engage in more productive discussions, and build deeper relationships.
- Effective questioning: This method promotes asking insightful and thought-provoking questions, which is especially important when creating ChatGPT and Microsoft Bing requests or prompts that deliver relevant responses.
- Problem-solving: Learning to approach problems systematically and explore different angles of inquiry by breaking down or decomposing complex issues into smaller components and examining them from multiple perspectives.
- Self-reflection: By constantly questioning one’s beliefs and being open to alternative viewpoints, individuals can enhance their self-awareness, recognize biases, and foster continuous learning and personal growth.
- Communication skills: By participating in meaningful dialogues and defending arguments with sound reasoning, individuals can refine their communication abilities, which are essential for effectively conveying complex concepts and collaborating.
This is accomplished by mastering the six types of Socratic questions (Figure 1).
Figure 1: Six Types of Socratic Questions
Thinking Like a Data Scientist (TLADS) Methodology
The TLADS methodology provides a specialized framework for value-based problem-solving and data-driven decision-making, incorporating business context, stakeholder alignment, and the practical application of data science techniques. It provides a structured process that maximizes the value and relevance of the analysis, making it particularly beneficial in the context of data science projects within organizations (Figure 2).
Figure 2: The Art of Thinking Like a Data Scientist
In particular, the TLADS methodology focuses on the following:
- Value-driven. Provides a framework with design templates and exercises for understanding how organizations create value, identifying the desired outcomes from those value-creation processes, and brainstorming the KPIs and metrics against which value-creation effectiveness will be measured across a diverse range of internal and external stakeholders.
- Structured problem-solving. Provides a structured methodology for problem-solving using data science techniques. It offers a systematic approach that guides practitioners through defining the problem, gathering relevant data, analyzing data, and communicating results. This structured process helps ensure that critical steps are covered and increases the efficiency and effectiveness of the analysis.
- Data-driven decision-making. Provides a step-by-step process for gathering, preparing, and exploring data to uncover insights (predicted behavioral and performance propensities) and test hypotheses. By exploiting the unique economics of data, the methodology enables practitioners to make scalable, repeatable evidence-based decisions, leading to improved business and operational outcomes.
- Business context and stakeholder alignment. Emphasizes understanding the business context and engaging stakeholders throughout the process. It recognizes the importance of considering the objectives and perspectives of the organization, ensuring that the data analysis aligns with the broader business goals. This focus on stakeholder alignment helps ensure the analysis outcomes are relevant and meaningful to the business.
- Practical application of data science. Bridges the gap between technical data science skills and real-world business problems. It provides practical guidance on applying data science techniques in a business context, helping practitioners translate their skills into actionable insights. This practical orientation makes it easier for organizations to implement the methodology and derive value from their data assets.
- Iterative and adaptive approach. Exploits the iterative nature of data analysis and decision-making. It encourages feedback and refinement throughout the process, allowing continuous learning and improvement. This adaptive approach helps practitioners address complexities and challenges that may arise during the analysis and ensures that the methodology remains flexible and responsive to evolving needs.
Blending Socratic Method with Thinking Like a Data Scientist Methodology
To enhance the prompt engineering for AI-powered chatbots such as ChatGPT and Microsoft Bing, it is essential to merge the questioning skills of the Socratic Method with the “Thinking Like a Data Scientist” framework. This fusion guarantees the provision of pertinent details.
Prompt engineering involves creating natural language inputs that guide large language models (LLMs) to provide relevant responses. By designing prompts carefully, users can improve the quality of insights delivered by LLMs.
By integrating the Socratic method with the TLADS methodology, prompt engineering can be improved as it promotes critical thinking, enables a deeper understanding of the targeted problem, and leads to robust and informed decision-making. The following steps can be taken to achieve this:
- Questioning assumptions: The Socratic method is centered around questioning assumptions and exploring underlying beliefs. By incorporating this approach into the TLADS methodology, data scientists can challenge assumptions made during the problem definition and hypothesis development stages to uncover hidden biases, consider alternative perspectives, and refine the problem statement for a more comprehensive analysis.
- Guiding inquiry: The Socratic method encourages a guided inquiry process, where thoughtful and targeted questions prompt deeper exploration of the subject matter. This can be applied during the data exploration and feature engineering phases, where data scientists collaborate with business subject matter experts to ask probing questions to uncover patterns, anomalies, or potential confounding factors.
- Testing and validating: The Socratic method encourages rigorous testing and validation of ideas. By combining this approach with some simple Design Thinking techniques in the hypothesis testing stage, data scientists can challenge their assumptions and design more thoughtful experiments that rigorously test the validity of the hypothesis.
- Facilitating discussions: The Socratic method emphasizes asking thought-provoking questions and encouraging active subject matter expert engagement. As a result, data scientists can better understand the analysis findings, elicit different perspectives, and collectively arrive at a more informed and relevant approach.
- Iterative learning: The Socratic method encourages an iterative learning process, where knowledge and understanding evolve through ongoing questioning and exploration. By embracing this mindset, data scientists can continuously reflect, learn, and refine their approach. They can adapt their hypotheses, refine data collection methods, or explore new analytic techniques based on the insights gained from the Socratic questioning.
The Dean Meets Socrates Summary
Integrating the Socratic method with my Thinking Like a Data Scientist methodology adds a layer of critical thinking, promotes deeper analysis, and enhances the overall rigor of the decision-making process. It encourages a more thoughtful and reflective approach, leading to more robust and informed data-driven decisions. It enables everyone to solve complex problems, generate insights, and create value from data. It fosters a culture of curiosity, collaboration, and innovation conducive to rational, data-driven decision-making.
As AI technology such as ChatGPT, Google Bard, and Microsoft Bing continue to advance, asking insightful and thought-provoking questions will become increasingly crucial for success in prompt engineering.
It’s an intriguing thought. Could technologies such as ChatGPT push us to be more human? Perhaps we’ll start asking more precise, insightful questions that align with our goals and learning objectives. It’s almost ironic that technology might force us to be more human.
Yea, now that’s a most excellent adventure!