Master Data Analysis with ChatGPT (in just 12 minutes)

How to Analyze Data Using ChatGPT

Introduction to Data Analysis Framework

• The speaker emphasizes that everyone works with data but lacks formal training in structured data analysis.

• They introduce a three-step framework called DIG (Description, Introspection, Goal setting) that allows users to leverage ChatGPT as a personal data analyst without needing technical skills.

Understanding the DIG Framework

• The DIG framework enables quick understanding of unfamiliar datasets and helps extract insights that non-data analysts might overlook.

• A visualization is presented showing how inputting prompts into ChatGPT increases understanding of the dataset over time.

Case Study Setup

• The speaker uses a free Apple TV Plus dataset for demonstration, which includes popular shows and movies like "Avatar" and "The Godfather."

• Although the industry standard is Exploratory Data Analysis (EDA), they prefer using DIG for its simplicity and memorability.

Step 1: Description

Initial Prompts for Understanding Data

• The first prompt instructs ChatGPT to list all columns in the spreadsheet and provide sample data from each column, facilitating an overview of the dataset.

• Notable observations include potential issues such as incorrect release years or unclear identifiers (e.g., IMDb ID).

Further Exploration of Samples

• A second prompt requests five additional random samples from each column to ensure comprehensive understanding and identify any outliers.

• This step reveals various types of content (TV shows vs. movies), genre counts, and availability across countries.

Quality Check on Data

• The third prompt runs a quality check on each column, looking for missing values or unexpected formats.

• Results indicate significant missing values in certain columns (e.g., 99.7% missing in available countries), suggesting limitations for geographical analysis.

Conclusion of Step 1 Insights

• While ChatGPT aids significantly in analysis, it does not replace human judgment; follow-up questions are essential for clarity.

Introspection and Data Analysis with ChatGPT

Understanding the Purpose of Introspection

• The introspection step involves using ChatGPT to brainstorm questions that can be answered with a given data set, revealing its understanding of the data.

• Good questions indicate that ChatGPT comprehends the data; poor questions suggest misunderstandings that need addressing before proceeding.

Key Questions for Analysis

• Example question: "How has Apple TV's yearly output grown since launch?" This could indicate market share growth.

• Another important question: "What share of releases are movies versus series each year?" This helps analyze viewer behavior trends.

• A third question: "Which genres dominate the catalog and how have they shifted over time?" This insight is crucial for content investment decisions.

Assessing Data Sufficiency

• For each key question, it's essential to determine if the current data is sufficient. Minor cleanup may be needed for some analyses.

• Confirmations from ChatGPT about data sufficiency help ensure readiness for deeper analysis.

Identifying Data Gaps

• Prompting ChatGPT to identify unanswerable questions due to missing information reveals gaps in the dataset, such as lacking viewing metrics or production costs.

• An example of a gap: "What's the most watched genre?" cannot be answered without viewership metrics.

Merging Datasets for Enhanced Insights

• A hypothetical scenario introduces a second dataset containing IMDb IDs, total viewership, and production costs to enrich analysis capabilities.

• After merging datasets using IMDb IDs, new insights can be derived, such as calculating cost per viewer ROI by genre.

Goal Setting in Data Analysis

Importance of Clear Goals

• Setting clear goals is critical; analyzing data without defined objectives can lead to irrelevant results despite technical accuracy.

Defining Specific Objectives

• An example prompt emphasizes specifying goals clearly—e.g., understanding what content Apple TV should invest in next—to guide analysis focus effectively.

Prioritizing Aspects Based on Roles

• Depending on team roles (content vs. finance), different aspects of data become priorities—viewership demand for content teams versus unit economics for finance teams.

Roadmap Development

• A structured roadmap emerges from goal setting, including steps like cleaning data and building a genre scorecard to rank opportunities based on trend velocity.

Insight Generation from Analysis

What Are the Key Takeaways from This Session?

Insights on Overcoming Challenges

• The speaker humorously addresses concerns about criticism from managers and peers, emphasizing a light-hearted approach to workplace dynamics.

• Two main points are highlighted: the importance of the DIG framework and its accessibility for untrained individuals, making it a practical tool for immediate use.

Learning Opportunities

• The full Coursera course offers additional insights beyond today's essentials, including strategies to mitigate hallucinations and debug data errors.

• A special offer is mentioned for viewers interested in enhancing their data skills through Coursera, providing a 40% discount for three months of Coursera Plus.