1. Introduction and Overview of AI Data Analysis [0]

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  1. The speaker, a former data scientist at Meta, took 11 courses to compile the cliff notes version of data analysis with AI. [0]
  2. The video outline covers: [10]
    1. When AI is useful for data analysis.
    2. The DIG framework for approaching analysis.
    3. Various examples of AI data analysis.
    4. Extending analysis into dashboards or AI applications.
  3. While many examples use ChatGPT, other tools like Gemini and Claude work similarly, sometimes even better. [14]

2. When to Use AI for Data Analysis: The ACHIEVE Framework [20]

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  1. The ACHIEVE acronym comes from Dr. Jules White's course, ChatGPT Advanced Data Analysis, at Vanderbilt University. [22]
  2. ACHIEVE identifies five areas where AI is useful in data analysis: [24]
    1. Aiding human coordination.
    2. Cutting out tedious tasks.
    3. Help provide a safety net for humans.
    4. Inspire better productivity and problem-solving.
    5. Enable great ideas to scale faster.

2.1. Aiding Human Coordination and Cutting Out Tedious Tasks [26]

  1. Aiding human coordination helps people work better together, reducing messiness and miscommunications. [26]
    1. Example: Upload a meeting transcript and ask the AI to act as an assistant. [29]
    2. The AI can provide a summary of key points of discussion to bring clarity for humans. [31]
  2. Cutting out tedious tasks suggests letting AI handle repetitive and boring activities. [33]
    1. Example: Analyzing workshop sign-up data that includes name, occupation, department, and interests. [35]
    2. Ask the AI to describe the data in the file as a best practice. [38]
    3. The AI provides file details (e.g., CSV, columns: timestamp, name, email). [40]
    4. If department names have variations, ask the AI to list them and perform intelligent grouping. [44]
    5. Finally, ask it to create a bar chart showing total registrations per department. [46]
    6. AI handles this mundane data cleaning and visualization much quicker than humans. [47]

2.2. Safety Net, Inspiration, and Scaling Great Ideas [48]

  1. Help provide a safety net for humans addresses human mistakes, despite AI's known hallucinations. [48]
    1. People make frequent and sometimes dumb mistakes. [51]
    2. Example: Seeking reimbursement for a business trip and needing to generate an invoice. [55]
    3. Upload the invoice and the business expense policy to the AI. [59]
    4. Ask the AI to read the policy and verify if the receipt complies with it. [59]
  2. Inspire better problem-solving and creativity challenges the notion that AI reduces human creativity. [62]
    1. Success depends on asking the right questions. [65]
    2. Example: Upload an important PowerPoint presentation to the AI. [66]
    3. Ask the AI to act as a skeptic and find flaws in assumptions and assertions. [67]
    4. Request the AI to generate 10 hard questions for preparation. [67]
    5. This forces a broader perspective, helping people think beyond their rigid ways. [69]
  3. Enable great ideas to scale faster focuses on automating complex tasks for large groups. [72]
    1. Revisit the workshop example with diverse participants. [74]
    2. After data analysis, the goal is to create a custom cheat sheet for each attendee. [75]
    3. Ask the AI to map each attendee's domain of interest. [76]
    4. Generate a new column called ideas containing a corresponding specific prompt for their cheat sheet. [76]
    5. This allows sending a personalized cheat sheet via email after the workshop. [78]
    6. Scaling this task manually would be extremely difficult with more than 10 attendees. [79]

3. Approaching Data Analysis: The DIG Framework [99]

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  1. The DIG framework comes from the course ChatBT Plus Excel, master data, make decisions, and tell stories. [99]
  2. DIG stands for Description, Introspection, and Goal setting. [100]
  3. This is similar to EDA (Exploratory Data Analysis) but specifically tailored for using AI. [101]

3.1. Step 1: Description (Data Exploration and Validation) [103]

  1. Describe is the first step where the user and AI explore the data together. [103]
  2. This familiarity helps both parties notice issues and reduces hallucinations later. [104]
  3. After uploading data (e.g., spreadsheet), ask the AI to: [106]
    1. List the columns in the spreadsheet.
    2. Show a sample of the data in each column.
  4. Example: Data about roles and salaries shows columns like max salary, min salary, and pay period. [108]
    1. Noticing NaN (Not Available) values in salary columns is important. [109]
    2. Missing or incorrectly formatted data is a primary cause of hallucinations. [110]
  5. First, confirm if the AI is parsing the data correctly. [111]
    1. If parsing is incorrect, explicitly tell the AI how it should be done. [112]
    2. If data is genuinely missing, simply make a note. [113]
  6. Request the AI to take a few more random samples of the data. [116]
  7. The goal is to verify that the data is parsed correctly and the AI understands each column. [118]
    1. You can ask the AI what it thinks each column represents (e.g., job ID is a unique identifier). [120]
  8. Validate the AI's understanding, treating it like a very competent, but junior developer. [121]
    1. Wrong understanding will lead to incorrect analysis. [125]

3.2. Step 2: Introspection (Pattern Finding and Skepticism) [126]

  1. Introspection is when the AI starts looking for patterns and relationships in the described data. [127]
  2. This is another crucial opportunity to catch the AI's misconceptions; maintain skepticism. [129]
  3. Ask the AI: "Tell me some interesting questions that could be answered with this dataset, and why." [132]
  4. Example AI question: Is there a relationship between compensation type (e.g., base salary) and salary range variability? [132]
  5. Example Red Flag: The AI asks about patterns in salary data for different currencies if they exist. [134]
    1. If you know all currencies are USD, inform the AI to prevent future irrelevant analyses. [136]
  6. If the AI asks unanswerable questions, ask it to generate new, answerable questions. [139]
  7. Do not skip these initial steps to rush into making graphs and charts. [141]
    1. Messing up here will propagate throughout the entire analysis. [143]
  8. This detailed exploratory analysis is essential even for human data scientists. [145]

3.3. Step 3: Goal Setting (Defining the Output) [148]

  1. Goal setting requires clearly communicating what the user is trying to achieve. [149]
    1. A vague prompt like "Analyze this data" is ineffective. [150]
  2. Example goal: "My goal is to answer a couple of these questions... and turn them into a really exciting, interesting report to post on LinkedIn." [152]
  3. Context helps the AI tailor the analysis and provide ideas (e.g., LinkedIn specific content). [153]
  4. This contrasts with setting a goal for a serious report for a boss. [155]
  5. Goal setting is part of good prompt engineering practices. [156]
  6. The DIG framework is a great foundation for any AI data analysis. [161]

4. Advanced AI-Specific Data Analysis Techniques [162]

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  1. While the DIG framework is standard for analysis in Excel/Python/SQL, AI enables unique, difficult-to-achieve tasks using traditional tools. [162]

4.1. Intelligent Filtering and Traceability [164]

  1. Intelligent Filtering (GenAI Specific): GenAI can filter data using highly abstract criteria. [166]
    1. Example: Job hunting for a job between $50k-$80k, on the East Coast, and "works with wood." [164]
    2. If the data doesn't have explicit columns for "East Coast" or "works with wood," GenAI can still intelligently filter. [166]
  2. Traceability and Replication: AI can create documentation for reproducibility. [169]
    1. Traditional analysis often gets stuck in notebooks, making reproduction difficult. [170]
    2. Ask AI to create a traceability document allowing others to perform the same analysis. [171]
    3. The document should cover: data used, how analysis was performed, and threats to validity. [172]
    4. This can be stored as a readme.md or a Word document. [174]
    5. For each analysis, ask AI to write a single Python script that performs the full analysis and produces results/visualizations. [177]

4.2. Practical Examples and Multimedia Manipulation [182]

  1. Simple Document Analysis: Upload small documents, perform DIG, and analyze/transform the information. [183]
  2. Structured Data Analysis (Inventory): Using a CSV file of inventory over months. [185]
    1. Ask for filtering, trends over time, and identifying popular/less popular items. [186]
    2. Request a predictive model to optimize stocking for the next few months. [187]
    3. AI can create visualizations: static (bar chart ranking) or time series graphs. [188]
  3. Interactive Dashboards: Claude is often better than other models for creating interactive dashboards and writing the necessary code. [191]
  4. Different Media Forms (Video/Images): AI can manipulate multimedia data. [194]
    1. Example: Extract 10 music frames from a video, evenly spaced one second apart. [195]
    2. Manipulate images: resize to 300 pixels, convert to grayscale, increase contrast by 30%. [196]
    3. Combine images into an animated GIF flipping at one-second intervals. [197]
    4. Catalog all images into a CSV file listing the image name, source movie, and operations applied. [198]

4.3. Automating with Zip Files and Turning Conversations into Software [201]

  1. Automating with Zip Files: Zip files are useful as they maintain folder hierarchy. [201]
    1. You can zip multiple Excel files, ask the AI to combine and analyze them, and send back the result. [203]
  2. Organizing Files: Ask AI to help organize files within a zip. [206]
    1. Step 1: Open and read files to create a summary. [206]
    2. Step 2: Propose a better folder structure. [207]
    3. Step 3: Propose new file names using only A-Z and 0-9, keeping extensions. [208]
    4. Step 4: Show the proposed structure and names. [209]
    5. Finally, ask the AI to zip everything back up and send it. [211]
  3. Turning Conversations into Software Programs (Advanced): Automating a sequence of analyses. [212]
    1. Example: A sequence involving extracting movie frames, photo manipulation, description generation, and compiling a CSV. [215]
    2. Ask AI to convert this process into a Python program. [216]
    3. Specify that the program should accept document paths as command line arguments. [217]
    4. Request the AI to zip up the executable program for download. [218]
    5. The AI can write a script performing all steps, creating an automatable sequence. [219]

5. Extending AI Data Analysis to Applications [229]

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  1. Analysis using AI does not need to be the final step. [229]
  2. Analysis can be used to generate: emails, social posts, reports, PowerPoint slides, software programs, and dashboards. [231]
  3. Building Full Applications: Users can build complete applications based on analyses without needing to know how to code (using pipe coding). [233]
    1. Example: An application that analyzes real-time traffic data to give alerts or generate incident reports. [236]
    2. Example: An application that takes videos and blurs out people's faces or identifications. [239]
  4. Example: An investment research AI agent built in a boot camp. [240]
    1. This agent maintains a database of investment information. [240]
    2. It uses an interface where users ask specific questions. [240]
    3. The agent analyzes the data to generate responses, conversations, and reports. [240]
  5. The ability to analyze and harness data using AI opens up many possibilities. [242]