AI Tools for Data Analysis: From Chatbots to Coworkers - A Short Course

Using natural language and AI tools, you will learn how to:

• • Analyze common data formats such as CSV, Excel, PDF, JSON, and text corpora.
  • Clean and transform data.
  • Generate descriptive statistics and summary reports.
  • Create static and interactive visualizations.
  • Run common statistical analyses and models.
  • Build interactive dashboards.
  • Automate recurring tasks through reusable skills and workflows.
  • Brief introduction to LLMs for qualitative coding, classification, and extraction.

Rather than teaching a single tool or interface, the emphasis will be on developing a more durable set of capabilities—how to work effectively with AI-powered analytical environments, coding agents, and workflow-based systems as these technologies continue to evolve.

You will need access to ChatGPT and Claude for this course. A ChatGPT Plus (pricing here) and Claude Pro (pricing here) subscription is recommended, as the free versions offer more limited functionality for data analysis.

ChatGPT Plus has a message limit, so please avoid heavy ChatGPT usage before each session.

The course is designed for both beginning and experienced analysts. It emphasizes practical, hands-on learning and focuses not only on what AI tools can do, but also on how to verify outputs, reduce errors, and decide when human judgment is still essential.

1. Foundations of LLMs and agentic AI

• • Short introduction to LLMs
  • Current capabilities of AI models
  • Benchmarks and pace of development
  • Comparison of leading LLMs
  • From conversational chatbots to agentic AI coworkers (Codex, Cowork, and Code)

2. AI for data analysis

• • Introduction to ChatGPT, Claude, and related AI tools for data analysis
  • Working with popular data formats (CSV, Excel, PDF, JSON, text)
  • Python libraries and execution environment
  • Comparison with traditional statistical software
  • Using AI not just to answer questions, but to take action

3. Limitations and best practices

• • AI hallucinations
  • Verification strategies
  • Privacy and security considerations

4. Working with data and workflows

• • Importing different file formats
  • Handling missing values and outliers
  • Data cleaning and preprocessing
  • Creating summary tables and reports
  • Building reusable prompts, skills, and repeatable analytical workflows

5. Statistical analysis, visualization, and dashboards

• • Descriptive statistics
    • Means, medians, standard deviations
    • Frequency distributions
    • Group summaries
  • Data visualization
    • Histograms and density plots
    • Box plots and violin plots
    • Scatter plots and line graphs
    • Heat maps and correlation matrices
    • Interactive tables and charts
  • Advanced techniques
    • Linear regression analysis
    • Time series analysis
    • Causal methods (e.g., event studies)
    • Brief intro to qualitative data analysis with LLMs
    • Other methods (e.g., model uncertainty)
  • Building dashboards and presentation-ready outputs

6. Real-world applications

• • Survey data analysis
  • Price prediction models
  • Cross-country comparisons
  • Time series trends
  • Event analysis
  • Working with qualitative and unstructured text data

7. Recent developments

• • Because AI tools change rapidly, this section will cover recent developments in the field.

“The diversity in applications for using ChatGPT for data analysis, especially the creation of interactive dashboards and HTML to host on a website, were invaluable.”
   Christine Davis, The National Center for Construction Education and Research

“I have been using ChatGPT every day since 2023 for research, teaching, and even creative writing, though primarily to code advanced statistical models. Despite this experience, I learned so much new information about what AI can do and its potential.”
  Jane C. Daquin, Sam Houston State University 

“I think Boris explained all the topics very well. He knows a lot and was really interactive. Data analysis and AI are very, very important topics that really help our academic career.”
  Laura Fabregat, Universitat Rovira I Virgili

“This course was very relevant and very cutting edge. We learned about developments that happened in last 3 weeks.”
  Navendu Shekhar, American Association of Retired Persons