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ChatGPT for Advanced Data Analysis

Published by O'Reilly Media, Inc.

Advanced content levelAdvanced

Unlocking insights from complex modern data with generative AI

Course outcomes

  • Parse and analyze semistructured data (e.g., JSON, API responses) with ChatGPT
  • Conduct NLP tasks such as sentiment analysis and text summarization on unstructured data
  • Extract patterns and generate forecasts from behavioral and time-series datasets
  • Understand ethical and practical considerations when using ChatGPT for advanced analytics

Course description

Join expert Deanne Larson to explore ChatGPT’s capabilities for analyzing semistructured (e.g., JSON, APIs) and unstructured (e.g., text, audio) data, as well as behavioral and time-series datasets. You’ll engage in hands-on exercises to parse and analyze complex data, extract actionable insights, and generate forecasts. You’ll also explore future-oriented topics, such as ChatGPT’s potential for handling emerging data types like blockchain and streaming data, while addressing its limitations and ethical considerations. You’ll come away having gained practical, AI-driven techniques to streamline workflows, optimize decision-making, and address modern data challenges beyond traditional tools like SQL, Python, and Excel.

What you’ll learn and how you can apply it

  • Gain proficiency in analyzing diverse modern data types (structured, semistructured, unstructured, etc.) using ChatGPT
  • Perform exploratory data analysis, predictive modeling, and hypothesis testing using AI
  • Integrate ChatGPT into workflows for text, geospatial, and time-series data to uncover insights
  • Develop ethical considerations and best practices for applying AI-driven methods responsibly

This live event is for you because...

  • You’re a data analyst looking to enhance analytical capabilities using AI tools like ChatGPT.
  • You work with diverse data types and want to leverage AI for faster, more accurate analysis.
  • You want to become proficient in applying large language models to real-world data challenges.

Prerequisites

  • Basic proficiency in Python programming
  • Experience with data analysis concepts and workflows

Recommended preparation:

  • A Python environment with Anaconda installed
  • Install required libraries (openai, pandas, Matplotlib, seaborn, NLTK, TextBlob, statsmodels)
  • Obtain an OpenAI API key

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Introduction and foundations (30 minutes)

  • Presentation: Overview of the course, objectives, and expectations; ChatGPT’s role in data analytics; advanced data types (semistructured, unstructured, and time-series)
  • Group discussion: Your prior experience with ChatGPT and analytics workflows
  • Q&A

Semistructured and unstructured data analysis (60 minutes)

  • Presentation: ChatGPT applications for semistructured data (e.g., JSON, API responses); NLP techniques for unstructured data (e.g., text summarization, sentiment analysis)
  • Hands-on exercises: Extract and analyze data from JSON files or API outputs; conduct sentiment analysis or summarize free-text documents
  • Q&A

Behavioral and time-series data analysis (60 minutes)

  • Presentation: Identifying patterns in behavioral data (e.g., customer activity, clickstreams); forecasting trends in time-series data using ChatGPT
  • Hands-on exercises: Generate actionable insights from a behavioral dataset; perform trend forecasting using time-series data
  • Q&A

Ethics and limitations (20 minutes)

  • Presentation: Ethical considerations and limitations of ChatGPT in data analytics; future applications (e.g., blockchain, streaming data)

Wrap-up and Q&A (10 minutes)

Your Instructor

  • Deanne Larson, PhD

    Deanne Larson, PhD, is an author, data science practitioner, and faculty member at Purdue Global. Her research has focused on enterprise data strategy, agile analytics, and data science best practices. She holds Project Management Professional (PMP), Project Management Agile Certified Practitioner (PMI-ACP), Certified Business Intelligence Professional (CBIP), and Six Sigma certifications. Deanne attended AT&T Executive Training at Harvard Business School, focusing on IT leadership; Stanford University, focusing on data science; and New York University, focusing on business analytics. She has presented at multiple conferences including TDWI, TDWI Europe, PMI, and others. She has consulted for several Fortune 500 companies and has authored multiple research articles on data science methodology and best practices.

Skill covered

GPT