ChatGPT for Data Analytics: Full Course

Introduction

This tutorial provides a comprehensive guide on utilizing ChatGPT for data analytics, based on the full course presented by Luke Barousse. You'll learn how to set up ChatGPT, work with data sources, perform analysis, visualize data, and leverage advanced features, including plugins and web scraping. This knowledge is invaluable for data analysts looking to enhance their workflows with AI.

Step 1: Setting Up ChatGPT

• Sign Up for ChatGPT
  • Visit OpenAI's ChatGPT page and create an account.
• Access ChatGPT
  • Go to ChatGPT and log in.
• Explore the Course Chatbot
  • Use the course-specific chatbot for data analytics by accessing this link.

Step 2: Introduction to Advanced Data Analysis

• Familiarize with the Kaggle Job Posting Data Set
  • Download the dataset from here.
• Connect to Data Sources
  • Learn to integrate data sources for analysis.
• Data Exploration
  • Utilize ChatGPT to ask questions and get insights about your dataset.

Step 3: Data Cleaning and Visualization

• Clean Your Data
  • Identify and handle missing values or outliers in your dataset.
• Visualize Data
  • Use core visualization techniques:
    • Bar charts, histograms, and scatter plots for numerical data.
    • Pie charts and heat maps for categorical data.
• Follow Visualization Best Practices
  • Refer to the visual vocabulary on Tableau's website.

Step 4: Performing Basic Analysis

• Types of Analytics
  • Understand the four main types of analytics:
    • Descriptive Analysis: What happened?
    • Diagnostic Analysis: Why did it happen?
    • Predictive Analysis: What is likely to happen?
    • Prescriptive Analysis: What should we do?
• Utilize Statistical Visualizations
  • Apply statistical methods to derive insights from the data.

Step 5: Advanced ChatGPT Techniques

• Avoid Hallucinations
  • Be aware of potential inaccuracies in AI responses and verify data.
• Prompting Best Practices
  • Use effective prompting strategies to get better responses from ChatGPT, referencing techniques from experts like Jeff Su.

Step 6: Exploring Plugins and Tools

• Use Plugins for Enhanced Functionality
  • Explore tools such as:
    • Bing for browsing capabilities.
    • Wolfram for computational queries.
    • DALL-E for image generation.

Step 7: Data Collection Strategies

• Access Public Data Sets
  • Utilize resources like:
    • Kaggle (Kaggle Datasets)
    • GitHub (Awesome Public Datasets)
    • Reddit (Datasets Subreddit)
    • Google Dataset Search (Google Datasets)
• Learn Web Scraping Techniques
  • Begin with basic scraping methods on platforms like Glassdoor.
  • For advanced techniques, consider building a web scraper as discussed in this video.

Conclusion

This tutorial covers the essentials of using ChatGPT for data analytics, from initial setup to advanced data manipulation and visualization. By following these steps, you’ll be equipped to integrate AI into your data analysis processes. For further learning, consider exploring the provided resources and practicing your skills with real datasets.