Generative AI for Data Analysis & Workflow Automation - Online Course

Module 1: Introduction to Generative AI for Data Analysts

• Course Overview: Why AI is essential for data analysts.

• AI Tools Overview: ChatGPT, Gemini, Copilot, Claude, Llama, Microsoft Power Automate, Codex.

• Versions & Access: Desktop and app versions of AI tools.

• Interaction Modes: Upload files, voice commands, images, websites, and screen recordings.

Module 2: Best Practices and Ethics in AI

• AI Ethics & Limitations: Responsible AI usage, data privacy.

• Prompt Engineering & Management: Crafting and refining prompts.

• Staying Current: Keeping up with AI advancements.

Module 3: AI for Coding and Scripting Assistance

• AI for Code Writing: How AI assists in coding and scripting.

• Code Assistance Tools: IDE support (RStudio, VS Code, SQL clients).

• AI as a Scripting Console: Using AI tools for direct coding.

• Python & R for Beginners: Intro to Google Colab & Posit Cloud.

• Advanced Tools: Amazon Textract for data extraction.

Module 4: Supercharging Excel and Google Sheets

• AI-Powered Features: Macros, VBA scripts in Google Sheets and Excel.

• Python Integration: Using Python in Excel.

• Google Colab for Sheets: Advanced features for automation and AI.

Module 5: Automation with AI

• Workflow Optimization: Tools like Zapier and ChatGPT.

• Data Integration: BigQuery + Google Sheets with AI integration.

• AI Agents: Building and using AI-driven agents.

Module 6: AI for Data Sourcing

• Data Extraction: From images, PDFs, and web scraping.

• Sourcing Leads: Finding sales leads online.

• Dummy Datasets: Generating synthetic data.

Module 7: AI for Data Cleaning & Preprocessing

• Data Cleaning Prompts: Identifying missing data and outliers.

• Feature Engineering: Automating feature extraction.

• Text Classification: Example using Google Search Console data.

Module 8: AI for Quick Data Analysis

• Quick Analysis & Summaries: Efficiently analyzing data with AI.

• Pattern Detection: Recognizing trends and patterns.

• Sentiment Analysis: Using AI for text analysis.

Module 9: AI for Hypothesis Testing

• Generating Ideas: A/B testing concepts with AI.

• Hypothesis Testing: Performing statistical tests.

Module 10: AI for Data Visualization and Reporting

• Visualizations: Creating heatmaps, bar charts, and time-series plots using prompts.

• Training models with your own data

Module 11: Advanced AI Applications

• Predictive Models: Building and evaluating predictive analytics models.

• Churn Prediction: Using AI to predict customer churn.

• Customer Segmentation & LTV: Analyzing customer lifetime value.

• Marketing Mix Modeling & Competitive Analysis: Using AI for business insights.

Module 12: AI for Market Research & Competitive Analysis

• Competitive Analysis: Utilizing ChatGPT for competitor insights.

• Market Trend Analysis: Leveraging AI for market research.

Module 13: AI for Personalized Learning

• Customized Learning: Use AI to make you a better analyst

Module 14: AI for Enhancing Commentary and Presentations

• AI for Slide Creation: Using AI tools to generate slides quickly and effectively.

• Improving Commentary: Leveraging AI to write or improve speaker notes and presentation scripts.

• Slide Design with AI: How AI can help in designing visually appealing and professional slides.

• AI-Powered Summaries: Summarizing complex data and insights for reports or presentations.

• Real-Time Assistance: Using AI tools like ChatGPT for on-the-fly commentary during presentations.

Module 15: Resources & staying connected

• Do stay in touch

• Resources: Course prompts library (PDF).

• Future Discounts: Discount code for future purchases (PDF).