English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 23m | 196 MB
AI is changing the world around us, and completely revolutionizing the way we work. Data professionals now have access to tools that provide superpowers to work smarter and faster than ever before. In this course, the Maven Analytics team guides you through the world of deep learning and generative AI and shows you how to leverage SQL and ChatGPT together to work more efficiently and make smarter, data-driven decisions.
Explore the rapid rise of large language models like ChatGPT and Google Gemini, and get up and running with free tools that will take your skills to the next level. You’ll also dive into the art of prompt engineering, review tips and best practices for generating consistent and accurate model outputs, and find out how to address common limitations and pitfalls to be aware of. From there, John Pauler takes you through some of the most powerful and practical ChatGPT use cases for data science and analytics, with instructor-led demos in SQL.
Table of Contents
Introduction
1 The future is now Intro to AI for data analytics
2 Setting expectations
Why AI for Data Analytics
3 Why AI is a game-changer for data analysis
4 AI use cases for data analytics
Intro to AI, LLMs, and ChatGPT
5 The AI landscape
6 Generative AI and large language models
7 The road to ChatGPT
8 Generative AI tools
9 Warning Pitfalls of ChatGPT
10 Accessing ChatGPT and Google Bard
Prompt Engineering
11 Intro to prompt engineering
12 Prompt tip Be clear and specific
13 Prompt tip Provide context
14 Prompt tip Establish roles
15 Prompt tip Set the tone
ChatGPT for SQL
16 Intro to ChatGPT for SQL
17 Explain fundamental SQL concepts
18 Explain a SQL query
19 Add comments to a SQL query
20 Debug and troubleshoot SQL code
21 Create a SQL query from scratch
22 Optimize SQL queries
Conclusion
23 Key takeaways and next steps
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