Practical Product Analytics: Solve Daily Tasks with Python, SQL, and LLMs

Navigating the daily responsibilities of a product analyst can be challenging without a clear workflow. To succeed in modern product teams, you need to know how to extract data, analyze user behavior, and leverage artificial intelligence to speed up your insights. This text-based course guides you through the exact scenarios product analysts face every day, helping you transition from understanding basic product metrics to executing complex data workflows using SQL, Python, and Large Language Models (LLMs) to automate and elevate your analysis. What you'll learn: Understand fundamental product metrics like retention, churn, LTV, and conversion funnels; Write clean SQL queries to extract and aggregate user behavior data from relational databases; Analyze dataset patterns using Python dataframes and clean code principles; Apply LLMs and prompt engineering to accelerate SQL writing, debug Python scripts, and summarize qualitative feedback; Design basic A/B testing frameworks and interpret statistical results with confidence; Solve typical daily tasks of a product analyst through realistic written case studies and step-by-step code walkthroughs. You will begin by exploring core product concepts and metrics before moving on to practical SQL querying and Python data manipulation. Finally, you will learn how to integrate LLMs into your daily routine to work faster and smarter. This course is designed for aspiring product analysts, product managers, and beginners looking to build a modern, AI-assisted data skillset without any prior technical experience. Start reading today to build the modern technical toolkit required for a successful career in product analytics.