What you’ll learn
- Execute advanced statistical analyses, predictive modelling, and automated charting using LLM code interpreter sandboxes.
- Clean, transform, and join complex multi-file datasets using conversational Natural Language Processing (NLP).
- Validate AI-generated statistical models, regression outputs, and underlying Python code for exact accuracy.
How the course runs
Before the session
Pre-session prep · about 30 minutes
Download and inspect a multi-tab business performance database (sales, churn, marketing metrics).
Live session
Instructor-led · 2.5 hours
2.5-hour advanced analytics lab utilizing native code interpreter environments.
After the session
Practice & reinforcement
Conduct an automated trend and outlier analysis on a quarter-end operational dataset.
Course modules
Code Interpreter Sandbox Architecture
Execution environments, security, and temporary file handling.
Conversational Data Cleaning
Merging mismatched tables, imputing missing variables, and parsing timestamps.
Exploratory Data Analysis (EDA)
Automated generation of correlation matrices, distributions, and trend visuals.
Audit & Verification
Extracting, reviewing, and verifying underlying Python code for analytical accuracy.
What you’ll take away
Deliverable: An Automated Data Analysis Report accompanied by verified Python code logs and executive visual charts.
How your learning is checked: Technical audit comparing AI analytics results against control dataset benchmark calculations.