LLMs for Predictive Analysis Training in Astana, Kazakhstan

This course introduces how Large Language Models (LLMs) can enhance and support predictive analytics tasks. It blends traditional predictive modeling concepts with emerging generative AI capabilities, showing how LLMs can assist in hypothesis generation, model interpretation, code generation, and narrative explanation of results. The course emphasizes practical use cases and tools for working analysts and decision-makers.

LLMs IN DATA PREPARATION AND MODELING SUPPORT

Introduction to LLMs in Predictive Workflows

  Role of LLMs in the data science lifecycle

  Complementing vs. replacing traditional ML tools

  Tools overview: ChatGPT, Claude, Hugging Face, Jupyter + Copilot

EDA and Feature Engineering with LLMs

  Using LLMs to guide exploratory data analysis

  Prompting for feature ideas and correlation detection

  Automating data cleaning with prompt-driven code

Model Building and Code Generation

  Generating code for regression, classification, or clustering models

  Prompting for hyperparameter suggestions and tuning

  Explaining model choices and metrics using LLMs

MODEL EXPLANATION, SIMULATION, AND AUTOMATION

Interpreting Model Results with LLMs

  Explaining predictions and coefficients in plain language

  Using LLMs to describe model accuracy, overfitting, and bias

  Summarizing performance across multiple models

Scenario Simulation and Predictive Reasoning

  Using LLMs to generate what-if scenarios

  Prompting for domain-specific insights and assumptions

  Limitations of LLMs in causal inference and uncertainty

Narrative Reporting and Presentation Automation

  Auto-generating executive summaries and visual insights

  Integrating LLM output with BI tools, Notebooks, or slide decks

  Validating outputs and combining human + AI analysis

Final Labs and Wrap-Up

  Discussion on production usage, ethics, and next learning steps