Certification Course
Exclusive track on AI in data analytics. Learn Prompt Engineering, AI-driven analysis, and predictive insights for next-generation decision-making.
AI-DATA PROGRAM
₦100k
FEATURES:
Introduction to Prompt Engineering
AI for Data Analysis
AI for Predictive Analysis
An exclusive program that explores the intersection of data analytics and artificial intelligence. Participants will gain hands-on experience with Prompt Engineering, AI for Data Analysis, and AI for Predictive Analysis, preparing them to leverage AI-driven tools for next-generation insights and decision-making.
Learning Schema
Week 1: Introduction to Prompt Engineering
Session 1
- Introduction to Generative AI and ChatGPT
- What is Prompt Engineering?
- Types of prompts: instructional, comparative, role-based
- Prompt crafting principles: clarity, constraints, context
Session 2
- Use cases in data tasks: exploratory analysis, cleaning, summarisation
- Role-play prompting: Analyst, Engineer, Statistician
- Exercise: Crafting prompts for data summarisation and cleaning
- Assignment: Draft 5 practical prompts for data work
Week 2: Prompt Engineering for Data Analysis
Session 1
- Structuring prompts for reproducible code
- Naming conventions for datasets, variables, and DataFrames
- Generating modular Python scripts using prompts
- AI-enhanced exploratory data analysis prompts
Session 2
- Hands-on: Prompt-to-code for EDA (using student dataset or sales data)
- Copying and running AI-generated code in Jupyter Notebooks
- Debugging AI-generated code
- Case Study: Analyse dataset using only AI-generated code
Week 3: AI for Supervised Learning (Part 1)
Session 1
- Introduction to Predictive Modelling & Supervised Learning
- Overview of classification vs regression problems
- Prompting for classification models (e.g. Logistic Regression, Decision Trees)
- Splitting data and model training using AI
Week 4: AI for Supervised Learning (Part 2)
Session 1
- Prompting for regression models: Linear Regression, Random Forest Regressor
- Evaluation metrics: RMSE, MAE, R²
- Feature selection and engineering prompts
Week 5: AI for Unsupervised Learning
Week 6: Final Project & Deployment Guidance
Session 1
- Final Project Introduction
- Students pick either a classification, regression, or clustering task
- Guidance on structuring prompts for a full workflow
- Summary of evaluation techniques and prompt hygiene
Session 2
- Project Presentation: Students present prompt-to-code workflow
- Feedback & Improvements
- Intro to deploying Python models using AI-generated Flask code (Pre-recorded)
