Module 1: Data Detectives

Investigate Datawith Python

Become a real data detective. Students use Python, Pandas, and NumPy to clean and analyze real-world datasets, then communicate their findings with crisp Matplotlib visualizations.

3 modules · 12 lessonsReal-world data projects

Final Project

Olympics Data Investigation

What you'll walk away with

Four Learning Outcomes.One confident data scientist.

Real, transferable skills bridging Python programming, data analysis, and storytelling with visuals.

Code Confidently in Python

Master variables, control flow, functions, and data structures in a notebook workflow.

Wrangle Real Datasets

Use Pandas to load, clean, filter, and transform messy CSV files into tidy DataFrames.

Analyze with NumPy

Run high-performance numerical computations and statistics across large arrays.

Tell Stories with Charts

Use Matplotlib to build polished bar, line, and scatter charts that communicate insights.

Tools & Technologies

Data Scientist's Tech Stack

Real-world tools used by data scientists at top companies — all run inside a single Google Colab notebook.

Python

The most popular programming language in data science. Students write real syntax to power every analysis.

Pandas & NumPy

The two libraries at the heart of every data project. Load, clean, transform, and compute on real datasets.

Google Colab

A free, cloud-based Jupyter notebook environment where students run Python, build charts, and share their analyses.

The Learning Path

Three modules.A real data investigation at the end.

From your first Pandas DataFrame to publishing a full data story with charts and conclusions.

Python for Data

Refresh and deepen your Python skills with a focus on data structures, file I/O, and the Google Colab notebook environment used by professional data scientists.

Python for Data

Refresh and deepen your Python skills with a focus on data structures, file I/O, and the Google Colab notebook environment used by professional data scientists.

Pandas & NumPy

Master the two libraries at the heart of every data science workflow. Load datasets, clean missing values, run aggregations, and compute statistics.

Pandas & NumPy

Master the two libraries at the heart of every data science workflow. Load datasets, clean missing values, run aggregations, and compute statistics.

Visualization & Insights

Transform raw numbers into insights. Use Matplotlib to build charts, then present findings as a professional data investigation.

Visualization & Insights

Transform raw numbers into insights. Use Matplotlib to build charts, then present findings as a professional data investigation.

Skills Gained

More than just code.

Every Grade 12 graduate leaves with data fluency, the ability to wrangle messy real-world datasets, and the storytelling skills to make findings matter.

Technical Skills

Python Fluency

Write professional Python using variables, control flow, functions, and data structures to script real analyses.

Data Wrangling with Pandas

Load, clean, filter, and transform messy real-world datasets using Pandas DataFrames.

Numerical Analysis with NumPy

Run high-performance numerical operations on arrays and compute statistics that drive insights.

Data Visualization

Turn numbers into stories with Matplotlib bar, line, and scatter charts that communicate findings clearly.

Soft Skills

Analytical Thinking

Ask the right questions about a dataset and choose the right tools to answer them.

Structured Problem Solving

Break down vague business questions into structured, testable analysis pipelines.

Data Storytelling

Communicate findings to non-technical audiences with clear visuals and concise insights.

Professional Presenting

Pitch projects with confidence — walk audiences through your data, code, and conclusions.

The Final

The Graduation ProjectOlympics Data Investigation.

By the end of this course, every student investigates a real-world dataset using Python, Pandas, and NumPy — then presents their findings as a full data story.

Olympics Data Investigation

Students load a real Olympics dataset, clean it with Pandas, run statistical analyses with NumPy, and uncover insights about countries, athletes, and medals.

Data Cleaning: Handle missing values, fix types, and reshape DataFrames.
Aggregation: Group by country, sport, and decade to uncover trends.
Statistical Analysis: Use NumPy to compute means, medians, and distributions.

Pitch & Publish

Students design Matplotlib visualizations, build a final presentation, and pitch their data story to a real audience.

Polished Charts: Build bar, line, and scatter charts that communicate clearly.
Final Slides: Frame findings as a story with takeaways for non-technical audiences.
Live Pitch: Present the investigation, take questions, and defend conclusions.

Module 2 : Smart Models

Build Predictive Modelsfrom Data to Decisions

Step into the world of advanced Data Science! Students learn to preprocess complex data, engineer robust features, and train sophisticated machine learning models including Linear Regression, Decision Trees, and Random Forests. By bridging AI assistance with business acumen, they will transform raw data into powerful predictive engines.

3 sections · 12 lessons · Predictive Machine Learning

Final Project

Delivery Time Predictor

What you'll walk away with

Four Learning Outcomes.One confident data scientist.

Real, transferable skills bridging Python, ML, version control, and deployment.

Engineer Data Features

Transform raw datasets through encoding, scaling, and advanced feature selection techniques to optimize model performance.

Train Machine Learning Models

Implement and combine regression and classification algorithms to make accurate, data-driven predictions.

Evaluate & Optimize

Use advanced evaluation metrics and data splitting strategies to ensure AI models are highly reliable.

Bridge Tech & Business

Apply AI models to real-world business logistics and showcase projects professionally on a GitHub portfolio.

Industry-Standard Tools

The Tech Stack.

Professional ML and engineering tools used by data teams in production.

Python

The industry-standard language for machine learning. Students use Python to preprocess data, engineer features, and train predictive models end-to-end.

Google Colab

A cloud-based notebook environment for training and evaluating machine learning models directly in the browser, no setup required.

GitHub

The professional platform where students version control their code, publish their ML projects, and build a portfolio recruiters can actually browse.

The Curriculum

Your Step-by-StepRoadmap.

From raw data pipelines to deployed ensemble models.

Data Prep & ML Fundamentals

Lay the groundwork! Learn the foundations of predictive analysis, prepare datasets through encoding and scaling, and train your very first linear regression models.

Data Prep & ML Fundamentals

Lay the groundwork! Learn the foundations of predictive analysis, prepare datasets through encoding and scaling, and train your very first linear regression models.

Advanced Models & Evaluation

Level up your AI. Build classification models, evaluate their accuracy using industry-standard metrics, and harness the power of Decision Trees and Random Forests.

Advanced Models & Evaluation

Level up your AI. Build classification models, evaluate their accuracy using industry-standard metrics, and harness the power of Decision Trees and Random Forests.

AI Agents, Business & Deployment

Connect code to the real world. Use AI agents to accelerate your coding, apply data science to business logistics, and publish your models to a professional GitHub portfolio.

AI Agents, Business & Deployment

Connect code to the real world. Use AI agents to accelerate your coding, apply data science to business logistics, and publish your models to a professional GitHub portfolio.

Skills Gained

Hard Code. Soft Skills.

Every graduate leaves with applied machine learning fluency and the business storytelling skills to ship a portfolio-ready predictive engine.

Technical Skills

Machine Learning Algorithms

Implement regression and classification algorithms to power accurate, data-driven predictions.

Random Forest

Train ensemble Random Forest models to boost predictive accuracy on real-world datasets.

Feature Engineering

Encode, scale, and select the right features so models learn from clean, signal-rich data.

Google Colab & GitHub

Develop notebooks in Colab and ship versioned, reviewable code to a GitHub portfolio.

Soft Skills

Business Acumen

Frame ML problems around business goals so models solve what stakeholders actually need.

AI-Assisted Coding

Collaborate with AI coding agents to ship faster while staying in control of the code.

Model Evaluation

Use evaluation metrics and validation strategies to judge model quality before shipping.

Data-Driven Decision Making

Translate model output into clear, justified recommendations leadership can act on.

The Capstone

The Final Project.

Showcasing predictive machine learning applied to business logistics.

Food Delivery Time Prediction

Students combine all their machine learning knowledge to solve a real-world business problem: predicting food delivery times. They build, train, and evaluate an AI model that processes complex datasets to generate accurate estimates.

Professional Deployment via GitHub

After building their predictive engine, students connect Google Colab to GitHub to version control their code and present their findings to parents, proving their readiness for university and the tech industry.