Data Science & AI with Python for Teens

Total Duration: 10-Hour
Course level: Intermediate to Advanced
Delivery Method: Live Instructor-led Virtual Classes

Regular Price: £250 (Approximately $333.67 USD)
Batch Price: From £250 (approx. $334 USD) 📅 View Dates & Prices

Course Overview

This 10-hour live course introduces students aged 13–17 to the exciting fields of data science and artificial intelligence using Python. Teens will learn how to work with real-world datasets, visualise data trends, and build simple AI models using beginner-friendly tools like Pandas, Matplotlib, and Scikit-learn.

Perfect for STEM-inclined learners, this course fosters analytical thinking, coding confidence, and practical AI understanding — aligned with A-Level Computing objectives.

Requirements

No prior data science or AI experience needed
Completion of Python Level 1 or similar experience
Basic knowledge of Python syntax (variables, loops, functions)
Zoom and an internet connection

We highly recommend you complete the following course(s) before attending the Data Science & AI with Python for Teens course:

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes

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Course Content

Introduction to Data Science
- What is data science?
- Types of data: structured vs unstructured
- Introduction to Jupyter Notebooks / Google Colab
- Loading data with Pandas
- Exploring CSV files
Data Visualisation
- Introduction to Matplotlib and Seaborn
- Creating bar charts, line graphs, histograms, and pie charts
- Labelling graphs and adding titles
- Using graphs to tell a story
Data Cleaning & Analysis
- Handling missing data
- Filtering, grouping, and sorting
- Descriptive statistics: mean, median, mode, std
- Finding correlations
Intro to Machine Learning
- What is AI vs ML?
- Supervised vs unsupervised learning
- Intro to classification problems
- Using Scikit-learn to train a classifier
- Splitting data into train/test sets
Final AI Project
- Choosing a dataset (or use a provided one)
- Planning features and target
- Training and testing an ML model
- Visualising and presenting results
- Discussing ethics of AI (bias, fairness)

Certification

Students will receive a Certificate of Completion upon completing the course and submitting their project.

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