Data Science & AI with Python for Teens

Batch Price From £250 (approx. $337 USD) View Dates & Prices Python Game Development for Teens (Age 13-17) Training Course

Total Duration: 10-Hour
Course level: Intermediate to Advanced
Delivery Method: Live Instructor-led Virtual Classes
Certification: Students will receive a Certificate of Completion upon completing the course and submitting their project.

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

Time Zone:

There is no date for this course at this moment. Please complete the BOOKING REQUEST FORM below or come back to this page again later.

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)

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