Data Science for Teens
Turn data into decisions
Python data tools, statistical thinking, machine learning and real-world projects, the analytical skill that defines the 21st century, taught by a dedicated live tutor.
Curriculum designed by educators & engineers from
Projects
What Your Teen Will Build
Data Dashboard
An interactive visualisation of a real-world dataset, charts, filters and the story the data tells, built in Python
Predictive Model
A machine learning model trained on historical data to predict a real outcome, sports results, prices, outcomes
Exploratory Analysis Report
A complete EDA (exploratory data analysis) on a Kaggle dataset, findings presented as a clear, well-designed report
Real-World Data Project
An end-to-end project: question → data collection → cleaning → analysis → visualisation → written conclusion
What They'll Learn
Python for Data
NumPy arrays, Pandas DataFrames, loading datasets (CSV, JSON, APIs), basic data manipulation
Data Cleaning
Handling missing values, outliers, data type conversion, merging datasets, making data analysis-ready
Visualisation
matplotlib and seaborn: line charts, bar charts, histograms, scatter plots, heatmaps, correlation matrices
Statistical Thinking
Distributions, central tendency, spread, correlation vs causation, hypothesis testing, p-values
Machine Learning Fundamentals
Regression, classification, train-test split, cross-validation, model evaluation metrics
Storytelling with Data
How to present findings clearly, structure a data narrative, design charts for an audience not a notebook
Is This Course Right for Your Teen?
Perfect for
- Teens aged 13–17 with a passion for numbers, patterns and real-world questions
- Students with basic Python experience (variables, loops, functions)
- Teens interested in economics, science, medicine, finance or technology careers
- Students who want to stand out in university applications with a real analytical project
Not quite the right fit
- Students with no Python experience, take our Python Programming course first
- Students looking primarily for AI/model building. Data Science includes ML, but for deeper AI work, see AI Expert
How Sessions Work
60–90 Min Sessions
Long enough to explore a dataset, discover something surprising and understand why, not a rushed demo
Live Collaboration
Tutor and student work through data together, shared notebook, live analysis, instant explanations
Practice Between Sessions
Exercises, datasets and open-ended challenges to extend the analysis started in session
Progress Tracking
Session notes and project notebooks shared with parents after every lesson, always know what was covered
What Students & Parents Say
“He analysed the relationship between teacher-student ratios and WAEC results across Nigerian states. His secondary school published it in their newsletter.”
Ibrahim D., parent, Abuja
“I built a model that predicts Premier League match outcomes based on team statistics. It's 71% accurate. The tutor helped me understand why 100% is impossible.”
Chidi N., 16, Manchester
“Data science gave her a language for questions she already had. She's applying to read Economics at university. Her personal statement references her Homtutor project.”
Yetunde K., parent, London
Frequently Asked Questions
Is data science hard without a strong maths background?
You need to be comfortable with graphs, averages and basic algebra, roughly GCSE level. We explain statistical concepts intuitively before formalising them mathematically. Strong maths helps but isn't a prerequisite.
What's the difference between Data Science and AI Expert?
Data Science focuses on finding and communicating insights from data (statistics, visualisation, exploratory analysis) with ML as one tool in the kit. AI Expert goes deeper into how machine learning models actually work, architecture, training, neural networks. Many students do both.
Where does the data come from for their projects?
We use Kaggle datasets, public APIs, government data portals and sometimes data students collect themselves. Part of the learning is finding, evaluating and cleaning real data, not working with toy examples that are already tidy.
Will this help with A-Level or university statistics?
Significantly. The statistical thinking module covers concepts that appear in A-Level Maths (Statistics) and many university modules. Students often find their school stats lessons much easier after this course.
What careers does data science lead to?
Data analyst, data scientist, business intelligence, quantitative finance, academic research, healthcare analytics and many more. It's one of the highest-demand and highest-paying skills across every sector. Python + data skills is a combination that opens doors at 16 and continues opening them at 30.