Didn’t take computer science? Try these 5 data science projects anyway!

yasirazeem227
Feb 9
3 min read

You don’t need a CS background to start data science! Here are five easy projects to help you dive in—even if you’ve never coded before.

When I decided to study data science at university, I realised I had a problem—I had never studied computer science in school. I took maths, physics, and chemistry at A-level, so I was comfortable with numbers, but I had never written a single line of Python.

If that sounds like you, don’t worry—you don’t need a CS background to start learning data science. The best way to get comfortable with it is by doing simple, real-world projects. Here are five beginner-friendly projects that you can do, even if you’ve never coded before.

1. Analysing your own study habits

Skills used: Basic Python, spreadsheets, statistics

Good for: Getting comfortable with data collection and simple analysis

If you’re a student, data science is already part of your life—you just don’t realise it yet. Have you ever wondered:

What time of day do you study best?
Do certain subjects need more revision than others?
How do your test scores change over time?

A simple project idea is to track your study sessions for a week (or use past exam scores) and look for trends. You can do this in Excel first and then try basic Python to analyse the data.

💡 Next step: Try using Python’s pandas library to organise your data and matplotlib to visualise trends.

2. Predicting your exam scores using past performance

Skills used: Basic Python, statistics, linear regression

Good for: Learning how data is used for predictions

If you’ve ever wondered “Can I predict my next exam score based on my past ones?”, that’s exactly what data scientists do in real life. You can take your previous test scores, add study time as a factor, and use a basic linear regression model to see how well you can predict future results.

💡 Next step: Learn about scikit-learn, a beginner-friendly Python library for machine learning.

3. Finding patterns in your favourite Netflix or Spotify playlists

Skills used: Python (optional), data visualisation

Good for: Understanding how data science powers entertainment platforms

Have you ever thought about why Spotify suggests certain songs or why Netflix recommends a show right after you finish another one? Data science plays a huge role in this.

For this project, take your own Spotify history (you can download it) and explore:

Do you listen to more upbeat songs in the morning?
Are your music choices seasonal?
Which artists dominate your playlist?

💡 Next step: Use matplotlib or seaborn to visualise trends in your music data.

4. Analysing trends in sports (or any topic you love)

Skills used: Data collection, basic Python, data visualisation

Good for: Applying data science to something interesting and relatable

If you’re into football, F1, cricket, or any sport, why not analyse team performance over time? You can download free datasets on match statistics, player performance, or even betting odds.

Here are some ideas:

Which football team wins most often when they score first?
Do home teams have an advantage?
Which F1 driver performs best in wet conditions?

💡 Next step: Try using the FIFA or NBA datasets on Kaggle to analyse player performance.

5. Exploring global temperature changes with real-world climate data

Skills used: Basic statistics, Python (optional), data visualisation

Good for: Understanding how data is used for real-world problems

If you’re interested in climate change, you can explore historical temperature records to analyse:

How much has global temperature increased in the last 50 years?
Which cities are warming the fastest?
How do CO2 levels correlate with temperature changes?

You don’t even need to code for this—many datasets come with ready-made charts, and you can use Excel or Google Sheets before diving into Python.

💡 Next step: Download datasets from NASA or Kaggle and explore trends in global temperatures.

Fast facts: what these projects will teach you

Data collection – Learn how to work with real-world datasets.
Data visualisation – Get comfortable with charts and graphs before coding.
Basic statistics – Start seeing patterns in data.
Python (if you’re ready) – Experiment with beginner-friendly libraries.

Do you need to learn Python before university?

If you’re heading to university for a degree in data science, you don’t have to be a Python expert before you start. Universities assume that many students (especially those from maths or science backgrounds) haven’t done coding before.

However, knowing the basics can make your first year easier. If you haven’t coded before, try: