Exciting Data Science Project Ideas for High Schoolers in 2025

In today's AI-driven world, data science is transforming industries by enabling automation, predictive analytics, and better decision-making. Whether you're a beginner or an advanced learner, working on real-world projects is the best way to improve your skills. This list of data science project ideas will help you gain hands-on experience and build a strong portfolio.

1. Exploratory Data Analysis (EDA) on Public Datasets

Objective: Discover trends and patterns in datasets.

How to Do It:

• Choose a dataset from Kaggle or the UCI Machine Learning Repository.

• Clean the data using Pandas, handling missing values and duplicates.

• Create visualizations using Matplotlib and Seaborn to analyze distributions and relationships.

Tools: Pandas, Matplotlib, Seaborn

2. Movie Recommendation System

Objective: Build a system that suggests movies based on user preferences.

How to Do It:

• Use the MovieLens dataset to collect user ratings.

• Implement collaborative filtering or content-based filtering.

• Train the model using Scikit-learn or the Surprise library.

• Evaluate performance using RMSE (Root Mean Square Error).

Tools: Pandas, NumPy, Scikit-learn, Surprise

3. Sentiment Analysis of Social Media Posts

Objective: Analyze the sentiment of tweets or social media posts.

How to Do It:

• Collect tweets using the Twitter API and Tweepy.

• Preprocess text by tokenizing and removing stop words.

• Use TextBlob or VADER for sentiment classification.

• Visualize results with word clouds and bar charts.

Tools: Tweepy, NLTK, TextBlob, Matplotlib

4. Stock Price Prediction

Objective: Predict stock prices based on historical data.

How to Do It:

• Gather historical stock data from APIs like Yahoo Finance or Alpha Vantage.

• Perform feature engineering using moving averages and volume trends.

• Train models such as Linear Regression or LSTMs.

• Evaluate accuracy using Mean Absolute Error (MAE).

Tools: Pandas, NumPy, Scikit-learn, TensorFlow/Keras

5. Customer Segmentation Using Clustering

Objective: Group customers based on purchasing behavior.

How to Do It:

• Use e-commerce transaction data to analyze shopping patterns.

• Apply K-means clustering to segment customers.

• Use Principal Component Analysis (PCA) to visualize the clusters.

Tools: Pandas, Scikit-learn, Matplotlib

6. COVID-19 Data Analysis and Visualization

Objective: Track and analyze COVID-19 trends over time.

How to Do It:

• Collect data from Johns Hopkins University datasets.

• Analyze infection rates, recovery rates, and vaccination progress.

• Create interactive maps and time-series graphs with Plotly and Folium.

Tools: Pandas, Matplotlib, Plotly, Folium

Why Work on Data Science Projects?

• Gain practical experience and improve technical skills.

• Build a strong portfolio to showcase your expertise.

• Prepare for job interviews with real-world applications.

BetterMind Labs helps high schoolers dive into the world of data science by offering internships that provide hands-on experience with real-world projects like those mentioned in this article. Students can work on exciting tasks such as exploratory data analysis, building recommendation systems, or analyzing sentiment from social media posts. With personalized guidance, BetterMind Labs ensures that students not only sharpen their technical skills in tools like Python, Pandas, and Scikit-learn, but also build impressive portfolios that can set them apart when applying to top universities. These internships provide the perfect opportunity to apply classroom learning to meaningful projects, boosting both academic and career prospects in the rapidly growing field of data science.

Unlock your potential with BetterMind Labs!