Top 15 Beginner-Friendly Data Science Project with Source Code

Are you ready to level up your practical skills in data science? Building real-world applications is the best way to learn and advance in a new skill. Creating a complete data science project not only enhances your technical skills but also boosts your confidence and increases your chances of landing a highly rewarding career.

In this blog, we will look into the top 15 beginner-friendly data science projects. This blog lists project ideas from basic to advanced levels. Plus, we are not just giving you the ideas; we will also provide you with source code for each project to help you get started quickly. So, let’s dive into these projects!

What is Data Science? 

By the name itself, we can say that it is a science about data. Data Science is the study of data to extract meaningful information and insights. Data science is used to provide a data-driven solution to real-world problems. It includes maths, statistics, machine learning(ML), and artificial intelligence(AI).

Top 15 Beginner-Friendly Data Science Project Ideas

This section lists beginner-friendly data science project ideas from difficulty levels ranging from basic to advanced. We will present each project idea with an estimated time taken to build it, its difficulty level, tech stack to use, deployment guidance, learning outcome, and Python version. So, let’s get started!

1. Web Scraping Movie Data from IMDB

In this project, you will scrape movie data such as title, genre, rating, year, director, and cast from the IMDB dataset using BeautifulSoup. Once the information is extracted, it can be stored in a structured dataset for further processing. It is the best way to understand web scraping and build your mini dataset.

Time Taken: 2 hours

Difficulty Level: Easy

Tech Stack: Python, BeautifulSoup, pandas

Python Version: >= 3.8

Learning Outcome: Scrape websites, parse HTML, and structure extracted data

Deployment: NA

Source Code: GitHub

2. Simple Stock Price Tracker

In this project, you will create a simple tool to track stock prices using real-time data from Yahoo Finance. It is used to monitor stock prices by pulling a live dataset. You will visualize price trends and learn to automate data collection using APIs.

Time Taken: 3 hours

Difficulty Level: Easy

Tech Stack: Python, yfinance, matplotlib

Python Version: >= 3.8

Learning Outcome: Learn to fetch financial data and visualize stock trends

Deployment: Streamlit (optional)

Source Code: GitHub

3. Weather Data Dashboard

Build a dashboard that displays real-time weather data for any city using OpenWeatherMap’s API. It pulls key details such as temperature, weather conditions, humidity, and wind speed from the API and presents them in an easy-to-read format. It is a great project to showcase your ability to connect frontend and backend logic.

Time Taken: 3 hours

Difficulty Level: Easy

Tech Stack: Python, OpenWeatherMap API, Streamlit

Python Version: >= 3.8

Learning Outcome: Usage of APIs, JSON data, and interactive dashboards

Deployment: Streamlit or Heroku

Source Code: GitHub

4. EDA COVID-19 Data

This project dives into pandemic data and analyzes how COVID-19 spread across different regions. Working with real-world datasets, it highlights key patterns and differences in the virus’s spread on both global and regional levels. This will visualize datasets, generate insight,s and spot global and regional trends through graphs and plots.

Time Taken: 4 hours

Difficulty Level: Easy

Tech Stack: Python, pandas, matplotlib, seaborn

Python Version: >= 3.8

Learning Outcome: Data cleaning, aggregation, and visualization

Deployment: Not required

Source Code: GitHub

5. Credit Risk Analysis

This project uses financial history and customer demographics to predict credit risk using basic machine learning models such as decision trees. The machine learning model aims to identify patterns that signal potential defaults. The main focus of this project is to turn the raw data into actionable insights.

Time Taken: 5 hours

Difficulty Level: Easy

Tech Stack: Python, pandas, scikit-learn

Python Version: >= 3.8

Learning Outcome: Learn to analyze financial data and build a basic classification model

Deployment: Streamlit

Source Code: GitHub

6. Movie Recommendation System

This is a recommendation system built using Python that leverages the power of the pandas library. It suggests movies based on user preferences by analyzing similarities between films. It is a great project for understanding data-driven solutions.

Time Taken: 6 hours

Difficulty Level: Intermediate

Tech Stack: Python, pandas, scikit-learn

Python Version: >= 3.8

Learning Outcome: Understanding of recommendation algorithms and similarity metrics

Deployment: Streamlit or Flask

Source Code: GitHub

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7. Flight Price Prediction

Build a predictive model to predict flight ticket price using an airline dataset based on factors such as travel date, flight duration, number of stops, and other relevant information. The goal is to uncover how these columns influence the price of a flight ticket.

Time Taken: 6 hours

Difficulty Level: Intermediate

Tech Stack: Python, pandas, scikit-learn, XGBoost

Python Version: >= 3.6

Learning Outcome: Deep understanding of regression models and feature extraction

Deployment: Heroku

Source Code: GitHub

8. Fake News Detection

This project’s detection system trains a model to distinguish between real and fake news articles by using TF-IDF to break down the content and then applies Naive Bayes or Logistic Regression to make predictions. This highlights how machine learning can be used to tackle misinformation by recognizing patterns in language and writing styles.

Time Taken: 7 hours

Difficulty Level: Intermediate

Tech Stack: Python, scikit-learn, TF-IDF, NLP

Python Version: >= 3.6

Learning Outcome: Master NLP techniques and build a text classifier

Deployment: Heroku (optional)

Source Code: GitHub

9. Building ChatBots

This project involves designing a chatbot to answer user queries and hold basic conversations. This is a real-time chatbot used to provide service to customers by addressing common queries and offering assistance in real time. By incorporating natural language processing techniques, it aims to create smooth and efficient interactions.

Time Taken: 8 hours

Difficulty Level: Intermediate

Tech Stack: Python, NLTK/Rasa, Flask

Python Version: >= 3.8

Learning Outcome: Building a conversational AI chatbot

Deployment: Webhook

Source Code: GitHub

10. Credit Card Fraud Detection

This project was used to detect fraud in transactional datasets using anomaly detection techniques. This helps in identifying suspicious activity in financial data. By analyzing patterns and identifying outliers, the system can flag suspicious transactions.

Time Taken: 8 hours

Difficulty Level: Intermediate

Tech Stack: Python, pandas, scikit-learn, SMOTE

Python Version: >= 3.8

Learning Outcome: To detect rare events accurately using imbalanced datasets

Deployment: Optional

Source Code: GitHub

11. Image Classification with CNNs

This project will classify images like cats and dogs using convolutional neural networks (CNN). It helps in understanding image processing, feature maps, and neural network architectures. By applying CNNs, this aims to gain a deeper understanding of how machines can recognize visual data.

Time Taken: 10 hours

Difficulty Level: Advanced

Tech Stack: Python, Tensorflow/Keras, OpenCV

Python Version: >= 3.8

Learning Outcome: CNN models and Image processing

Deployment: Streamlit (Optional)

Source Code: GitHub

12. Classifying Breast Cancer

This involves building a classifier model to detect malignant tumors in the breast from diagnostic data. By analysing extracted features from medical records, the models aim to accurately distinguish between benign and malignant cases. It is a helpful project and a practical application in the healthcare domain.

Time Taken:  9 hours

Difficulty Level: Advanced

Tech Stack: Python, scikit-learn, pandas

Python Version: >= 3.8

Learning Outcome: Usage of classification model in the healthcare domain

Deployment: Streamlit (optional)

Source Code: GitHub

13. Recognizing Speech Emotions

This project classifies emotional states such as happy, sad, or angry from speech. It can be achieved with the help of extracting MFCC features and using deep learning to train the classification model.

Time Taken: 11 hours

Difficulty Level: Advanced

Tech Stack: Python, librosa, TensorFlow

Python Version: >= 3.8

Learning Outcome: Extraction of audio features from speech and building an emotion classifier

Deployment: Streamlit (optional)

Source Code: GitHub

14. Social Media Trend Analysis

This project uses any of the social media API (Instagram, Twitter, etc) to collect and analyze the tweets from trending topics, user sentiment, and engagement metrics. This project is great for applying data science to real-time social media data.

Time Taken: 9 hours

Difficulty Level: Advanced

Tech Stack: Python, Tweepy, pandas, matplotlib

Python Version: >= 3.6

Learning Outcome: Understanding social media APIs, hashtags, user activity, and engagement trends

Deployment: Streamlit (optional)

Source Code: GitHub

15. Performing Sentiment Analysis on Tweets (BERT)

In this project, NLP is used heavily to fine-tune a transformer model like BERT to classify tweet sentiments such as negative, positive, or neutral. By training the model on social media text, the goal is to capture subtle differences in tone and content, making it effective for real-time sentiment analysis. 

Time Taken: 11 hours

Difficulty Level: Advanced

Tech Stack: Python, HuggingFace, Transformers, TensorFlow

Python Version: >= 3.8

Learning Outcome: Application of BERT and fine-tuning LLMs for classification tasks

Deployment: Streamlit or HuggingFace Space

Source Code: GitHub

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Conclusion

In conclusion, building projects that solve real-world problems is the key to mastering a skill. Similarly, creating various projects in Python and its libraries with different difficulty levels is the way to become an expert in data science. With the help of the top 15 data science projects listed above, you will master data analysis, exploratory data analysis, data visualization, machine learning models, LLMs, including deployment and version control systems. 

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