Top Data Science Projects For Beginners & Experts

Introduction

Data Science is in demand across industries as it has become paramount for organizations to be data-driven in their decision-making process. As more and more organizations are investing in improving their data infrastructure and promoting data-driven solutions, the demand for data scientists has skyrocketed in the past few years and is expected to grow for the next decade.

Hence, data science offers a lucrative and promising career path with abundant opportunities for students and experienced professionals looking for a change in their careers.

If you are considering becoming a Data Scientist, you will have to learn a certain set of technical as well as interpersonal skills to get your first job in Data Science. Initially, it can be difficult to understand the concepts and terminologies used in the field but with regular practice and discipline, you can master all skills required to become a Data Scientist.

Once you have gained some solid understanding of theoretical technical concepts used in Data Science such as Statistics, machine learning algorithms, deep learning, etc., then comes the time to start working on some actual projects. Having hands-on experience on actual data science projects would not only help you build a strong resume but will also help you understand the complete lifecycle of data science projects and gain a deeper understanding of how data-driven solutions in industries work.

Data Science Project Ideas

In this section, we have listed down seventeen data science projects along with the source code based on your learning level such as beginners, intermediate, or advanced.

Data Science Projects for Beginners

This section will provide a list of data science projects suitable for students or professionals new to data science. These projects should be able to give you hands-on experience in developing data science and machine learning-based solutions such as classification, clustering, computer vision, etc., using programming languages such as Python and R.

For each project, we have also provided details such as programming language and links for the source code

1. Fake News Detection

• Fake news needs no introduction in today’s world. We all have experienced how ridiculously easy it has become to spread fake news from unauthorized sources over the internet. Fake news not only can create problems for the targeted persons but also has the potential to cause widespread panic and even violence.

• So, it is very crucial to contain the spread of fake news by identifying the authenticity of the source. You can do that through this Data Science project. This project uses Python as the programming language and applies TfIdfVectorizer before implementing various classification techniques. - Details of the project are below -

  • Language - Python
  • Source Code - Fake News Detection Using Python.

Forest Fire Detection

• Forest fires or Wildfires can cause a lot of damage to human lives as well as to the economy. As per reports, wildfires have caused around 90 billion USD in damages in 2021. So it becomes extremely important to contain the wildfires as soon as possible.

• This project would allow you to apply Convolution Neural Networks to detect the fires in the forest and apply K-Means Clustering techniques to identify the hotspots. This can help greatly in resource allocations while controlling the spread of the fire.
  • Details of the project are below -
    • Language - Python
    • Source Code - Forest Fire Detection

3. Road Lane Line Detection

• A live road lane line detection system is heavily used in self-driving cars or autonomous cars systems. This can help drivers let know whether they are driving within the lanes or not and can also help steer vehicles in the self-drive mode for the cars.
• This project would give you exposure to applying computer vision techniques such as Convolutional Neural Networks to build road lane line detection systems.
• Details of the project are below - * Language - Python * Source Code - Road Lane Line Detection

4. Sentiment Analysis

• Sentiment analysis is the process of detecting positive or negative emotions/sentiments from the input text. Many E-Commerce providers analyze customers’ reviews/feedback data to monitor the brand or product sentiment and understand customer needs for further improvement.
• The below projects can give you exposure to working with text data, and natural language processing using TfIdfVectorizer or Deep Learning methods.
• Details of the projects are below -
  • Language - Python and R
  • Source Code - Sentiment Analysis on Twitter Data in R Language
  • Source Code - Sentiment Analysis on Movie Reviews Dataset

5. Parkinson’s Disease Detection

• Data science is already in use for the improvement of healthcare and services. Data science has immensely helped diagnose a disease in the early stage, which can have many advantages in prognosis and can save many lives.
• In this project, the objective is to detect whether a patient has Parkinson's disease or not. It will give you hands-on experience in working with the XGBoost algorithm to classify whether a patient’s records belong to Parkinson’s disease or not.
• Details of the project are below -
• Language - Python
• Source Code - Detection Of Parkinson’s Disease

6. Color Detection

• Color detection is necessary to recognize objects. Some real-world applications of color detection systems are as a tool in various image editing or drawing applications, detection of traffic signals in self-driving cars system, etc.
• In this project, you will work with OpenCV-based color detection using python language. This project would pick each color for a given image and will display its color name and respective RGB values.
  • Details of the project are below
  • Language - Python
  • Source Code - Color Detection using OpenCV

Intermediate Data Science Projects

1. Speech Emotions Recognition

• Organizations store various human speech data such as customer call recordings etc. They must process this speech data and recognize its emotions to understand how the customer is feeling about their services. Speech Emotions Recognition can also help organizations to provide personalized services to their customers.

• This project would provide you with hands-on experience in dealing with speech data, converting it into a usable format, and analyzing it to recognize emotions.
  • Details of the project are below -
    • Language - Python
    • Source Code - Speech Emotion Analyzer and Speech Emotion Recognition

2. Gender Detection and Age Prediction

• This project is a classification challenge where you will apply your computer vision skills to detect gender and age by processing a photograph of the person. A few prevalent use cases of gender and age prediction are user segmentation in social networking websites based on their genders and age to recommend relevant ads and feeds, identifying gender and age for security purposes, etc.
• Detecting the gender and predicting a person's age is harder than it seems. There are some challenges such as dim-lighting, out-of-the-way facial expressions, cosmetics applied on the skin, etc. which can affect your solution’s accuracy and consistency. So it is an ideal project to test and improve your skills as an intermediate learner.
  • Details of the project are below -
    • Language - Python
    • Source Code - Gender and Age Detection using OpenCV and Age and Gender Estimation using CNN

3. Chatbots Development

• Chatbots have helped organizations achieve high efficiency and the improved customer experience in customer support and operations by automating the resolution of many customer issues.
• Chatbots can automatically provide appropriate solutions for many customers’ queries which can reduce the load on the support resources as well as reduce the turnaround time for resolution.
  • This project would help you experiment with NLTK to build your first chatbot solution.
    • Details of the project are below -
    • Language - Python
  • Source Code - Build a Chatbot in Python using NLTK

4. Drivers Drowsiness Detection

• One of the most common causes of accidents on the roads is sleepy drivers. In the USA, each year drowsiness accounts for nearly 100K crashes. So it is essential to build a Drivers Drowsiness Detection to avoid accidents that can detect whether drivers are sleepy or not by looking and tracking their eye movement and alerting them with alarms.
• In this project, you can apply your computer vision skills, such as CNNs, etc., and classify whether drivers are sleepy or not.
  • Details of the project are below -
  • Language - Python
  • Source Code - Drivers Drowsiness Detection

5. Uber Data Analysis in R

• This is a data visualization or data exploration project where you will use R libraries and explore Uber Pickup data for New York City.
• You can explore this data by analyzing various metrics such as trips by hours in a day or the number of trips by months in a year. You would get exposure to using the ggplot library in R language to create visualization such as bar charts, histograms, heatmap, etc.
  • Details of the project are below -
    • Language - R
    • Source Code - Uber Data Analysis in R.

6. Handwritten Digit Recognition

• This is an excellent project to get an understanding of how deep learning or neural networks works. In this project, your objective is to process a handwritten digit image and label the respective number.
• It is a multi-class classification project and an ideal one to test your skills on Convolution Neural Networks or Artificial Neural Networks.
  • Details of the project are below -
    • Language - Python
    • Source Code - MNIST Dataset: Digit Recognizer (ANN) and Digit Recognition using CNN.

Advanced Data Science Projects

If you have tested your skills in the above projects, then now is the time to move to some advanced data science projects.

1. Credit Card Fraud Detection

• Credit card fraud is quite widespread in the era of digitalization. Scammers try to get your card details and use that to access your account with your knowledge.
• Estimates suggest that there will be 1 billion credit card users by the end of 2022, which will give rise to credit card fraud cases. Though with the use of Data Science and Artificial Intelligence, the credit card industry has been able to contain fraud to a quite extent.
• This project is a classification challenge where you can apply techniques such as XGBoost, Artificial Neural Networks, etc. to detect whether a given transaction is fraudulent or not.
  • Details of the project are below -
    • Language - Python
    • Source Code - Credit Card Fraud Detection

2. Customer Segmentations

• Customer Segmentation is widely used by organizations to segment customers by analyzing their buying behaviors, demographics, and age. It helps organizations to come up with personalized marketing campaigns for each segment to improve sales and customer engagement.
• This project uses the K-Means Clustering technique to profile each customer by analyzing various metrics such as spending habits, age, demographics, etc.
  • Details of the project are below -
    • Language - Python
    • Source Code - Customer Segmentation

3. Recommender Systems

• Today homepages of YouTube, Netflix, or any social media websites such as Facebook, Twitter, etc. are powered by recommendation engines. The recommendation engine tries to suggest relevant content or feeds to the consumer to increase his engagement with the platform.
• This project analyzes the MovieLens dataset and builds a recommendation engine to suggest relevant movies to a user by applying a matrix factorization approach.
• Details of the project are below - * Language - Python * Source Code - Recommender System for Movies

4. Traffic Signs Detection

• Self-driven and autonomous cars must have the ability to detect various traffic signs such as speed limit signs, STOP signs, zebra crossing signs, etc.
• This project processes an image and deciphers various traffic signs present in it. This project would give you hands-on experience in applying various deep learning-based techniques to process image data and detect various traffic signs in it.
  • Details of the project are below -
    • Language - Python
    • Source Code - Traffic Signs Detection

5. Breast Cancer Classification

• Breast cancer cases have been on the rise in recent years, it is essential to diagnose them at the early stage to combat it and apply preventive measures.
• This project’s objective is to develop a breast cancer detection system in Python language by processing the IDC(Invasive Ductal Carcinoma) dataset, which provides histology images for cancer-inducing malignant cells. This system uses Convolution Neural Networks to develop the classification-based detection system.
  • Details of the project are below -
    • Language - Python
    • Source Code - Breast Cancer Detection System

Data Sources to Download Data Science Projects for Free

Below are some free online portals where you can find free data sources to download to work on your data science problems-

• Kaggle

  • Kaggle is a community-based platform specifically built for Data Scientists and Machine Learning Engineers. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

• Google

  • Google Dataset Search engine from Google helps data science practitioners to locate various datasets that are freely available to use.

• Github

  • Github is a platform where developers across the world can host their codes for version control and collaboration. You can find free datasets along with the source code of the solutions on this platform.

• Government datasets

  • You can find free datasets provided by US Government and the European Union at Data.gov and data.europea.eu respectively.

• Free datasets related to healthcare and services can be located at Healthdata.gov, and Health Statistics & Data.

Tips for Creating Interesting Data Science Projects

• Whether you are a student or an experienced professional, the first step to create a data science project is to come up with the right problem. Then you can look for the right datasets to solve the chosen problem at any of the sources mentioned in the previous section.
• Once the problem and dataset are selected, the next step is to divide your project into multiple steps. Any data science project can be divided into six steps as mentioned below -
  • Hypothesis generation - In this step, you need to come up with at least one hypothesis to solve the problem
  • Data Cleaning - Post hypothesis creation, you would require to clean the data by discarding irrelevant information and handling NULL values and outliers.
  • Data Exploration - Once data is cleaned, you can employ various statistical or visualization methods to explore the patterns and trends in the data
  • Feature Engineering - You would need to create or engineer features based on the data exploration which can be fed as input to the ML models.
  • ML Model Training and Testing - In this step, choose the right ML model based on the problem for the training and evaluate it on the test dataset. You would also be required to train multiple models with multiple sets of hyperparameters to come up with the best solution to the problem.
  • Communicate Results - Once the model is trained and developed, you can prepare your results and findings in such a way that is easily comprehensible for non-technical stakeholders or colleagues.