Top Data Science Projects For Beginners & Experts

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Python and SQL for Data Science
Python and SQL for Data Science
by Srikanth Varma
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Python and SQL for Data Science
Python and SQL for Data Science
by Srikanth Varma
Start Learning
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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 -

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.

3. Road Lane Line Detection

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

  • 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 -

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

  • 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.

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.

Installing pip on ubuntu

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.

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

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 -

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.

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.

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 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.

2. Customer Segmentations

Customer Segmentation Project

  • 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.

3. Recommender Systems

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.

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.

Breast Cancer Classification

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 and respectively.
  • Free datasets related to healthcare and services can be located at, 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.


Q: How Can You Find Interesting Data Science Projects To Try?

A: Kaggle, Google, or Github contains a variety of data science projects for each level of complexity. We have a section in the same article where we have mentioned various platforms for free datasets to use.

Q: How Do You Measure the Success of Data Science Projects?

A: As a learner, the most important measure for the success of the Data Science Project is how you approached the problem and applied your skills and knowledge to come up with the best possible solution.

Q: What is a typical Data Science project?

A: A typical Data Science project should have a business problem along with the relevant dataset. A Data Science project includes multiple steps, such as data cleaning, data preparation, data exploration, ML model training, etc., to come up with the best possible solution to the problem.

Q: How do I start a data science?

A: You would need to learn core skills required to become a Data Scientist which involves a strong understanding of programming languages, Statistics & Mathematics, Machine Learning Concepts, etc.

Q: How Can You Showcase Your Data Science Projects?

A: Once you have worked on a data science project and come up with the best possible solution, you can put it in your resume by mentioning each step you performed including the final accuracy of the ML model.
You can also maintain an active account on LinkedIn or GitHub and link these projects to these profiles.


Data science future is quite promising as demand for data science professionals has increased in the past few years as businesses are focused to be more and more data-driven in their decision-making process. It is a lucrative career path for students and experienced professionals as well. If you want to build a career in Data Science and have learned the theoretical concepts of technical skills required, you can apply your skills to the projects we have mentioned in this article based on your current level. In fact, the practical application of any technology is best tested by working on several projects, which provides you with the right amount of exposure and increases your problem-solving skills.

If you’re looking for a high-paying career with plenty of job opportunities, becoming a data scientist may be the right choice for you. And if you want to start a career in Data Science, check out Scaler’s Data Science program.