Top 10 Data Science Project Ideas for Beginners and Experts

Top 10 Data Science Project Ideas for Beginners and Experts

List of 10 interesting data science projects ideas to boost your career growth in data science.

In the domain of artificial intelligence, data science has been a resonance for the last few years. As more industries and sectors are realizing the need for data science, more opportunities are finding their way. For this generation data science is providing the best career option. The demand for data scientists is continuously increasing in the market.

For becoming a data scientist professional you can do some technical data science projects, this will help in boosting your career growth. This will help you to gain practical skills and real-time data science experience.

If you do this in combination with formal training, then it is even better. Formal Power BI courses speed up your learning significantly and give potential employers evidence that you have the skills they need.

Following are 10 interesting data science projects for beginners as well as for the experts:


Chatbots can seamlessly manage customer queries and messages in real-time without any break. Chatbots help reduce the work pressure of humans by responsibly handling customer questions. This is done by utilizing techniques supported with AI, ML, and data science.  To train the chatbot, you can use recurrent neural networks with the intents JSON dataset and you can manage the application using Python. Whether you want your chatbot to be based on a specific realm or any realm depends on its purpose.

Credit Card Fraud Detection

We can see credit card fraud everywhere; it has become very common especially in the era of digital transformation. But innovations in technologies like artificial intelligence, machine learning, and data science, have led credit card companies to successfully recognize and catch these frauds with adequate accuracy.

For this project, you can use either R or Python to track the customer's transaction history as the dataset and take it into decision trees, artificial neural networks, and logistic regression.

Fake News Detection

Fake news needs no introduction. With the advent of the internet and social media fake news is growing to a large extent. The spread of fake news is affecting the lifestyles of all. Now and then we can witness fake information being spread from unauthorized sources creating widespread panic. By using data science projects, it is possible to identify the authenticity of any information whether it is fake or real. Using Python will help to segregate real news from fake ones. Some of the Python libraries suited for this project are pandas, NumPy, and sci-kit-learn.

Forest Fire Prediction

Data science offers numerous capabilities and building a forest fire prediction can be one such use done with the help of those capabilities. Forest fire or wildfire is something that is uncontrollable and causes a huge amount of damage. To manage and even assume the disrupted nature of wildfires, you can use k-means clustering to spot major fire hotspots and their acuteness. This could be useful in properly dispensing resources. You can also make use of the meteorological data to search for specific seasons for wildfires to increase your model's accuracy.

Classifying Breast Cancer

Breast cancer is the deadliest disease. Cases of breast cancers are rising, and the best possible way to fight breast cancer is to detect it at an early stage and take suitable protective measures. You can create a breast cancer detection system using Python. To create such a system with Python, you can use the IDC (Invasive Ductal Carcinoma) dataset, which carries histology images for cancer-inducing malignant cells, and you can train your model on this dataset. For the Python libraries, you can use NumPy, OpenCV, TensorFlow, Keras, sci-kit-learn, and Matplotlib.

Sentiment Analysis

Sentiment analysis is the act of exploring words to stimulate sentiments and opinions that may be positive or negative in polarity. This is a type of codification where the codes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted, etc). You can apply this data science project in the language R and use the dataset by the 'janeaustenR' package. You will have to use general-purpose lexicons like AFINN, bing, and Loughran, to display your result.


There are 16 million colors based on the different RGB color values but you only recall a few. So in this project, you can design an interactive app that will identify the selected color from any image. To execute this, you will need classified data of all the known colors then you can determine which colorfavors the most with the selected color value.

Driver Somnolence Detection

Sleepy drivers are highly prone to road accidents. One of the best ways to prevent this is to execute a fatigue detection system. This system can constantly evaluate the driver's eyes and can alert him with alarms when the system detects the closing of eyes. A webcam is important for this project to enable the system to occasionally monitor the driver's eyes. This Python project will require a deep learning model and libraries such as OpenCV, TensorFlow, Pygame, and Keras.

Recommender Systems (Movie/Web Show Recommendation)

Just like how Netflix, Amazon Prime use a recommendation system, you can make one for your project. You have to consider various metrics like age, formerly watched shows, most-watched genre, watch frequency, and inject them into a machine learning model which then produces what the user might like to watch next. For this project, you can select R with the MovieLens dataset that includes ratings for over 58,000 movies, and as for the packages, you can use ggplot2, reshap2, and data table.

Exploratory Data Analysis

For a data science project, data analysis can be the best one for you. For data, analysis visualization is important before exploration. For visualization, you can pick histograms, scatterplots, or heat maps. Once you have identified the structures and obtained the necessary insights from your data, you are ready to go.

Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.

Related Stories

No stories found.
Analytics Insight