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12 Exciting Data Science Projects for Beginners in 2021
  • Siddhartha
  • Apr 25, 2021


The Economist has claimed that the world’s most valuable resource is no longer oil but DATA. The amount of data generated and collected through sources such as sensors and user activities on the Internet has given rise to a new digital economy. As the world continues to become more data-centric with the advent of new technologies, the profession of data scientists continues to become more demanding. Termed as Harvard Business Review’s ‘sexiest job of the 21st century,’ data science has proven to be the most vital and sought-after job role by most leading companies today. The combination of factors including the boom in data collection, development of algorithms to model this data and increasingly cheap computing power have enabled data scientists to become an integral part of organizations today.

The world is moving forward with the extraction of large amounts of data from the potential consumers of products. This data is being used by companies worldwide for tailoring their products and offering extra services for their customers. That’s exactly what data science comes in. Building the products that process these large data sets and give a result requires data science knowledge. Getting into data science isn’t as easy as it looks: there are a lot of prerequisites before one can even solve a basic regression problem. The best way to learn is to build a few projects while putting the concepts into action.

This post provides a list of 12 interesting data science projects that cover different aspects related to data science. Whether you have just completed a data science course or just getting started with data science, implementing such projects provides a vigorous understanding and experience with core concepts required in data science. Coming up with a few topics for projects might be a bit difficult especially if you are a beginner and don’t know the various applications of data science. Our list of data science projects for beginners should be good enough to demonstrate where data science can be used in our daily lives, and solve the beginner’s dilemma for most of our readers.

Project 01: Credit Card Fraud Detection

credit card fraud detection data science project

The role of credit cards as a method of transaction has gained a lot of popularity over the years as the world aims to be a cashless society. However, it is also important to consider that credit card fraud is ranked as the most common kind of identity theft fraud. One of the principal tasks that can be done by machine learning algorithms is classification. Every credit card transaction results in the generation of some data that can be used by machine learning algorithms to develop a classifier. Using such a classifier in real-time can help detect fraudulent transactions almost immediately, resulting in time being saved and money. It is also one of the most common data science project ideas for beginners. You can implement it using machine learning.

Dataset: Credit Card Fraud Detection Data

Concept: Classification Algorithms

Project 02: House Price Prediction

Data science deals with two kinds of statistics: descriptive statistics and inferential statistics. Inferential statistics help predict results from unseen data using previously known data. The Boston housing dataset contains such data that can be used to predict the median value of owner-occupied homes by applying machine learning algorithms. Machine learning algorithms, specifically regression-based algorithms can extract patterns from the data and use these patterns to process new information and predict a real value. This dataset can help explore and understand different regression-based algorithms.

Dataset: House Price Prediction Data

Concept: Regression Algorithms

Project 03: Customer Segmentation

Customer segmentation is the grouping of market customers that share similar characteristics into collections. Customer segmentation according to their characteristics can be a huge advantage to developing uniquely appealing products. Promoting products to a particular customer segment can be more advantageous than advertising to less interested customers. Predicting a customer’s spending patterns according to the cluster they are classified into can be of significant business value. Clustering algorithms in machine learning help with clustering i.e. grouping similar data points. The dataset for customer segmentation contains attributes such as gender, age, annual income, and spending score that can help grouping customers that share a common pattern. Using data science for clustering is very beneficial for predictive analysis.

Dataset: Customer Segmentation Data

Concept: Clustering Algorithms

Project 04: Gender Detection & Age Prediction

An important form of data that data scientists may have to work with is images, especially images of people. The rise of deep learning algorithms and computer vision algorithms has allowed data scientists to be able to detect and extract a person’s facial features from images. Deep learning models that involve neural networks contain several convolutional layers that make it easier to extract information from images. A combination of a convolutional neural network and a classifier can help extract facial data from images and predict their age and gender. Hence, developing this project can be a good introduction to CNNs and image processing methods.

Dataset: Gender and Age Classification Data

Concept: Convolutional Neural Networks

Project 05: Movie Recommendation Engine

Recommendations are another type of prediction that a data scientist is now capable to develop using data. Recommendation engines are most commonly used in e-commerce sites and proven to have immense business value. Content streaming sites like Netflix can suggest movies using previous customers' watch history and patterns from other similar users. This corresponds to the two most common types of recommendation systems: content-based filtering and collaborative filtering. Developing this project involves building a recommendation engine that recommends other movies based on a specific movie. Students find this as a very interesting data science project idea as a beginner. You can make this a python project as well.

Dataset: Movie Recommendation Engine Data

Concept: Recommendation Systems

Project 06: Sarcastic News Detection

Data scientists use machine learning and deep learning tools such as natural language processing to enable machines to detect sentiment from text-based data. One such sentiment is sarcasm, which admittedly even humans find difficult to detect. Websites such as The Onion post satirical news articles that many people mistake to be real headlines, leading to misinformation. This project involves developing a classification model to classify a news headline as sarcastic or not sarcastic. Building such a model will introduce important NLP concepts such as word embedding and LSTMs in neural networks.

Dataset: Sarcastic News Detection Data

Concept: Natural Language Processing

Project 07: Fake News Detection

fake news detection for data science project as a beginner

This is one project that most data scientists look up to nowadays. Media has taken over the whole world: they virtually control every choice one can have. Politicians have inadvertently become attached to the media, forcing the distortion of many news articles. Many countries like the USA, Turkey, Brazil, and India have a huge problem of circulation of fake news that many residents believe to be real. Building something to separate the fake news articles from the rest would be a real boost to one’s resume. This also helps build up an understanding of natural language processing. It is also a quite new data science/ data mining project for beginners to make.

Dataset: Fake News Detection Data

Concept: Natural Language Processing

Project 08: Handwritten Character Recognition

With the world transitioning to an online presence, the detection of handwritten data has become even more important. Detection and simultaneous classification of the character is a good way to get started with data science. The project makes use of convolutional neural networks, so it should also serve as a beginner’s introduction to neural networks. The MNIST dataset has all the basic data to get one started for this project: it has data for a ton of handwritten images that can help one calculate the percentage match for a particular character.

Dataset: Handwritten Character Recognition Data

Concept: Convolutional Neural Networks

Project 09: Chatbot

chatbot project for data science beginners

One of the best projects that data science enables you to build might just be a chatbot. With most businesses trying to build their presence online, chatbots have become the lifeline for the industry. While e-commerce was the starting point, gradually most online businesses started using chatbots as a way of getting into the industry. Building a chatbot might be one of the most challenging things to take on for a data scientist: yet it might just be the thing to do if one wants to get into the industry. Chatbots can be generic by nature, or they can be specific for a particular domain. It is much easier to train a chatbot for a particular purpose because queries and responses are very restrictive by nature here. Generic chatbots require vast datasets and a lot of training time to put into operation.

Dataset: Chatbot Data

Concept: Natural Language Processing

Project 10: Breast Cancer Prediction

There are a few evils that human ingenuity hasn’t conquered yet, and cancer is one of them. Breast cancer claims the lives of millions of women throughout the globe. Building a prediction algorithm for the detection of the disease might go a long way in helping secure a data science internship in the medical industry. The IDC_regular dataset has the required data for the detection of Invasive Ductal Carcinoma: the most common type of breast cancer that has been observed. This project involves deep learning at its core with the help of the Keras library for classification.

Dataset: Breast Cancer Prediction Data

Concept: Deep Learning

Project 11: Image Caption Generator

It is very easy for humans to come up with captions for images just by seeing them. However, a computer cannot come up with captions for the image in the same way as humans do. The task can be split into two smaller subtasks: teach the computer what the image is, and then forcing it to come up with a caption in English. This goes into the domain of deep learning, and the Keras library is used for the classification of the data. Generally, a Long Short Term Memory (LSTM) network is used for projects like these.

Dataset: Image Caption Generator Data

Concept: Deep Learning

Project 12: Speech Emotion Recognition

A data scientist can never be too happy or satisfied with himself. Text and visuals are just tools for getting started; one can’t go too far without working on the audio. Detection of a person’s emotion from his or her speech is a really good way to branch out into new libraries and learn new stuff. Librosa is one library that will be used during this project to deal with the audio. For the actual detection of emotion, a Multi-Layered Perceptron (MLP) classifier has to be used. Human emotions are very fleeting and subjective by nature, so this is one difficult project that should way its way into a resume.

Dataset: Speech Emotion Recognition

Concept: Neural Networks

Most of the projects covered above are a bit complicated by nature and will require some studying and research work before one can proceed with them. But there’s quite some amount of mathematics involved in it! Fundamentals include both linear algebra and statistics: the greater one’s knowledge in these subjects, the better. Understanding how to train the data and then use it for performing the actual tasks comes after that. In general, one can proceed with regression, clustering, and classification algorithms in the beginning, and then move on to neural networks later on. Deep learning and natural language processing are some of the toughest parts of data science, and they are best kept aside till one has a solid understanding of the approach required to proceed with the project. Our list of data science project ideas for beginners is quite comprehensive, and they should cover all aspects of the subject; enough to get one started.

Hopefully, this post provides some helpful ideas to get started with data science projects for beginners. And if you need any help with data science, we can assist you and you get 1:1 live tutoring from our tutors.