12 Data Science Projects for Beginners and Experts

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills. The purpose of this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in enhancing your skills.

Top Data Science Projects

The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive. In this section, we’ll share a handful of fun and interesting projects designed for all skill levels.

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1. Building Chatbots

• Language: Python
• Data set: Intents JSON file
• Source code: Build Your First Python Chatbot Project

Chatbots automate a majority of the customer service process,  single-handedly reducing the customer service workload. They utilize a variety of techniques backed by artificial intelligence, machine learning and data science.

Chatbots analyze customer inputs and reply with an appropriate mapped response. To train the chatbot, you can use recurrent neural networks with the intents JSON dataset, while the implementation can be handled using Python. Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase. 

2. Credit Card Fraud Detection

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.
• Source code: Credit Card Fraud Detection Using Python

Credit card fraud is more common than you think. In fact, it’s an issue that has now impacted around 60 percent of credit card holders in the United States. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

The idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into decision trees, artificial neural networks and logistic regression. As you feed more data to your system, you should be able to increase its overall accuracy. 

3. Fake News Detection

• Language: Python
• Data set/Packages: news.csv
• Source code: Detecting Fake News

In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.

To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some Python libraries best suited for this project are pandas, NumPy and scikit-learn. For the data set, you can use News.csv. 

4. Forest Fire Prediction

• Language: Python 
• Data set: Algerian forest fires data set
• Source code: Forest Fire Predictor

Building a forest fire and wildfire prediction system is another good use of data science’s capabilities. A wildfire or forest fire is an uncontrolled fire in a forest. Every forest wildfire has caused an immense amount of damage to nature, animal habitats and human property.

To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of meteorological data to find common periods and seasons for wildfires to increase your model’s accuracy.

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5. Classifying Breast Cancer

• Language: Python
• Data set: IDC (Invasive Ductal Carcinoma)
• Source code: Breast Cancer Classification with Deep Learning

If you’re looking for a healthcare project to add to your portfolio, you can build a breast cancer detection system using Python. Breast cancer cases have been on the rise, and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.

To build a system with Python, you can use the invasive ductal carcinoma (IDC) data set, which contains histology images for cancer-inducing malignant cells. You can train your model with it, too. For this project, you’ll find convolutional neural networks are better suited for the task, and as for Python libraries, you can use NumPy, OpenCV, TensorFlow, Keras, scikit-learn and Matplotlib. 

6. Driver Drowsiness Detection

• Language: Python
• Source code: Driver Drowsiness Detection System with OpenCV & Keras

Road accidents take many lives every year, and one of the root causes of road accidents is sleepy drivers. A driver drowsiness detection system that constantly assesses the driver’s eyes and alerts them with alarms if the system detects frequently closing eyes is yet another project that has the potential to save many lives.

A webcam is a must for this project in order for  the system to periodically monitor the driver’s eyes. This Python project will require a deep learning model and libraries such as OpenCV, TensorFlow, Pygame and Keras.

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7. Recommender Systems (Movie/Web Show Recommendation)

• Language: R
• Data set: MovieLens
• Packages: Recommenderlab, ggplot2, data.table, reshape2
• Source code: Movie Recommendation System Project in R

Media platforms like YouTube and Netflix recommend what to watch next using a tool called the recommender/recommendation system. It takes several metrics into consideration, such as age, previously watched shows, most-watched genre and watch frequency, and it feeds them into a machine learning model that generates what the user might like to watch next.

Based on your preferences and input data, you can build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can use R with the MovieLens data set, which covers ratings for over 58,000 movies. As for the packages, you can use recommenderlab, ggplot2, reshape2 and data.table. 

8. Sentiment Analysis

• Language: R
• Data set: janeaustenR
• Source code: Sentiment Analysis Project in R

Also known as opinion mining, sentiment analysis is an AI-powered technique that allows you to identify, gather and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews and survey responses, and span a range of emotions such as happy, angry, positive, love, negative and excitement.

Modern data-driven companies benefit the most from a sentiment analysis tool as it gives them critical insights into customers’ reactions to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with janeaustenR’s data set along with the tidytext package. 

9. Exploratory Data Analysis

• Language: Python
• Packages: pandas, NumPy, seaborn, and matplotlib
• Source code: Exploratory data analysis in Python

Exploratory data analysis (EDA) plays a key role in data analysis as it helps you make sense of your data and often involves visualizing data points for better exploration. You can pick from a range of visuals, including histograms, scatterplots or heat maps. EDA can also expose unexpected results and outliers in your data. Once you have identified patterns and derived the necessary insights from your data, you are good to go.

A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy, seaborn and matplotlib.

A great source for EDA data sets is the IBM TechXchange Community. 

10. Customer Churn Analysis

• Language: Python
• Data set: Telco Customer Churn
• Source code: Telco Customer Churn options

Customer churn refers to the percentage of customers who stop using a company’s products or services during a specific time period. Businesses analyze churn to understand what led customers to leave, looking at factors like demographic information, services selected and customer account details. This way, they can identify other at-risk customers likely to leave and take measures to retain them. 

One way to approach this problem is to use Scikit-learn to build a decision tree, which can help predict which customers are at risk of leaving after being trained on churn data. Kaggle offers a churn data set (listed above) to get started, along with various data set notebooks containing unique source code that you can experiment with.   

11. Recognizing Speech Emotions

• Language: Python
• Data set: RAVDESS
• Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
• Source code: Speech Emotion Recognition with librosa

Speech contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. By analyzing the emotions behind speech, companies can use this information to restructure their actions, services and products to offer more personalized services.

This project involves identifying and extracting emotions from multiple sound files containing human speech. To make something like this in Python, you can use the Librosa, SoundFile, NumPy, Scikit-learn and PyAudio packages. For the data set, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which contains over 7,300 files. 

12. Customer Segmentation

• Language: R
• Source code: Customer Segmentation using Machine Learning

Modern businesses strive to deliver highly personalized services to their customers, which would not be possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.

For this project, you will use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region and interests. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

More Data Science Project Ideas to Build

• Visualizing climate change.
• Uber’s pickup analysis.
• Web traffic forecasting using time series.
• Impact of Climate Change On Global Food Supply.
• Detecting Parkinson’s disease.
• Pokemon data exploration.
• Earth surface temperature visualization.
• Brain tumor detection with data science.
• Predictive policing.

Throughout this article, we’ve covered 12 fun and handy data science project ideas for you to try out. Each will help you understand the basics of data science technology — a field that holds much promise and opportunity but also comes with looming challenges.

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