Data science is a practical field. You need various hands-on skills to stand out and advance your career. One of the best ways to obtain them is by building end-to-end data science projects that solve complex problems using real-world datasets.
Not sure where to start?
In this article, we provide 10 case studies from finance, healthcare, marketing, manufacturing, and other industries. You can use them as inspiration and adapt them to the domain of your interest.
All projects involve real business cases. Each one starts with a brief description of the problem, followed by an outline of the methodology, then the expected output, and finally, a recommended dataset and a relevant research paper. Most of the datasets are available on Kaggle or can be web scraped.
Below, we present 10 data science project ideas with step-by-step solutions. But first, we’ll explain what the data science life cycle is and how to execute an end-to-end project.
Top 10 Data Science Project Ideas: Table of Contents
- The Data Science Life Cycle
- Hospital Treatment Pricing Prediction
- YouTube Comments Analysis
- Illegal Fishing Classification
- Bank Customer Segmentation
- Dogecoin Cryptocurrency Prices Predictor with LSTM
- Book Recommendation System
- Gender Detection and Age Prediction Using Deep Learning
- Speech Emotion Recognition for Customer Satisfaction
- Traveling Agency Customer Service Chatbots
- Detection of Metallic Surface Defects
- Data Science Project Ideas: Next Steps
The Data Science Life Cycle
End-to-end projects involve real-world problems which you solve using the 6 stages of the data science life cycle:
- Business understanding
- Data understanding
- Data preparation
Here’s how to execute a data science project from end to end in more detail.
First, you define the business questions, requirements, and performance measurement. After that, you collect data to answer these questions. Then come the cleaning and preparation processes to get the data ready for exploration and analysis. These are the understanding stages.
But we’re not done yet.
Next comes the data preparation process. It involves the preprocessing and engineering of the features to prepare for the modeling step. Once that’s done, you can train the models on the prepared data. Depending on the task you are working on, you can do one of two things:
- Deploy the model on a live server and integrate it into a mobile or web application; then, monitor it and iterate again if needed, or
- Build dashboards based on the insights extracted from the data and the modeling step.
That wraps up the data science life cycle. Before you start working, you need some ideas for a data science project.
For starters, select a domain you are interested in. You can choose one that fits your educational background or previous work experience. This will give you a head start as you will know the field.
After that, you need to explore the common problems in this domain and how data science can solve them. Finally, choose a case study and formulate the business questions. Only then can you apply the life cycle we discussed above.
Now, let’s get started with a few project ideas.
Hospital Treatment Pricing Prediction
The increasing cost of healthcare services is a major concern, especially for patients in the US. However, if planned properly, it can be reduced significantly.
The purpose of this project is to predict hospital charges before admitting a patient. Data science projects like this one are a great addition to your portfolio, especially if you want to pursue a career in healthcare.
This will allow people to compare the costs at different medical institutions and plan their finances accordingly in case of elective admissions. It will also enable insurance companies to predict how much a patient with a particular medical condition might claim after a hospitalization.
You can solve this project using predictive analysis. This type of advanced analytics allows us to make predictions about future outcomes based on historical data. Typically, it involves statistical modeling, data mining, and machine learning techniques. In this case, we estimate hospital treatment costs based on the patient’s clinical data at admission.
- Collect the hospital package pricing dataset
- Explore and understand the data
- Clean the data
- Perform engineering and preprocessing to prepare for the modeling step
- Select the suitable predictive model and train it with the data
- Deploy the model on a live server and integrate it into a web application to predict the pricing in real time
- Monitor the model in production and iterate
There are two expected outputs from this project:
- Analytical dashboard with insights extracted from the data that can be delivered to hospital and insurance companies
- Deployed predictive model into production on a live server that can be integrated into a web or mobile application and predict treatment costs in real time
YouTube Comments Analysis
This following example is form the marketing and finance domain.
Sentiment analysis or opinion mining refers to the analysis of the attitudes, feedback, and emotions users express on social media and other online platforms. It involves the detection of patterns in natural language that allude to people’s attitudes toward certain products or topics.
YouTube is the second most popular website in the world. Its comments section is a great source of user opinions on various topics. There are many examples of how you can approach such a data science project.
Let’s explore one of them.
You can analyze YouTube comments with natural language processing techniques. Begin by scraping text data using the library YouTube-Comment-Scraper-Python. It fetches comments utilizing browser automation.
Then, apply natural processing and text processing techniques to extract features, analyze them, and find the answers to the business questions you posed. You can build a dashboard to present the insights.
- Define the business questions you want to answer
- Build a web scrapper to collect data
- Clean the scraped data
- Text preprocessing to extract features
- Exploratory data analysis to extract insights from the data
- Build dashboards to present the insights interactively
Dashboards with insights from the scraped data.
- Analysis and Classification of User Comments on YouTube Videos
- Sentiment Analysis on YouTube Comments: A Brief Study
Illegal Fishing Classification
Marine life has a significant impact on our planet, providing food, oxygen, and biodiversity. Unfortunately, 90% of the large fish are gone primarily as a result of overfishing. In addition, many major fisheries notice increases in illegal fishing, undermining the efforts to conserve and manage fish stocks.
Detecting fishing activities in the ocean is a crucial step in achieving sustainability. It’s also an excellent big data project to add to your portfolio.
Identifying whether a vessel is fishing illegally and where this activity is likely to occur is a major step in ending illegal, unreported, and unregulated (IUU) fishing. However, monitoring the oceans is costly, time-consuming, and logistically difficult.
To overcome these challenges, we must improve the ability to detect and predict illegal fishing. This can be done using classification machine learning models to recognize and trace illegal fishing activity by collecting and processing GPS data from ships, as well as other pieces of information. The classification algorithm can distinguish these ships by type, fishing gear, and fishing behaviors.
- Collect the fishing watch dataset
- Clean the data
- Perform data exploration to understand it better
- Perform engineering to extract features from the data
- Train classification models to categorize the fishing activity
- Deploy the trained model on a live server and integrate it into a web application
- Finish by monitoring the model in production and iterating
Deployed model running in a live server and used within a web service or mobile application to predict illegal fishing in real time.
- Fishing Activity Detection from AIS Data Using Autoencoders
- Predicting Illegal Fishing on the Patagonia Shelf from Oceanographic Seascapes
Bank Customer Segmentation
The competition in the banking sector is increasing. To improve their services and retain and attract clients, banking and non-bank institutions need to modernize their marketing and customer strategies through personalization.
There are various data science models that could aid these efforts. Here, we focus on customer segmentation analysis.
Customer or market segmentation helps develop more effective investment and personalization strategies with the available information about clients. This is the process of grouping customers based on common characteristics, such as demographics or behaviors. This substantially improves targeting.
In this project, we segment Indian bank customers using data from more than one million transactions. We extract valuable information from these clusters and build dashboards with the insights. The final outputs can be used to improve products and marketing strategies.
- Define the questions you would like to answer with the data
- Collect the customer dataset
- Clean the data
- Perform exploratory data analysis to have a better understanding of the data
- Perform feature preprocessing
- Train clustering models to segment the data into a selected number of groups
- Conduct cluster analysis to extract insights
- Build dashboards with the insights
Dashboards with marketing insights extracted from the segmented customers.
Dogecoin Cryptocurrency Prices Predictor with LSTM
Dogecoin became one of the most popularity cryptocurrencies in recent years. Its price peaked in 2021, and it’s been slowly decreasing in 2022. That’s the case with most cryptocurrencies in the current economic situation.
However, the constant fluctuations make it hard for a human being to predict with accuracy the future prices. As such, automated algorithms are commonly used in finance.
This is an extremely valuable data science project for your resume if you want to pursue a career in this domain. If that’s your goal, you also need to learn how to use Python for Finance.
In this section, we discuss a time series forecasting project, commonly encountered in the financial sector.
A time series is a sequence of data points distributed over a time span. With forecasting, we can recognize patterns and predict future incidents based on historical trends. This type of data analytics projects can be conducted using several models, including ARIMA (autoregressive integrated moving average), regression algorithms, and long short-term memory (LSTM).
- Collect the historical price data of the Dogecoin cryptocurrency
- Manipulate and clean the data
- Explore the data to have a better understanding
- Train a deep learning model to predict the future change in prices
- Deploy the model on a live server to predict the changes in real time
- Monitor the model in production and iterate
Deployed model into production integrated into a cryptocurrency trading web or mobile application. You can also build a dashboard based on the data insights to help understand the dynamics of Dogecoin.
Book Recommendation System
During the last few decades, with the rise of YouTube, Amazon, Netflix, and other similar services, the amount of information available online has grown immensely. As a result, it ca be difficult to find what you’re looking for without getting overwhelmed by the plethora of choices.
Recommendation systems provide а solution to this problem by offering quick access to relevant information. Big data projects of this kind are an excellent addition to your portfolio. They’re essential for any business selling or promoting products or content online, especially in big tech companies.
Recommender systems are everywhere – from e-commerce to online advertisement. Online platforms recommend to customers music, movies, articles, etc. based on the history of their preferences. That includes visited links, browsing activity, and other behaviors. In this project, we create a book recommendation system.
- Understand the business problem
- Collect the book recommendation data
- Explore, clean, and preprocess the data
- Predict the ranking using the trained model
- Deploy the model, monitor it, and iterate
The output is a real-time book recommendation system deployed on a live server and integrated into a web or mobile application.
Gender Detection and Age Prediction Using Deep Learning
Age and gender information have various real-world applications in biometrics, identity verification, video surveillance, human-computer interaction, electronic customer relationship management, crowd behavior analysis, online advertisement, item recommendation, and many more.
Automatically predicting age and gender from face images is a difficult task. From a technical point of view, the main challenge is the intra-class variations on facial images.
In this section, we show you how to build and train a CNN-based deep learning model to detect the age and gender of the person in a given image. Although challenging, demonstrating capability with such types of data science projects will impress future employers.
- Collet the dataset
- Data preprocessing, including face detection and alignment
- Train a deep learning model to detect gender and predict age
- Deploy the model on a live server and integrate it with a mobile or web application
- Monitor the model and iterate for updates
Deploy the trained models into production to estimate the gender and age of a person in a given image and integrate it into a web or mobile application.
Speech Emotion Recognition for Customer Satisfaction
Although we’ve learned to convey our attitudes and feelings in writing and through emojis, gifs, and pictures, speech remains one of the most reliable ways to recognize emotion.
As such, speech emotion recognition is an essential tool for measuring customer satisfaction. The results from such data science projects provide useful insights for improving user experience.
Customer service is the first point of contact for users and a common means to express dissatisfaction. It contains valuable information we can use to improve a business’s service or product.
However, customer service records contain various emotion-independent factors, such as speaker differences, environmental noise, voice quality, and so on, which reduce the reliability of speech emotion recognition.
- Collect speech data
- Data cleaning
- Speech preprocessing and feature extraction
- Train classification model to classify customer mood
- Deploy the model into production and integrate it with a mobile application
- Monitor the model in production and iterate
Deployed model to detect emotion and determine customer satisfaction levels. You can also build a dashboard representing the insights.
Traveling Agency Customer Service Chatbots
Chatbots are a common application of machine learning and AI in customer service and interesting data science projects for beginners.
Chatbots have become an integral part of e-commerce and e-services in general. They automate customer service using algorithms to answer basic questions via a business messaging app.
Here’s how to build one.
- Collect customer service text data
- Clean and prepare the data
- Train language model on the corpus data
- Deploy the model on a live server and integrate it into a mobile or web application
- Monitor the model and iterate
Real-time chatbot deployed on a live server and integrated into a mobile or web application.
Detection of Metallic Surface Defects
The last entry in our list of data science project ideas is in the manufacturing and heavy industries domain.
Quality control procedures are used to identify defects in products during the production phase of manufacturing. With the help of defect detection systems, they can be automated and improved.
Flawed products can result in substantial financial losses, so defect detection is crucial in manufacturing. Although human detection systems are still the traditional method employed, computer vision techniques are more effective.
In this example, we build a system to detect defects in metallic objects or surfaces during different phases of the production processes.
The types of defects can be aesthetic, such as stains, or potentially damaging the product’s functionality, such as notches, scratches, burns, lack of rectification, bumps, burrs, flatness, lack of thread, countersunk, rust, or cracks.
Since the appearance of metallic surfaces changes substantially with different lighting, defects are hard to detect even using computer vision. For this reason, lighting is a crucial component in solving such types of data science problems. Otherwise, the methodology of this project is standard.
- Collect the metal surface defects dataset
- Data cleaning and exploration
- Feature extraction
- Train models for defects detection and classification
- Deploy the model into production on an embedded system
- Monitor the model in production and iterate
A deployed model on an embedded system that can detect and classify metallic surface defects in different conditions and environments.
Data Science Project Ideas: Next Steps
Having diverse and complex data science projects in your portfolio is a great way to demonstrate your skills to future employers. You can choose one from the list above or use it as inspiration and come up with your own idea.
But first, make sure you have the necessary skills to solve these problems. If you want to start with something simpler, try the 365 Data Science Career Track. That way, you can build your foundational knowledge and gradually progress to more advanced topics. In the meantime, the instructors will guide you through the completion of real-life data science projects. Sign up and start your learning journey with a selection of free courses.