Data Science is a method that uses data to solve real-world problems. They aid in detecting fraud, forecasting market sales, climate change, and even heart illness. The rising use of artificial intelligence has boosted demand for data science, and the function of data scientists has grown in importance. It has increased the demand for data scientists globally.
The organizations use data scientists’ findings to forecast product futures, sales, and customer behavior to achieve long-term sustainability and growth. Preferring Data Science bootcamp and certification favors today’s tech-savvy pupils.
It is now the goal of every working professional and a newcomer to complete highly praised Data science projects. But why?
Every day, the demand for data scientists grows while the necessity for non-data scientists decreases. As a result, everyone wants to move to data science to avoid a recession.
What is the most crucial factor when it comes to landing your first high-paying data science job? It’s a real-time data science project on your resume that matters.
This writing has come up with a list of hot data science project ideas.
Top interesting Data Science projects
Data Science is a complex subject that requires continual practice to master the many concepts and terminologies. Aside from reading the literature, taking on some helpful initiatives can upskill you and improve your resume.
- Parkinson’s disease detection
Parkinson’s disease is an age-related condition where people lose control of their bodies. Symptoms include hand tremors, bodily rigidity, and even foot shuffling. This condition has 5 phases, with stage 1 being relatively unaffected by daily activities and stage 5 being severely restricted.
Data science comes into play here. You can provide a better health care service to a patient if a patient is diagnosed with Parkinson’s disease or shows indicators of the disease in the future. With XGBoost, you can identify Parkinson’s illness using Python as your coding language. In addition to C++, R, Python, Java, and Julia, XGBoost is an open-source software library supporting various languages and platforms.
- Face recognition
The HOG (Histogram of Oriented Gradients) technique is used in the face recognition project. This face recognition system can locate faces in images (HOG algorithm), align faces (ensemble of regression trees), encode faces (FaceNet), and generate predictions (Linear SVM).
An image’s 16×16 pixels square orientation gradients are calculated using the HOG technique rather than a pixel-by-pixel analysis. It will produce a HOG image that depicts the basic features of a person’s face. The next step is to utilize the dlib Python package for constructing and examining HOG representations.
- Forest fire prediction
One of the best data science projects is developing a forest fire forecasting system, which will make excellent use of the capabilities afforded by data science. A forest fire is an out-of-control blaze in a forest that destroys the natural environment and the habitats of animals and human property. You can use a data science project using k-means massing to predict forest fires and regulate their erratic patterns.
- Driver drowsiness detection
Every day at least one road accident is reported. Sleepy drivers have been linked to several traffic accidents. Drowsiness detection systems can help prevent unnecessary fatalities and road accidents. Another data science project could save many lives by continuously scanning the driver’s eyes and warning if the system detects frequent eye closures.
- Fraud app detection software
Malicious apps can access and misuse sensitive data stored on the phone. You can find fraudulent apps in both the Apple App Store and the Google Play Store. You’ll work on software that analyses app store information, such as ratings and reviews, to evaluate whether or not an app is authentic. Multiple applications can be processed at the same time by the program.
- Handwritten digit classification system
This project is an excellent method to learn about Deep Learning and neural networks. It works by recognizing images. Selecting the MNIST dataset for this project is good because it is diverse and user-friendly. This project teaches a machine (ML model) to recognize handwritten numbers as ten digits (0–9). It includes bank cheques, photos, emails, and everything else with a numeric entry.
- Credit card fraud detection
It is more widespread than you thought. By the year 2022, we’ll have surpassed a billion credit cardholders, figuratively speaking. Because of advances in artificial intelligence, machine learning, and data science, credit card issuers now have the tools necessary to detect and stop various types of fraud accurately.
Use R or Python to ingest the customer’s transaction history into decision trees, Artificial Neural Networks, and Logistic Regression. As you add additional data to your system, its accuracy should improve.
- Customer segmentation
Gender, age, income, and other characteristics of a customer’s profile can all be employed in customer segmentation. Unsupervised learning is used in this research. Clients are divided into subgroups in the partition approach based on predetermined characteristics. This data science study helps locate a market for the goods by identifying possible buyers. To train models, you can use customer data from the mall.
- Traffic sign recognition
In this Data Science assignment, you will use photographs of traffic signs and classify them with their meanings. The model gets more exact with additional photos, but it takes longer to train. The first step is to use convolutional neural networks (CNNs) to create an image model of a traffic signal. That’s how your model learns. The model would then recognize the new image as input.
- Building chatbots
Businesses are now using chatbots to automate customer care. Create your chatbot as a data science experiment. Open-domain chatbots and domain-specific chatbots are the two types of chatbots directly accessible. Recurrent neural networks and Natural Language Processing (NLP) are employed by both systems (RNN). If you’re an intermediate data scientist, you may want to consider designing a chatbot with the ability to identify user sentiment.
No data science project is challenging if you know the correct tools and approaches. Working on multiple projects is the best way to test any technology. It boosts your problem-solving skills and exposure. Your model-building skills will improve as you spend more time on Data Science projects.