Projects handled Domain wise
Data Science Applications:
1. Banking
banks can make smarter decisions through fraud detection, management of customer data, risk modeling, real-time predictive
analytics, customer segmentation, etc. In case of fraud detection, banks allow the companies to detect frauds that involve
a credit card, insurance, and accounting. Banks are also able to analyze investment patterns and cycles of customers and
suggest you several offers that suit you accordingly.
2. Finance
Data Science has played a key role in automating various financial tasks. Just like how banks have automated risk analytics, finance industries have also used data science for this task. Financial industries need to automate risk analytics in order to carry out strategic decisions for the company. Using machine learning, they identify, monitor and prioritize the risks. These machine learning algorithms enhance cost efficiency and model sustainability through training on the massively available customer data. Similarly, financial institutions use machine learning for predictive analytics. It allows the companies to predict customer lifetime value and their stock market moves.
3. Manufacturing
Data Science is being extensively used in manufacturing industries for optimizing production, reducing costs and boosting the
profits.
4. Transport
Data Science is actively making its mark in making safer driving environments for the drivers. It is also playing a key
role in optimizing vehicle performance and adding greater autonomy to the drivers. Furthermore, in the transport sector, Data
Science has actively increased its manifold with the introduction of self-driving cars.
Also, various transportation companies like Uber is using data science for price optimization and providing better
experiences to their customers. Using powerful predictive tools, they accurately predict the price based on parameters like a
weather pattern, availability of transport, customers, etc.
5. Healthcare
Medical Image Analysis
Genetics and Genomics
Drug Discovery
Predictive Modeling for Diagnosis
Health bots or virtual assistants
6. E-Commerce
Data Science is also being heavily used in collaborative filtering, where it forms the backbone of advanced recommendation system. Using this technique, the e-commerce platforms are able to provide insights to the customers based on their historical purchases and purchases made by people of the same style. These type of hybrid recommendation systems, consisting of both collaborative and content-based filtering are helping the industries to provide better services to their customers.