Focusing at extensive knowledge of consumer data we’ve come to see that inside every industries, business models and technology environments role and responsibility of data scientist have changed, companies have become intelligent enough to push & sell products as per customers purchasing power & interest. As a result, there are several Data Science Applications in various industries like banking, finance, manufacturing, transport, e-commerce, education, etc.

Today business is more future-oriented, responds rapidly to changes in its environment, excels at perceiving what customers will want next, and is continuously alert to everything going on across its operations—all because the enterprise is digital to the core.

For example in banking sector, as data scientists you have to predict the risk of customers missing their next collections payment, smarter decisions through fraud detection, real-time predictive analytics, customer segmentation, with the goal of reducing bad debt.

If you are working as data scientist in any bank you have access to enough data with many features and dimensions like including customers, product holdings, transactions and collections activity.Based on the data you can build fraud detection or several predictions model for security and smooth accessibility.

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.

Once data collection and preparation, we identify the independent and dependent variable (i.e. target).then try multiple machine learning algorithms on the data which result into best-performing model accuracy.

Finally, with the model ready for production

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