Introduction to Data Science Applications
Data Science has created a buzz in almost all possible industries! And why not! It is the hottest topic that industries are looking at. It is changing the world and provides answers to the questions of the future. Moreover, it is bringing many surprises for the future store. It is known that huge data is getting generated day by day in many industries, and data science uses those data for decision-making. Since it is the most demanding and promising field and is the necessity of all the industries, it is widely used in many sectors like Healthcare, Insurance, E-commerce, Banking, Finance, Manufacturing, Transport, etc. grow over other sectors. It has already become a driver for the biggest revenue for many industries. We would definitely see these sectors growing faster than ever before with the power of data science. Here we will discuss data science applications which are explained below in detail.
Applications of Data Science
BFSI sector is the most popular sector for data science applications. It constitutes Banking, Financial Services, and Insurance. According to experts research Financial Services and Banking sectors covers almost 36% of the market and holds the first rank. Marketing and advertising sectors hold the second rank by contributing 25%, and E-commerce sectors hold the third rank with 15% contribution towards the data science market.
Data Science is transforming the world in a revolutionary manner. It is changing the way of perceiving data. Let’s try to understand the existing data science applications over a wide range of sectors.
1. Finance & Banking
The finance & Banking sector is the oldest adopters of data science applications. Banks and Finance industries have automated tasks for performing risk analytics. These industries are very much data-centric and act as fuel for their business growth. Data Scientist builds processes to extract insights from available raw data and then perform various analysis like statistical analysis, forecasting, risk analysis, etc. Such applications are being used in Risk Analytics, Real-time Analytics, Consumer Analytics, Customer Data Management, Fraud Detection, etc.
- Risk Analytics: In this, we try to instigate various risks a company can face from its competitors, credits, markets, etc. The training data can be generated from customer information, financial transactions etc., which is used to develop risk scoring models to assess their customers’ worth.
- Real-time Analytics: With advancements in technology, now we can process data as soon as it gets generated. In real-time, transactions happen in minor seconds, which needs to be monitored in a real-time manner. This functionality is possible because of real-time data pipelines like Kafka, Flume, etc.
- Consumer Analytics: Banks & finance industries provide customized offerings to their most valued customers using Consumer Analytics. They observe consumer behaviour through their buying pattern, purchasing capability and many other attributes to derive the customer lifetime value and increase their business over these customers.
- Fraud Detection: In Finance & Banking sector, finding fraudulent transactions is significant because these are directly related to monetary value. Analysts try to detect unusual patterns in the data and generate alerts for suspicious transactions in a real-time manner to prevent fraud. Clustering mechanisms are used to derive the fraud pattern coming out from data.
2. Marketing & Advertising
Here are some marketing & advertising data science applications, which are given below:
- To enter the market, we must have a solid strategy in hand that targets specific customers for which we also need to segment them based on their needs.
- Data science helps us do the same thing with predictive and descriptive analytics, which leverages certain clustering techniques such as asking means, hierarchical, etc.
- We can also leverage mathematical modelling techniques such as neural networks, which helps understand how much a customer is close to a particular segment of customers. One example is Social media analysis, where we get the data from various social media sites such as Facebook, Twitter, Instagram, snap chat, etc.
- And we analyze these data’ s and devise a proper marketing plan which will satisfy the customer needs. These data’s is very much useful for us to analyze the mindset of the customer while purchasing any product or their behaviour online; for example, A/B testing is used in the case of to figure out which ad is the most profitable and effective one for our business; we can also use other strategies such as content marketing and advertising plan.
3. E-commerce
The two big industries, retail and e-commerce, are the major benefactors of data science; some of those applications are:
- Recommender systems, wherewith the help of data science suggesting customers new products based on the products they buy.
- Predictive analytics, majorly used for forecasting.
- To establish a proper customer base and make the necessary changes in the pricing of products according to the customer.
In these two Industries, advanced recommender systems, which are basically a combination of collaborative filtering and content-based filtering, is used regressively. Based on these systems, we will provide insights based on the history of products purchased by customers and products bought by people who have similar tastes. They also use sentiment analysis to analyze the feedback given by customers and derive insights from them to improve. These sentiment analysis systems use natural language processing, a niche subject in data science that focuses only on text mining and learning.
Conclusion
From this article, we can understand that data science has created a major impact on all the major industries such as healthcare, e-commerce, finance and banking, transport, etc., for improving their products and customer base. Data science must be an integral part of all industries as this is the only way for the industries to grow and move forward in their respective domains. Another major application we saw for data science is the fraud detection used in the finance and banking industry; they also use it to analyze various data trends using BI tools.
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