Now that you are aware that demand for Data Science is booming high, you should understand how Data Science is used in different sectors. Before that we should know what Data Analytics is all about?. We can recognize future trends or results based on historical Data. We can’t do this just like that as machine learning features and few algorithms are involved to achieve this process. And that is why the business geeks have great focus over data science and data scientist. All global organisations have to rely on large volumes of customer data with which they try to predict their future trend in terms of sales and revenue. I think you can connect the dots now.
Let’s look at some of the sectors that are using data science.
Not Sure if you heard the term “Medical Imaging”. It’s one of the important terms that is used in the healthcare sector as it seeks to reveal internal structures hidden by skin and bones to diagnose and treat disease. This is nothing but devices like X-Ray, MRI and CT Scan that i am talking about. But one disadvantage here is that it cannot identify minute irregularities in our body system which can be done successfully using Data Science where deep learning technologies are also involved. There are different image processing techniques and you will learn more about it when you learn Data Science in detail.
Data Science is also useful for pharmaceutical companies as they widely use this data analysis technology for preparing better drugs. They give a broader view for these drug manufacturing companies with their statistics and algorithm approach to give a detailed trend from the patient database , their profiles and their meta data.
Data Science plays a vital role in this sector as they build out data patterns to analyse risk modeling, Fraud detection and Effective Cash management. They will be very useful to identify potential customers which is useful for banks. Since Data Science can perform effective predictive analysis, it can segregate top class customers and assign significant future value for these banks to make important decisions on investments.
Data Science is efficiently used in assessing Risk Management which is helpful to quantify the risk associated with an asset. And it is useful to calculate credit risk on things such as loans, credit union etc. And it is also easy for professionals to use this data science and other machine learning techniques to point out financial transactions that are suspicious that could
help banks to make important decisions. Real Time Analytics is important in the financial sector as the historical data may not be useful for them at times take key decisions related to financial transactions. The reason is that they have to perform real time analytics as there should not be any impact in latency.
Data Science is used in higher education with the help of Big Data which helps universities and institutes to perform efficient data management and measure staff/ student performance. This helps both students and staff to set up a platform that offers pleasant experience for both.
Great Demand for Data Science applications in the telecommunication industry as they uses these services to manage millions of customer records and offer great customer experience. Telecom Sectors realises huge savings on using this technology effectively.
Real Time Analytics is the key to improving store and Staff performance especially in the retail sector. You can track the number of store visitors in real time, what they purchase that would be useful on analysing the trend on the revenue, products that got sold and the products that are making huge revenue.
Believe it or not, US government is using data science especially the Department of Homeland Security uses big data process more efficiently to integrate and analyze data to find out if there is any threat or disaster for the country.
So it is high time that you should realise that data science is growing and the aspect of data science is all about finding issues or trends from data. And the analysis is done at more granular level to identify complex behaviours and inferences.
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