DATA SCIENCE AND ITS APPLICATION IN GLIMPSE

 DATA SCIENCE AND ITS APPLICATION IN GLIMPSE

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Given the vast volumes of data created nowadays, data science is a crucial aspect of any industry. Data Science is currently one of the most hotly disputed subjects in the industry. Its popularity has expanded over time, and organizations have begun to use data science approaches to grow their operations and improve customer happiness.

Do you want to pursue a career in data science? If your answer is yes, then you've come to the right place. In this article, we'll explain what Data Science is, and its applications.

What is Data Science?

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Data science is an interdisciplinary field that uses scientific methods, procedures, algorithms, and systems to extract information and insights from structured and unstructured data and apply that information and actionable insights to a variety of application areas. Data mining, machine learning, and big data are all connected to data science.

Data science is a "concept that unifies statistics, data analysis, informatics, and related approaches" to use data to "understand and analyze actual occurrences." Within the framework of mathematics, statistics, computer science, information science, and domain knowledge, it employs techniques and theories from a variety of domains.

Data science, on the other hand, is different from computer science and information science. Data science, according to Turing Award recipient Jim Gray, is the "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven), and "everything about research is changing because of the impact of information technology" and the data flood.

Why Data Science?

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Let's take a glance at why we need Data Science. The growing need for Data Science can be attributed to several factors. Data Science is being used by all industries as a wonderful approach to improve business. The following are some of the reasons why Data Science is crucial.

1. In the retail industry, data science has played a critical role, aided by a recommendation system. Data Science can obtain a group of products that people have purchased together by analyzing their browsing history. As a result, if a buyer purchases one or two things from the set, the other products in the set can be recommended to him or her.

2.  Data Science plays an essential part in risk reduction in banking and finance by analyzing a customer's creditworthiness and, as a result, approving or denying a loan application. Stopping fraudulent transactions like credit card use, online purchasing, and insurance claims is another important use case.

3.  In the healthcare industry, data science is critical. Predictions can be generated using patient data to see if a person will become infected with a disease in the future. As a result, they can take some steps to avoid contracting the sickness. Data Science has made this possible since it can determine the relationship between several factors that contribute to the development of a disease.

4.     Data Science has benefited almost every industry, including health care, tourism, and education. Future projections can be formed based on existing data analysis, which will benefit these industries in growing their businesses and increasing profits. As a result, Data Science has become increasingly important in various fields.

5.  Data science is assisting businesses in connecting with their customers more effectively and efficiently. Clients are essential to a company's success and profitability. Companies can use Data Science to discover their clients' needs and provide them with better service.

6.   Better decision-making, predictive analysis, and pattern discovery are all aided by data science or data-driven science. It enables you to:

• Ask the proper questions to identify the root cause of an issue.

• Investigate the data in depth.

•  Use multiple algorithms to model the data.

• Graphs, dashboards, and other methods can be used to communicate and visualize the results.

In practice, data science is already assisting the airline sector in predicting flight disruptions to help both airlines and customers avoid misery. Airlines may improve their operations in a variety of ways with the help of data science, including:

• Plan routes and decide whether to fly direct or connecting.

• To predict flight delays, create predictive analytics algorithms.

•   Provide clients with customized promotional offers depending on their booking patterns.

• Choose which plane class to buy for better overall performance.

Data Science Applications

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Data Science Applications haven't taken on a new role overnight. We can now forecast outcomes in minutes, which used to take many human hours to process, because of faster computers and cheaper storage. A Data Scientist earns a remarkable $124,000 a year, thanks to a scarcity of qualified workers in this industry. Data Science Certifications are at an all-time high because of this!

Here are some applications that build on Data Science concepts and explore a variety of domains, such as:

1. Detection of Fraud and Risk

 

Finance was one of the first industries to use data science. Every year, businesses were fed up with bad loans and losses. However, they did have a lot of data that was acquired during the first filing for loan approval. They decided to hire data scientists to help them recover from their losses.

 

Banking businesses have learned to divide and conquer data over time using consumer profiles, historical spending, and other critical indicators to assess risk and default possibilities. Moreover, it aided them in promoting their banking products depending on the purchasing power of their customers.

 

2. Internet Search

 

When you think about Data Science Applications, this is usually the first thing that comes to mind. When we think of search, we instantly think of Google. Is that correct? However, there are other more search engines, such as Yahoo, Bing, Ask, AOL, and others. Data science techniques are used by all of these search engines (including Google) to offer the best result for our searched query in a matter of seconds. Because Google processes over 20 petabytes of data per day.

 

Without data science, Google would not have evolved into the 'Google' we know today.

 

3. Targeted Advertising

 

If you thought Search was the most important data science use, consider this: the full digital marketing spectrum. Data science algorithms are used to determine practically anything, from display banners on various websites to digital billboards at airports.

 

This is why digital advertisements have a far greater CTR (Call-Through Rate) than traditional commercials. They can be tailored to a user's previous actions. And, this is why you may see adverts for Data Science Training Programs while I see an advertisement for apparels in the same spot at the same time.

 

4. Healthcare

 

Data science applications are especially beneficial to the healthcare industry.

 

Genetics & Genomics

 

Through genetics and genomics research, Data Science applications also provide a higher level of therapy customization. The goal is to discover specific biological linkages between genetics, illnesses, and treatment response to better understand the impact of DNA on our health.

 

Data science tools enable the integration of various types of data with genomic data in illness research, allowing for a better understanding of genetic concerns in medication and illness response. We will have a better grasp of human DNA as soon as we have solid personal genome data. Thus, Advanced genetic risk prediction will be a significant step toward more personalized care.

 

Medical Image Analysis

 

To identify appropriate parameters for tasks like lung texture classification, procedures like detecting tumors, artery stenosis, and organ delineation use a variety of approaches and frameworks like MapReduce. It uses machine learning techniques such as support vector machines (SVM), content-based medical picture indexing, and wavelet analysis for solid texture classification.

 

Drug Development

 

The drug discovery process is quite complex and entails a wide range of professions. The best ideas are frequently limited by billions of dollars in testing, as well as significant money and time commitments. A formal submission takes an average of twelve years.

 

From the first screening of therapeutic compounds through the prediction of the success rate based on biological parameters, data science applications, and machine learning algorithms simplify and shorten this process, bringing a new viewpoint to each step. Instead of "lab experiments," these algorithms can predict how the substance will operate in the body using extensive mathematical modeling and simulations.

 

The goal of computational drug discovery is to develop computer model simulations in the form of a physiologically appropriate network, which makes it easier to anticipate future outcomes with high accuracy.

 

Virtual assistance for patients and customer support

 

The concept of optimizing the clinical process is based on the idea that patients do not need to see their doctors in person in many circumstances. By bringing the doctor to the patient, a mobile application can provide a more effective treatment.

 

Basic healthcare help can be provided via AI-powered smartphone apps, which are typically chatbots. You just explain your symptoms or ask questions, and you'll get important information about your medical condition gleaned from a vast network of symptoms and causes. Apps can help you remember to take your medicine on time and, if necessary, schedule a doctor's appointment.

 

Your.MD and Ada are now the most popular applications.

Gaming

 

Machine learning algorithms are increasingly used to create games that develop and upgrade as the player progresses through the levels. In motion gaming, your opponent (computer) also studies your previous moves and adjusts its game accordingly. EA Sports, Zynga, Sony, Nintendo, and Activision-Blizzard have all used data science to take gaming to the next level.

Reference List:

1. What Is Data Science? Roles, Skills & Courses, by Sumeet Bansal,

https://www.analytixlabs.co.in/blog/what-is-data-science/

 

2. Data science Wikipedia,

https://en.wikipedia.org/wiki/Data_science

 

3. What is Data Science and its Importance in 2021, by Simplilearn,

https://www.simplilearn.com/tutorials/data-science-tutorial/what-is-data-science

 

4. Top 10 Data Science Applications, by Edureka!,

https://www.edureka.co/blog/data-science-applications/

By AJEET SINGH,

I am Ajeet Singh possessing a year of experience in developing content for various blogs and websites. Leveraged passion for keeping up-to-date with developments in the digital and social media landscape helped me crave my path for coming up with better content. However, I am pursuing my Engineering in Computer Science. Also, I do have my own website(MYBOOKFLEX) where I put reviews of different kinds of books.