Top 10 Data Science Applications

22-Sep-2022

The dynamic role of data science applications has come about with its interventions proving beneficial to the technologically driven world. The advances made can be credited to the quick computing and cost-effective storage systems that provide prediction outcomes within minutes. Alongside the growth of data science applications, the demand for experts manning the applications is at an all-time high, with data scientists earning a huge amount of $124,000 as their salary annually. 

The majority of today's industries are dominated by the data science field, which has evolved into their source of energy. It has created a new environment and transformed how people view data. It has emerged as a new trend across several industries, including manufacturing, banking, e-commerce, and healthcare. Hence the data science applications have a large pool  spread across numerous sectors, including the following:

• Fraud and Risk Detection

• Healthcare

• Internet Search

• Augmented Reality

• Targeted Advertising

• Website Recommendations

• Speech recognition

• Advanced Image recognition 

• Gaming 

• Airline Route Planning

• E-commerce

• Manufacturing

Let us discuss these best data science applications here in this post and gain an in-depth comprehension of the benefits of data science applications and how they are transforming the world. 

Data Science Applications across Varied Industries

Fraud and Risk Detection:

The field of finance saw early data science applications. Businesses were sick of suffering losses and bad debts year after year. However, their job requires collecting a lot of information during the original process for loan approval. To prevent losses, they made the decision to hire data scientists. The benefits of data science applications have been well exploited by banking organizations. They have mastered the art of data division and control through the use of consumer profiling, historical purchases, and other crucial factors to calculate the likelihood of risk and default. Additionally, it enabled them to market its banking services based on the purchasing power of its customers.

Healthcare

Data science applications are very helpful for the healthcare industry. The role of data science applications in the following scopes: 

• Analysis of medical images: Various techniques and frameworks, including MapReduce, are used in procedures like tumor detection, artery stenosis detection, and organ delineation to discover the best parameters for jobs like lung texture categorization. For the classification of solid textures, it uses wavelet analysis, support vector machines (SVM), content-based medical picture indexing, and machine learning techniques.

• Genetics & Genomics: Through the use of data science applications, high levels of therapeutic customization are also made possible by genetic and genomic research. The objective is to understand the effects of DNA on health and detect specific molecular links between genetics, disease, and treatment response. In illness research, data science techniques enable the integration of many types of data with genomic data, which offers a greater knowledge of genetic difficulties in reactions to certain diseases and medications. We will have a better grasp of the human genome as soon as we have access to trustworthy personal genome data. A significant step toward more individualized care will be the development of improved genetic risk prediction. 

• Development of Drugs: The process of discovering new drugs is extremely difficult and encompasses numerous disciplines. Implementing brilliant ideas is time-consuming, money-intensive, and requires a massive number of testing. It may take up to twelve years normally to submit a form. This process is made simpler and quicker by data science applications and machine learning algorithms, providing context to each stage from the first screening of medicinal compounds through the prediction of the success rate with reference to biological parameters. In place of conducting "lab tests," a prediction can be brought forward by these algorithms forecasting the substance reaction in the body through sophisticated simulations and mathematical models. The purpose of computational drug development is to develop computer simulations as biologically relevant networks that make future outcome prediction easier and more accurate.

• Virtual assistance for patient and customer support services: The idea that it is frequently unnecessary for patients to see doctors in person is the foundation for optimizing the clinical process. By sending the doctor to the patient directly, a mobile application can provide a more practical option. The mobile AI-powered apps, which are typically chatbots, can offer rudimentary healthcare support. Simply by describing your symptoms or posing inquiries, you can obtain important knowledge about your medical condition drawn from a large network that connects symptoms to causes. Apps can schedule a doctor's appointment for you if necessary and remind you to take your medication on time. This method eliminates the need for patients to wait in line for appointments, encourages them to make healthy decisions, and frees up doctors' time to attend to more urgent situations.

Internet Search:

This probably tops the list when thinking about data science applications. And Google will probably tick the first box in your head when talking about internet searches. The fact, however, is that there are numerous other search engines like yahoo, AOL, Ask, Bing, and others, and all of them employ data science algorithms to provide quick and accurate search results. Google is known to handle over 20 petabytes of data per day, which translates into massive processing. Without data science, Google would not have existed as it does today.

Augmented Reality

Another significant data science application that seems most promising for the future is the domain of enhanced reality. Virtual reality and data science do have interaction. We may see this in the incorporation of algorithms, computer expertise, and data in virtual reality headsets in order to provide an optimal viewing experience. The immensely popular game Pokemon GO is a very little step in that direction. the ability to navigate obstacles while viewing Pokemon on buildings, streets, and other fictitious objects. The Pokemon and gym locations in this game were chosen using information from Ingress, the previous app from the same business.

Targeted Advertising

There is an assumption that search engines have been the most popular data science applications, while it extends beyond and covers entire digital marketing.  Starting from digital billboards to display banners on various websites, their selection is made based on data science algorithms. This explains why traditional commercials have a far lower Call-Through rate (CTR) than digital ads. They may be chosen based on a user's previous actions. This explains the reason for another person might be seeing a clothing ad while another might be seeing an educational training program ad like a Data science course in the same location. 

Website Recommendations

The benefits of data science applications are greatly seen in the numerous business merchants like Amazon, where we often see suggestions for products that we may like, based on our search history. This intervention not only aids in finding relevant items or products but also adds manifold to the user experience. Many businesses have fervently utilized this engine to market their goods in accordance with customer desire and informational relevancy. This technique is used to enhance the user experience by internet behemoths like Twitter, Google Play, Netflix, LinkedIn, Amazon, IMDb, and many others. The suggestions are generated based on a user's prior search results.

Advanced Image Recognition

Upon posting a picture on Facebook with friends, you receive suggestions for tagging your friends. Face recognition software is used in this automatic tag recommendation feature. Facebook detailed the significant advancements they've made in this area in their most recent update, highlighting, in particular, their improvements in image recognition accuracy and capacity.

Voice Recognition

Google Voice, Siri, Cortana, and other products that use speech recognition are some of the best examples. Even if you are unable to type a message using speech-recognition technology, your life will continue. To convert a message to text, just speak it out loud. Speech recognition is challenged with accuracy faults at times. 

Airline Route Planning

The benefits of data science applications in the Airline industry are extensive. The interventions have ushered in better services which include the following:

Flight delay predictions

Assist in choosing airplane class 

Assist in the decision of landing, halting, direct flights to destinations, etc.

Promote customer loyalty programs efficiently.

Gaming

Data science algorithms have significantly helped in the development and upgradation of games nowadays.  In motion gaming as well, your adversary (the computer) studies your prior moves and adjusts your game accordingly. With the use of data science, various gaming developers like Sony, EA Sport, Nintendo, etc., have elevated their game experience.

E-commerce

The e-commerce industry is greatly benefited from data science techniques and machine learning ideas like NLP and recommendation systems. Using these methods, e-commerce platforms can examine consumer comments and transactions to gain valuable data for their company's growth. To evaluate texts and online questionnaires, they employ NLP. In order to better serve its clients, it uses collaborative and content-based filtering to examine the data. Identifying the client base, anticipating goods and services, determining the design of popular products, optimizing pricing schemes, and other activities have all had an impact on the data science business.

Manufacturing

Data scientists have taken on a vital role in both manufacturing and retail since they are the new factory workers in the manufacturing sector. By integrating robust gear and utilizing machine learning techniques like reinforcement learning, it has reduced the number of redundant employment. Additionally, enterprises may forecast possible issues, monitor systems, and analyze the constant stream of data by integrating with technologies like the Internet of Things (IoT). Let's examine the effects of data science on manufacturing:

Reduce energy expenses and increase working hours.

Depending on client feedback, make better judgments and improve the quality of the items.

Create an autonomous system to improve the production line utilizing previous and current data.

Every industry, including education, banking, finance, manufacturing, and others, have been impacted by data science to improve the quality of its goods and services for consumers. Due to the wide variety of data science applications, industries must stay current and advance with technology to be competitive.

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