10 Data Science Applications in Real Life

  • Ashesh Anand
  • Sep 04, 2021
  • Data Science
10 Data Science Applications in Real Life title banner

According to the Journal of Data Science(JDC):

 

 “By ‘Data Science’ we mean almost everything that has something to do with data.” 

 

Today, data science has overtaken nearly every industry on the planet. There isn't a single industry in the world now that isn't reliant on data. As a result, data science has become a source of energy for businesses.

 

Data Science Applications haven't taken on a new function overnight. We can now anticipate outcomes in minutes, which used to take many human hours to process, because of faster computers and cheaper storage.

 

Future-oriented questions are addressed by data scientists. They begin with big data, which has three characteristics: volume, variety, and velocity. The information is then used to feed algorithms and models. Models that autonomously self-improve, recognizing and learning from their failures, are created by the most cutting-edge data scientists working in machine learning and AI.


 

Data science, often known as data-driven science, combines several aspects of statistics and computation to transform data into actionable information. Data science combines techniques from several disciplines to collect data, analyze it, generate perspectives from it, and use it to make decisions. Data Mining, statistics, machine learning, data analytics, and some programming are some of the technical disciplines that make up the data science field.

 

( Interested in Data Science? Read this: Skills required to excel as Data Scientist )

 

You can sneak a glance at the video below to understand what Data Science is



Data science is quickly becoming one of the most in-demand disciplines, with applications in a wide range of sectors. We know it has been revolutionizing the way we perceive data. 

 

 

Applications of Data Science in different sectors

 

We have rounded up some applications of Data Science in action. Let's explore them :

 


This image depicts different fields in which Data science is being used such as Travel, Healthcare, Sales, Marketing, Credit and insurance, Social Media, Automation, etc.

Different Sectors utilizing Data Science


 

  1. Healthcare

 

The healthcare industry, in particular, benefits greatly from data science applications. Data science is making huge strides in the healthcare business. Data science is used in a variety of sectors in health care.

 

  • Image Analysis in Medicine

 

  • Genetics and Genomics 

 

  • Drug Development

 

  • Virtual Assistants and Health bots

 

( Also Read - How Data Science is simplifying Healthcare? )

 

  • Medical Image Analysis

 

To discover ideal parameters for jobs like lung texture categorization, procedures like detecting malignancies, artery stenosis, and organ delineation use a variety of methodologies and frameworks like MapReduce. For solid texture classification, it uses machine learning techniques such as support vector machines (SVM), content-based medical picture indexing, and wavelet analysis.

 

  • Genetics & Genomics

 

Through genetics and genomics research, Data Science applications also offer a higher level of therapy customization. The objective is to discover specific biological links between genetics, illnesses, and medication response in order to better understand the influence of DNA on human health. 

 

( Also Read - Introduction to EpiGenetics )

 

  • Drug Development

 

From the first screening of medicinal compounds through the prediction of the success rate based on biological variables, data science applications and machine learning algorithms simplify and shorten this process, bringing a new viewpoint to each stage. 

 

Instead of "lab tests," these algorithms can predict how the chemical will behave in the body using extensive mathematical modelling and simulations. The goal of computational drug discovery is to construct computer model simulations in the form of a physiologically appropriate network, which makes it easier to anticipate future events with high accuracy.

 

  • Virtual Assistants and Health bots

 

Basic healthcare help may be provided via AI-powered smartphone apps, which are often chatbots. You just explain your symptoms or ask questions, and you'll get vital information about your health condition gleaned from a vast network of symptoms and effects. Apps can help you remember to take your medication on time and, if required, schedule a doctor's visit.

 

( Suggested Reading - Revolutionary Innovations in AI )


 

  1. Targeted Advertising

 

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

 

This is why digital commercials have a far greater CTR (Call-Through Rate) than conventional marketing. They can be tailored to a user's previous actions.

 

That’s the reason why you may see advertisements for Data Science Training Programs while someone else would see an advertisement for apparel in the same area at the same time.

 

Learn More about CTR - ( Call- Through Rate)

 

 

  1. Website Recommendations

 

Many businesses have aggressively exploited this engine to advertise their products based on user interest and information relevancy. This method is used by internet companies such as Amazon, Twitter, Google Play, Netflix, Linkedin, IMDb, and many more to improve the user experience.

 

The recommendations are based on a subscriber's prior search results.

 

( Related: How Netflix maximizes Customer Experience )


 

  1. E-Commerce

 

The e-commerce sector benefits greatly from data science techniques and machine learning ideas such as natural language processing (NLP) and recommendation systems. Such approaches may be used by e-commerce platforms to analyse consumer purchases and comments in order to gain valuable information for their company development. 

 

They utilise natural language processing (NLP) to examine texts and online questionnaires. To evaluate data and deliver better services to its consumers, it is utilised in collaborative and content-based filtering.

 

( Suggested Read : Applications of NLP )

 

Recognizing the consumer base, predicting goods and services, identifying the style of popular items, optimizing pricing structures, and more are all examples of how data science has influenced the data science industry.


 

5. Transport

 

In the field of transportation, the most significant breakthrough or evolution that data science has brought us is the introduction of self-driving automobiles. Through a comprehensive study of fuel usage trends, driver behavior, and vehicle tracking, data science has established a foothold in transportation. 

 

It is creating a reputation for itself by making driving situations safer for drivers, improving car performance, giving drivers more autonomy, and much more. Vehicle makers can build smarter vehicles and improve logistical routes by using reinforcement learning and introducing autonomy. 

 

According to ProjectPro, Popular cab services like Uber employ data science to improve price and delivery routes, as well as optimal resource allocation, by combining numerous factors such as consumer profiles, geography, economic indicators, and logistical providers.


 

( Also Read: Top Self Driving Car Companies )

 

  • Airline Route Planning

 

The airline industry has a reputation for persevering in the face of adversity. However, a few airline service providers are striving to maintain their occupancy ratios and working benefits. 

 

The necessity to give considerable limitations to customers has been compounded by skyrocketing air-fuel costs and the need to offer reductions in air-fuel expenses. It wasn't long before airlines began employing data science to identify the most important areas for improvement. 

 

Airlines may use data science to make strategic changes such as anticipating flight delays, selecting which aircraft to buy, planning routes and layovers, and developing marketing tactics such as a customer loyalty programme.


 

  1. Text and Advanced Image Recognization

 

Speech and picture recognition are ruled by data science algorithms. In our daily lives, we can see the wonderful work of these algorithms. Have you ever needed the help of a virtual speech assistant like Google Assistant, Alexa, or Siri? 

 

Its speech recognition technology, on the other hand, is working behind the scenes, attempting to comprehend and evaluate your words and delivering useful results from your use.

 

Image recognition may be found on Facebook, Instagram, and Twitter, among other social media platforms. When you post a photo of yourself with someone on your profile, these applications offer to identify them and tag them.

 

( Related blog - How AI and Big Data is used in Instagram )

 

 

  1. Gaming

 

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

 

( Also Read - Game Development Companies Integrating AI research )


 

  1. Security

 

Data science may be utilized to improve your company's security and secure critical data. Banks, for example, utilize sophisticated machine-learning algorithms to detect fraud based on a user's usual financial activity. 

 

Because of the massive amount of data created every day, these algorithms can detect fraud faster and more accurately than people. Even if you don't work at a financial institution, such algorithms can be used to secure confidential material. 

 

Learning about data privacy may help your firm avoid misusing or sharing sensitive information from consumers, such as credit card numbers, medical records, Social Security numbers, and contact information.

 

( Also Read - Best Data Security Practices )

 

  • Fraud Detection

 

Finance was an early adopter of data applications. Every year, businesses were fed up with bad loans and losses. They did, however, have a lot of data that was acquired during the first application for loan approval. They decided to hire data scientists to help them recover from their losses.

 

Finance and data science are inextricably linked since both are concerned with data. Companies used to have a lot of paperwork to start authorizing loans, keeping them up to date, suffering losses, and being in debt. 

 

As a result, data science methods were proposed as a remedy. They learned to segregate the data by consumer profile, historical expenditures, and other required characteristics in order to assess risk possibilities. It also aids in the promotion of banking products depending on the purchasing power of customers. 

 

Another example is customer portfolio management, which uses business intelligence tools for data science to evaluate data patterns. Data science also provides algorithmic training; financial organizations may use rigorous data analysis to make data-driven choices. As a result, making customer experiences better for consumers, as financial institutions may build a tailored relationship with their clients through thorough research of client experience and adjustment of preferences.

 

( How to Ensure Security while doing Digital Payments )


The image depicts different fields which are employing Data Scientists such as Transportation, Manufacturing, Banking, Finance, and Healthcare, etc.

Different Industrial fields employing Data Scientists


  1. Customer Insights

 

Data on your clients may offer a lot of information about their behaviors, demographics, interests, aspirations, and more. With so many possible sources of consumer data, a basic grasp of data science may assist in making sense of it.

 

For example, you may collect information on a customer every time they visit your website or physical store, add an item to their basket, make a purchase, read an email, or interact with a social network post. After you've double-checked that the data from each source is correct, you'll need to integrate it in a process known as data wrangling.

 

Related blog - Customer Behavioral Analytics

 

Matching a customer's email address to their credit card information, social media handles, and transaction identifications is one example of this. You may make inferences and discover trends in their behavior by combining the data.

 

Understanding who your consumers are and what drives them may help you guarantee that your product fulfills their needs and that your promotional strategies are effective.


 

  1.  Augmented Reality

 

This is the last of the data science applications that appear to have the most potential in the future. Augmented reality is a term that refers to one of the most exciting uses of technology

 

Because a VR headset incorporates computer expertise, algorithms, and data to provide you with the greatest viewing experience, Data Science and Virtual Reality have a connection. The popular game Pokemon GO is a modest step in the right direction. 

 

The ability to wander about and gaze at Pokemon on walls, streets, and other non-existent objects. To determine the locations of the Pokemon and gyms, the game's designers used data from Ingress, the company's previous software.

 

Data Science, on the other hand, will make more sense if the VR economy becomes more affordable and consumers begin to utilize it in the same way they do other applications.

 

( Also Read: Applications of AR in Healthcare )

 

 

Summing Up

 

These aren't the only domains where data science may be used. Apart from these uses, data science is used in marketing, finance, and human resources, healthcare, government programmes, and any other industry that generates data. 

 

Marketing departments use data science to determine which product is most likely to sell. Data can provide insights, drive efficiency initiatives, and inform forecasts when critical thinking meets machine-learning algorithms.

 

Understanding how to evaluate data sources, clean and organize information, and draw conclusions can be important skills in your job even if you aren't a data scientist. Let's wait to uncover what more incredible data science applications the future has in store for us! 

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