Applications of Data Science

There are various application areas on which Data Science can be applied:

Fraud and Risk Detection:

The earliest applications of data science was in Finance. The companies were fed up with bad debts and losses every year. However, they had many data that use to get collected during the initial paperwork while sanctioning loans. They decided to bring in the data scientists to rescue them from losses.

Over the years, banking companies had learned to divide and conquer the data through customer profiling, past expenditures, and other variables to analyze risk and default probabilities. Moreover, it also helped to push their banking products based on the purchasing power of the customer.

Healthcare:

The healthcare sector, mainly, receives great benefits from data science applications.

1. Medical Image Analysis: Procedures like detecting tumors, artery stenosis, and organ delineation employ various methods and frameworks such as MapReduce to find optimal parameters for tasks such as lung texture classification. It applies machine learning methods, content-based medical image indexing, support vector machines (SVM), and wavelet analysis for solid texture classification.

2. Genomics & Genetics: Data Science applications also enable an advanced level of treatment personalization through genetics and genomics research. The goal is to find the impact of the DNA on our health and find individual biological connections between genetics, diseases, and drug response. Data science techniques allow the integration of different kinds of data with the genomic data in the disease research that provides a deeper understanding of genetic issues in reactions to a particular drug and disease. As soon as you acquire reliable personal genome data, you will achieve a deeper understanding of human DNA.
3. Drug Development: The process of drug discovery is highly complicated and involves many disciplines. The greatest ideas are often bounded by substantial financial, billions of testing, and time expenditure. On average, it takes around twelve years to make an official submission. Data science applications and machine learning algorithms simplify and shorten this process, thereby adding a perspective to each and every step from the initial screening of drug compounds to predict the success rate based on biological factors. Such algorithms can forecast how the compound will act in the body using advanced mathematical modeling and simulations instead of using lab experiments. The idea behind the discovery of computational drug is the creation of computer model simulations as a biologically relevant network simplifying future outcomes with high accuracy.
4. Virtual assistance for patients and customer support: Clinical process optimization builds upon the concept that, for many cases, patients don’t need to visit doctors in person. A mobile application gives a more effective solution by bringing the doctor to the patient. The AI-powered mobile apps can also provide basic healthcare support, usually as chatbots. You describe your symptoms, ask questions, and then receive key information about your medical condition derived from the wide network of linking symptoms to causes. Apps also remind you to take your medicine on time, and if required, assign an appointment with a doctor. This approach also promotes a healthy lifestyle by encouraging the patients to make healthy decisions, saving their time waiting in the line for an appointment, and allowing doctors to focus on more critical cases.

Internet Search:

This is usually the first thing that strikes your mind when you think of Data Science Applications.

When we speak of search, we think ‘Google.’ But there are various other search engines also such as Yahoo, Bing, Ask, AOL, etc. All these search engines (including Google) use data science algorithms for delivering the best result for a searched query in a fraction of seconds. If there were no data science, Google wouldn’t have been the ‘Google’ we know today.

Targeted Advertising:

Starting from the display banners displayed on various websites to the airports’ digital billboards, almost all are decided by using data science algorithms.

That is why digital ads have been able to get a lot higher CTR (Click-Through-Rate) than traditional advertisements. They can also be targeted based on a user’s past behavior. This is why you might see ads of the Data Science Training Programs while seeing an ad of apparels at the same time in the same place.

Website Recommendations:

Suggestions help you find relevant products from billions of products available with them and add a lot to the user experience.

Many companies have used this engine to promote their products in accordance with the user’s interest and relevance of information. Internet giants such as Amazon, Twitter, Google Play, Netflix, Linkedin, IMDB, and much more use it to improve the user experience. The recommendations are also based on previous search results by a user.

Advanced Image Recognition:

You can upload your image with friends on Facebook, and start getting suggestions to tag your friends. This feature of automatic tag suggestion uses a face recognition algorithm. In the latest update, Facebook has outlined the further progress they have made in this area, making specific note of their advances in image recognition accuracy and capacity.

Google also provides you with the option to search for images by uploading them. It also uses image recognition and provides related search results.

Speech Recognition:

Some of the examples of speech recognition products are Google Voice, Siri, Cortana, etc. Using this feature, even if you are not in a position to type a message, your life won’t stop. Speak out the message, and the message will be converted to text. However, sometimes, you would realize that speech recognition doesn’t perform accurately.

Airline Route Planning:

Airline Industry is known to bear heavy losses. Airline companies are struggling to maintain their occupancy and operating profits. With the high rise in the prices of air-fuel and the need to offer heavy discounts to customers, the situation worsened. It wasn’t long when airline companies started using data science to identify the strategic areas of improvements. Now with the help of data science, the airline companies can:

• Predict the flight delay.
• Decide which class of airplanes to buy.
• Decide whether to directly land at the destination or take a halt in between.
• Effectively drive customer loyalty programs. 

Southwest Airlines, Alaska Airlines are some of the top companies that have embraced data science to change their working way.

Gaming:

Games are now designed using the machine learning algorithms that improve/upgrade themselves as they move up to a higher level. In motion gaming, your opponent (computer) analyzes your previous moves and shapes up its game accordingly. EA Sports, Sony, Nintendo, Activision-Blizzard have led the gaming experience to the next level using data science.

E-Commerce:

E-commerce and retail industries have been profited by data science. Below are some of the ways in which data science has transformed the e-commerce industries are:

• Using predictive analytics for forecasting goods and services.
• Data Science is also used for identifying the styles of popular products and predicting their trends.
• With data science, companies are also optimizing their pricing structures for their consumers.

• Data Science is also heavily used in collaborative filtering, where it forms the backbone of an advanced recommendation system. Using this technique, the e-commerce platforms can provide insights to the customers based on their historical purchases and purchases made by other people of the same style. These hybrid recommendation systems, consisting of collaborative and content-based filtering, help the industries provide better services to their customers.
• Also, companies use sentiment analysis to analyze the feedback provided by the customers. This uses natural language processing for analyzing texts and online surveys. Fraud Detection is tailored for finding fraud merchants and frauds in wire-transfers.

Transport:

Data Science is making its mark in making safer driving environments for the drivers in the transportation sector. It is playing a key role in optimizing vehicle performance and adding greater autonomy to the drivers. Also, in the transport sector, Data Science has actively increased its manifold by introducing self-driving cars.

Data science has created a strong place in the transport industry through extensive analysis of fuel consumption patterns, driver behavior, and monitoring of an active vehicle. Self-driving cars are the most trending topic in the world today. With the introduction of autonomy to vehicles via reinforcement learning, vehicle manufacturers can create intelligent automobiles. Also, industries can create better logistical routes with the help of data science. Using various variables like consumer profile, location, economic indicators, and logistics, vendors can optimize delivery routes and provide a proper allocation of resources.

Also, various transportation companies like Uber use data science for price optimization and provide better experiences to their customers. Using powerful predictive tools, they accurately predict the price based on parameters like weather patterns, transport availability, customers, etc.

Price Comparison Websites:

The price comparison websites are being used by lots of data fetched using APIs and RSS feeds. If you have ever used these type of sites, you would know the accommodation of looking at an item’s cost from numerous sellers in one place. Some of the examples of price comparison websites are PriceGrabber, Junglee, Shopzilla, Deal Time, etc. Presently, the price comparison websites are found everywhere, such as innovation, accommodation, automobiles, durables apparel, etc.