Top 10 Data Science use cases | Best Data Science Training Institute in Bangalore/India

• Image Recognition:

Image recognition is a potent use case. It is now being utilized in sundry industries like gaming, retail, automobile industry, convivial media, security, etc. The implementation of this technology in gregarious media needs no elaboration. Image apperception is widely put to utilize for security purposes. Netatmo is a smart indoor camera that commences recording when it detects an unknown face. Another use case is pet monitoring. Facial apperception is additionally optically discerning an ascending utilization cognate to security concerns at airports. Many countries are utilizing satellite imaging for crop monitoring to inspect agricultural areas more expeditiously.

Additionally, the automobile industry working on autonomous cars is utilizing the image/object apperception technology to make driving safer by finding ways to abbreviate accidents, following traffic rules, etc. Another paramount use case implementation is in the healthcare industry. Utilizing computer vision technology, x-ray reports, and other reports can be scanned, and health issues can be identified with considerable precision. Authentic-time facial apperception is utilized to identify the emotions of admitted patients to understand how they are reacting to the treatment.

• Healthcare:

Healthcare companies are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.

• Credit Scoring:

Being one of the most traditional applications of data science, credit scoring was introduced in 1989 with a FICO score. The score is still one widely used score for peer to peer lending through incipient machine learning algorithms and capture innovative factors that traditional scoring absolutely cannot.

A good instance that we optate to quote emanates from Ferratum Bank that used machine learning models to make better lending decisions, detect fraud more precisely, and expand their customer base more efficiently than other lenders. And by employing this approach they were able to reinvent how both consumers and businesses wanted to obtain a loan.

• Communication, Media & Entertainment:

Consumers now expect opulent media in different formats as and when they optate it on a variety of contrivances. Amassing, analyzing, and utilizing these consumer insights is now a challenge that data science is stepping into the tackle. Data science is being used to leverage convivial media and mobile content and understand authentic-time, media content utilization patterns. With data science techniques, companies can better engender content for different target audiences, measure content performance, and recommend on-demand content.

For example, Spotify, the on-demand music streaming accommodation, uses Hadoop sizably voluminous data analytics to accumulate and analyze data from its millions of users to provide better music recommendations to individual users.

• Outsourcing Industry:

The value of the global data science and analytics outsourcing market was US$ 2.49 Bn in 2018 and is expected to grow to USD 19.36 Bn by 2027 at a CAGR of 25.8%. Factors driving this magnification are a shortage of adept resources and high adoption by diverse industries.

Outsourcing companies are not far when it comes to Data Science Accommodations. They are making utilization of data science to automate back-office processes, keep prices in check, and truncate the turnaround time. Flatworld Solutions is one such company utilizing artificial perspicacity (AI) and machine learning (ML) to automate the back-end processes for clients to automatically relegate and index documents, process PDF files, name, and relegate files, automatically discover documents, utilize image annotation for inventory management, and more.

• Fraud Detection:

The moment we read the word fraud; we immediately relate it to monetary fraud. As a matter of fact, the Banking, Finance, and Indemnification sector are mired with fraudulent incidences. Cases of fraud can result in astronomically immense financial losses to banks. This withal hits the customers confide in the bank cognate to the safety of their money. Many times, fraudulent claims are made by customers to gain indemnification money. This is an ecumenical menace.

The rise of Data Science has come to rescue for these industries from losses. Machine Learning and Deep Learning are being utilized to presage and detect potential fraud. The utilization cases like presaging loan defaulters, identifying credit card defaulters, fraudulent activities in the indemnification, etc are a few examples where these industries can utilize Data Science. Some genuine-time implementation examples can be found here.

• Education:

One challenge in the edification industry where data science and analytics can avail is to incorporate data from different vendors and sources and utilize them on platforms not designed for varying data.

For example, the University of Tasmania with over 26,000 students has developed a cognition and management system that can track when a student authenticates into the system, the overall progress of the student, and how much time is spent on different pages, among other things.

Big data can also be acclimated to quantify teachers’ efficacy by fine-tuning teachers’ performance by quantifying against subject matter, student numbers, student aspirations, student demographics, and many other variables.

• Supply Chain Optimization:

8% of all industrial AI implementations are amendments to industrial supply chains. Utilizing AI implements to amend inventory management is one of the key applications. Predictive inventory management leverages predictive analytics for a variety of inventory-cognate tasks including to abbreviate inventory orchestrating time, minimize inventory cost, optimize repairments, and find optimal reorder points.  For these tasks, techniques such as time-series analysis, probabilistic modeling (Markov and Bayesian models) as well as simulations (e.g., Monte-Carlo simulation) are most commonly utilized.

• Smart cars:

IBM recently surveyed top auto executives, and 74% expected that we would optically discern keenly intellective cars on the road by 2025. A perspicacious car would not only integrate into the Internet of Things but additionally learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically predicated on the driver, report, and even fine-tune quandaries itself, drive itself, and offer genuine-time advice about traffic and road conditions.

• Transportation:

The application of machine learning in the transport industry has gone to an entirely different caliber in the last decennium. This coincides with the ascension of ride-hailing apps like Uber, Lyft, Ola, etc.

These companies use machine learning throughout their many products, from orchestrating optimal routes to deciding prices for the ascension we take.