10 Top Data Science Applications
Data Science is a field of Big data that evaluates massive complex data and gives significant insights into the data. The field has been dominating most of the industries today and has become the fuel for industries. It has shaped a new world where it has revolutionized the way to perceive data. It has become a new trend globally, including various healthcare, banking, e-commerce, manufacturing, and more. It so has many Data science applications related to it in its pool. Big data giants like Amazon, Google, and Facebook use data science concepts for business insights and decisions for their organizations. The article explains the best data science applications. Let's have a look.
Top Data Science Applications
1. Fraud and Risk Detection
Finance made was an entrant in the data applications. Finance and data science go hand in hand as, like data science, Finance is also about data. Earlier companies had loads of paperwork to initiate sanctioning loans, maintaining them, incurring losses, and being in debt. So data science practices were thought of as the solution. To analyze risk probabilities, they learned to separate the data by customer profiling, past expenditures, and other necessary variables. It also helps to push their banking products based on customer's purchasing power. Another application could be that customer portfolio management analyzes trends in data through business intelligence tools for data science. Data science also introduces algorithmic training; financial institutions can make data-driven decisions through rigorous data analysis. Therefore, making the customer experiences better for the users as through extensive analysis of client experience and modification of preferences, financial institutions can create a personalized relationship with their customers.
Various industries in the field of health care take advantage of data science to get valuable insights. Let us study these popular industries and how they integrate with data science to make their outcomes productive.
2.1. Image Analysis and Diagnosis
Traditionally medical images were studied by doctors themselves, looking for clues to detect an abnormality, if any, in the patient's reports. Eventually, MRIs, X-rays, and CT scans created an influence on analyzing medical images. With further development in computing technologies and the surge in data, machines and image recognition tools have been created to automatically detect flaws in the image. Different methods and frameworks like MapReduce assist doctors in procedures and diagnose various diseases like detecting tumors, organ delineation, and much more.
2.2. Genetics and Genomics Research
Genetics and genomics research has taken a new turn and is inclined towards an advanced level of treatment with data science integration for better data analysis. Data science helps bioinformaticians and geneticists to analyze the reaction of genes to various medications. It aims to understand and study the impact of DNA on an individual's health trying to find biological connections between diseases, genes, and drug response. Big data technologies like MapReduce are known to reduce the processing time for genome sequencing significantly.
2.3. Drug Discovery and Development
Exploring and discovering a new drug involves many years of research and testing by the time it makes it to the manufacturing and finally gets a clearance to make it to medical stores and hospitals for patients. The procedure also involves a massive expenditure in terms of time and money. Data science has a significant impact on drug manufacturing by simplifying and shortening the process significantly.
It offers specific algorithms that provide us with timely insight into the success rate of the newly discovered drug.
3. Image Recognition and Speech Recognition
Data science algorithms rule the speech and image recognition fields. We can come across the great work of these algorithms in our everyday lives. Ever found yourself and taking assistance from virtual voice assistants like Google Assitant, Alexa, or Siri? Well, its speech recognition algorithm is working behind the system, trying to understand and evaluating your words and returning with productive outcomes of your use. Image recognition can also be all over your social media sites like Facebook, Instagram, and Twitter. These apps offer to recognize the person on your list and offer to tag them when you upload a picture with them on your profile.
4. Airline Route Planning
Companies like Southwest Airlines, Alaska Airlines have started to incur data science in their processing of flights to bring in the new change in their working style. Earlier airline companies suffered a massive loss with rising fuel prices as it became difficult for them to maintain occupancy ratio and operating profits. With data science, airline companies can think of strategic improvements like predicting flight delays, deciding on the aircraft to purchase, planning routes and layovers, and marketing strategies like a customer loyalty program.
They're also machine and data science algorithms that upgrade themselves to a new level as the gamer moves up the higher level in the game. The algorithm is designed and developed to analyze the previous performance of the gamer and shape up the game accordingly. Top-notch gaming studios like Zynga, EA Sports have upgraded to a new experience altogether with the help of such algorithms.
Data science has enabled banks across the globe to be more secure and manage their resources efficiently. It also enables them to make smarter and more strategic decisions and be saved from fraud. It also helps manage customer data, risk analysis and modeling, predictive analysis, and much more.
Data science allows bankers to assess the customer lifetime value allowing them to monitor and thus derive several predictions and analyze investment patterns of customers for their business. Machine learning algorithms improve analytics strategy in real-time.
Data science algorithms and machine learning concepts like NLP and recommendation systems are hugely benefitting the e-commerce market. The e-commerce platforms can study customer purchases and feedback using such techniques to get powerful insights for their business development. They make use of NLP to analyze texts and online surveys. It is used in collaborative and content-based filtering to analyze the data and provide better services to its customers.
Other ways that data science has impacted the data science industry include identifying customer base, forecasting goods and services, identifying the style of popular products, optimizing pricing structures, and more.
Data scientists are the new factory labor in the manufacturing industry and thus acquired a crucial position in manufacturing and retail. It has shrunk away redundant jobs by introducing powerful machinery using machine learning techniques such as reinforcement learning. Moreover, integrating with technologies like the Internet of Things(IoT) enables the industries to predict potential problems, monitor systems, and analyze the continuous stream of data. Let us see how does data science impacts manufacturing:
- Optimize energy costs and productive hours.
- Improving decisions and improving the quality of the products based on customer reviews.
- Build an autonomous system using historical and real-time data to boost up the manufacturing line.
The most significant advancement or the evolution that data science has given us in transportation is introducing self-driving cars. Data science has created a stronghold in transport through extensive analysis of fuel consumption patterns, driver behavior, and vehicle monitoring. It is making its name by providing safer driving environments for drivers, optimizing vehicle performance, adding autonomy to the driver, and much more. Through reinforcement learning and introduction to autonomy, vehicle manufacturers can create intelligent automobiles and better logistical routes. Popular cab services like Uber use data science to use various variables such as customer profiles, location, economic indicators, and logistics vendors to optimize price and delivery routes and proper allocation of resources.
Data science plays a vital role in nurturing a person's academic, non-academic, and social-emotional skills. It provides tools for measuring instructor performance, monitoring student requirements. It plays a diverse role in education, and universities are using them to innovate the curriculum.
To conclude, data science has made its mark on every industry like education, banking, finance, manufacturing, and more to make their products and services better for customers. Due to such diverse data science applications, industries need to stay updated and move forward with the technology in the competitive world. I hope you found this article interesting. Share in the comments below! Do you have any other data science applications to share where data science is used?
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