Top Data Science Applications You Should Know About
Data Science is a blend of various tools, methods, algorithms, and processes. It involves storing, managing, and analyzing data to excerpt valuable information from both structured and unstructured data . Since it uses machine learning principles to get insights from raw data, most companies with a large amount of data opt to adapt it nowadays . Not only it helps to use this large volume of data at the right place but also bring in many skills like knowledge of the business domain and statistics to help companies thrive in various ways .
Moreover, it also helps in . The role of data science applications hasn’t evolved in a day. There are still lots of predictive analysis, weather forecasting, and making critical decisions when it comes to business data science challenges that professionals need to focus on to make it truly beneficial to humanity but faster computing and cheaper storage has played a crucial role to predict its results in a few minutes .
This Topic focuses on the main applications that build upon the concepts of data science. Let’s have a look at a few of them!
Use Cases of Data Science in Finance
Fraud & Risk Detection
Many companies got weary with the bad debts and losses almost every year. These companies have huge data that they collect during the initial paperwork while sanctioning loans. To get rid of this distress, they decided to bring in data scientists to rescue them out of losses, which resulted in finance being one of the earliest applications of data science.
Fraud detection is the most important part of any financial industry — data science and AI are often used together. Minor malfunctions and glitches can lead to financial loss and real-time predictive analysis helps in improving fraud detection and cybersecurity. Companies can provide their financial services in an efficient manner with data science’s help. This technology makes it easier to identify potential-fraudulent transactions and blocks the session in case detection of any unusual financial activity.
Financial Institutions need data. In fact, big data has transformed how financial institutions operate. So, banking companies learned to divide and conquer data through customer profiling, previous expenditures, and other dynamic variables to scrutinize the possible risks and default . Also, it facilitated them to drive their banking products on the basis of the customer’s purchasing power.
Use Cases of Data Science in Healthcare
When it comes to data science applications, the healthcare sector gets major benefits from it . Data science applications allow an advanced-level of treatment personalization via research in genetics and genomics . The aim is to understand the DNA’s impact on our health and determine individual biological links between diseases, drug response, and genetics . Data science techniques enable the integration of various kinds of data with genomic data in the disease research, providing a clear insight of genetic issues in reactions to certain drugs and diseases . Once you get a reliable personal genome data, it becomes easier to get a clear understanding of human DNA. This advanced genetic risk prediction will be a big move towards more individual care .
The drug discovery process involves many disciplines and is hard. Indeed, the key ideas are bound by a profusion of testing, massive financial, and time expenditure . Not only data science applications and machine learning algorithms simplify and abridge this process, but also add a perspective to every step — from the preliminary screening of drug compounds to the forecast of the success rate; all based on the biological factors . Such algorithms can forecast how the compound will react to the body using the latest mathematical modeling and simulations .
It is one of the fine emerging technology in healthcare. Procedures including artery stenosis, detecting tumors, and organ delineation need different methods frameworks like ‘MapReduce’ to find optimal parameters for tasks such as; lung texture classification . It employs machine learning methods, support vector machines (SVM), medical image indexing (content-based) as well as wavelet analysis for solid texture classification .
Customer Support & Virtual Assistance
Optimization of the clinical process builds upon the notion that for some cases, patients are not required to visit doctors in-person — a mobile application has the ability to give an effective solution by bringing the doctor to the patient instead . AI-powered mobile apps offer basic healthcare support in the form of “chatbots.” It goes like; you describe your symptoms or ask a question, and then get key information related to your medical condition derived from a wide network connecting symptoms to causes . These apps also remind you to take your medicine on time and assign an appointment with a doctor, if necessary .
Moreover, this approach encourages a healthy lifestyle and saves patients time waiting in line for an appointment and allows doctors to pay more attention to critical cases . Nowadays, “Your.MD” and “Ada” are the most common applications in use for this purpose.
Use Cases of Data Science in Algorithm Matchmaking
Everyone is addicted to technology through the Internet. When about searching something, “Google” is the first thing that comes to our mind. Yet, there are so many other search engines like Yahoo, Bing, and AOL. These search engines also make use of data science algorithms to provide the best result for our searched query in a bunch of seconds . Taking this into consideration, Google processes over 20 petabytes of data on a daily basis .
Many think that ‘search’ is the biggest of all data science applications. If you think the same too, here’s a challenger for you! From the display banners on websites to the digital billboards at the airports, almost every decision involves using data science algorithms . This is another reason why digital ads have the ability to get higher CTR than traditional advertisements as they are base on the user’s past behavior ……………
Read Full story at https://autome.me on August 18, 2020.