Data Science Applications commonly Used
Data science is one of the most exciting fields of our modern age, and its applications and uses are almost endless, as we will see data science applications are abundantly around us in everything we do and deal with.
Due to the permeation of the Internet in all our life activities and the development of electronic devices, we are producing huge amounts of data every day. This data describes our activities, our behavior, our purchasing habits, our lifestyle, our preferences, and what goes beyond this down to our psychological state, moods, fears and aspirations.
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| Data Science Applications |
This data, through science or the field called Data Science, helps us understand and analyze our behavior and activities to provide better services and improve our experience as users, which will benefit the provider of these services, whether it is a company, organization or government in the affirmative.
In this article, I will review the applications of data science, but because they are too many and exceed the limit, we will focus on the most important and prominent 6 applications of data science that directly affect our lives, but first we have to know what data science is.
What is data science?
Data science is the most exciting science in the twenty-first century, and this is because it deals with the most valuable thing we own and produce now, which is data. This science, in short, is the one that combines statistics with programming in order to collect and analyze data and extract results and facts from it.
We produce data in every action we do, whether we browse Facebook or even through our banking operations, and this data is collected and analyzed by these companies and institutions, in order to understand us and make their services more attractive and appropriate to our needs and preferences.
This data may be used for good purposes, as we mentioned to improve the services and make them attractive to us, but - the data - may also be used for bad purposes, such as: manipulation, fraud, fraud or control of the owner of this data, and for this reason your data is very important and you should protect it as much as possible .
Data Science Applications
There are a lot of data science applications that probably need dozens of volumes to mention, but in this article we will focus on the uses or applications of data science in only six areas, namely:
- Health Care.
- The field of banking services.
- The financial sector.
- Retail Industry.
- Transportation.
- Government sector.
First Data Science Applications in Health Care
I admit that my medical background prompted me to talk about healthcare first, but that's because of the amazing amount of things data science has given us, which has greatly improved the efficiency of our disease diagnosis, better understanding of these diseases as well as discovery and improvement of drugs, and increased patient quality of life.
The use of data science in medicine is the trend of scientific research during this period due to the Covid-19 virus, but before the virus there was a strong demand for it as well, so that there are certain areas that were established to include the areas of data science use in medicine and biology, and we can summarize the contributions of data science in the field of Medical care in seven basic points, namely:
- Medical Image Analysis.
- Genetics and Genomics.
- Drug Discovery.
- Predictive Modeling for Diagnosis.
- Monitoring Patient Health.
- Tracking & Preventing Diseases.
- Medical Virtual Assistance.
We will discuss it in detail in the following lines:
A) Medical Image Analysis
Medical radiology is an important part of the process of discovering and diagnosing some diseases, especially cancerous tumors and lung diseases. Therefore, any progress we make in early detection of these diseases through radiology will save the lives of hundreds of thousands and perhaps millions of patients annually, and here comes the role of data science.
By collecting various x-ray images: X-Ray, CT-Scans, MRI, etc., and then processing them using Image Recognition and processing algorithms, we were able to make the computer able to read and diagnose them.
We also developed a method that made it able to detect patterns on its own, which eventually led to it being superior to humans in diagnosis. Computers and software used to diagnose breast cancer radiology outperform a human expert by more than 10%, and they can detect a tumor weeks before we are able to discover it.
The same applies to brain tumors, where the computer can diagnose these tumors weeks early, in addition to being superior to the human expert by more than 8%, which is very important in difficult tumors, such as: brain tumors.
This early detection saves tens of thousands of lives in advanced hospitals in the United States and Europe, in addition to saving millions of dollars annually due to the speed of detection and ease of treatment for the patient at this early stage.
b) Genetics and Genomics
Just as codes control the nature and how programs work, genes control us, whether through physical traits, diseases, and even the length or shortness of our lifespan.
Therefore, it is a valuable treasure. By analyzing the human genome, you can identify the diseases that he is likely to contract to try to prevent them, in addition to understanding the distinctive interaction between chemical compounds such as drugs and his body.
This is precisely the importance of data science in the field of genetics, that we can analyze this huge amount of genetic data in order to give us Insights results that we can make medical decisions through. By using this genetic analysis we will be able to save millions of lives and improve the quality of life for billions of people.
We will also be able to choose the most effective drugs for the patient's condition by analyzing his genes, which will increase the effectiveness of drugs, especially in patients with difficult diseases such as heart diseases, immune diseases and cancerous tumors, and this field is called personalized medicine.
c) Drug Discovery
The drug discovery process is very cumbersome and very costly in terms of material and time. The average cost of developing a new drug ranges between 1.3 and 2.8 billion US dollars, and the process usually takes 10 to 15 years, so it was necessary to speed up this process and reduce its cost.
And this has already been done through data science that has helped reduce the time and cost by using simulation methods that filter out a number of chemicals that could be medicine for the disease we are dealing with, and we are trying to discover an effective treatment for it.
It may also give us an idea of the disease's response to this drug and the way the body deals with the chemical of the drug, and thus you can reduce the number of drugs that are tested on animals and humans, making this process faster, faster and much less expensive.
These technologies have contributed to a huge boom in the discovery and improvement of medicines, and have helped accelerate the discovery and development of vaccines for the Corona virus.
d) Predictive Modeling for Diagnosis
Using the abundant data available on the condition of patients, their ages, and their fate in terms of the development of their condition for better or worse, we can build computer models that are used to predict the patient’s condition to see possible complications and try to anticipate them, especially in complex diseases such as heart, liver and tumor diseases.
These models help us to better treat the patient or at least improve his condition during his life if this disease is chronic, and to know effective prevention methods that will help prevent many people from contracting this disease.
e) Monitoring Patient Health
With data science and the Internet of Things , we can monitor patients' health in a more effective way, whether they are hospitalized or not, through devices that track the patient's condition in terms of blood pressure, heart rate, hours of sleep, calories, and others.
This tracking helps doctors better understand the patient's condition, it also makes it easier for us to monitor him and monitor his progress, and sometimes these devices help to rescue patients in emergency situations by calling an ambulance automatically.
f) Tracking and Preventing Diseases
This field has emerged and its importance has increased due to the Corona virus, as many countries have developed statistical models that help them contain the epidemic, by tracking the areas in which it appears, and through data and statistics, they assess the risk of this outbreak to help take the best measures.
With this, you can determine whether it is better to do a comprehensive quarantine and isolate the region or not, which saves thousands of lives, saves countries tens of millions of dollars, and avoids the many potential dangers of an outbreak.
j) Medical Virtual Assistance.
Imagine an app like Google Assistant, Amazon's Alexa, or Apple's Siri interacting with you, but in the role of a doctor, asking him questions about your condition and the medications you take, or even telling him your symptoms so he can diagnose your condition.
Well, this is not science fiction. Using Natural Language Processing, we can program chat bots or virtual assistants that can help patients, and even track and evaluate their condition periodically even if their ailments are psychological rather than organic.
Second, applications of data science in the field of banking services
The banking sector is one of the sectors that benefited the most from the development of data science, as human interaction with all the data of its customers is almost impossible, and the slightest mistake in its operations will cost it a lot of money, and most importantly, the trust and reputation of its customers.
Data science helps banks manage their resources effectively, provides them with the information needed to make decisions, contributes to fraud and manipulation detection, customer management, risk management, customer segmentation, CLV, recommendation systems, analytics, and let's address the most important of these applications in detail:
a) Fraud Detection
Any fraud or manipulation greatly damages the reputation of the bank and makes its customers lose confidence in it, which is what prompted banks to use data science and machine learning in detecting fraud, and this is done by identifying any manipulation or strange patterns in bank transfers.
Through the history of your transfers and banking transactions, the model or algorithms can determine if something unusual is happening, a transfer or abnormal activity, and therefore can deal with and prevent this fraud.
b) Risk Modeling
When dealing with billions and trillions of dollars, there must be strong models for forecasting and risk management, because any loss will be very costly and painful for the bank, and also because any profit will be a big step forward for this bank, especially if its investment in it is large.
Banks are now using smart computer models capable of evaluating risks, through which the decision is taken to make this investment or not, or whether the bank gives the owner of this project money or not, and this is according to the many data about the performance of the project and the performance of similar projects, and the economic factors that must be taken into account account.
c) Customer Segmentation and Customer Lifetime Value
Through the many data they collect from their customers, banks can divide them into segments according to behavior, personal characteristics, spending patterns, income rates, and other classifications in order to target them with the appropriate services for them and develop services periodically, which will help strengthen the relationship between them and the bank.
The use of data science through predictive models also helps determine the return from customers, specifically the value of the customer for life, or the duration of his dealings with the banking institution to achieve the largest possible profits from it.
Third, data science applications in the financial sector
Data science has done very much for the financial sector in general, from facilitating Automation to automate the majority of its routine processes, to developing fraud detection and management and forecasting systems for various financial risks.
Data science has also facilitated decision-making processes through the various analyzes it performs, such as: customer analytics, risk analytics, customer sentiment analysis and their comments on financial services, and many more, making decisions supported or directed by results.
In this article, we will address the most important applications of data science in the financial sector, namely:
a) Risk Analytics
When it comes to billions and trillions of dollars, risk management becomes one of the most important elements of decision-making, which is why financial sectors spend a lot of money to develop the best risk management and analysis models.
Using massive data on remittances and customers, these models can determine whether or not to invest in this idea or project, and algorithms provide improvements that help increase profits or reduce losses.
b) Consumer Analytics
Using the vast amount of data available from the business sector, financial sector companies can easily conduct analyzes on existing data, providing them with the ability to deal with and address current problems, or at least reduce losses as much as possible.
One of the most important analyzes in the financial sectors is user analytics because, in addition to what we said a while ago, it is able to provide a personalized experience for its users, which increases the link between them and the company and which increases its profits.
Personalized services are one of the best ways to win over customers at this time, and they are very effective in increasing corporate profits, in addition to allowing the development and innovation of new services that suit customers.
c) Financial Fraud Detection
Fraud is one of the biggest problems in the financial sector since ancient times, so the financial sectors have developed ways to track fraud and manipulation using big data and analyze user data especially credit card fraud.
d) Automated trading or algorithmic trading
Algorithmic trading is one of the most popular applications in recent times, as it provides a great service to investors who are pumping large funds into several stocks or projects, and this service is to facilitate the decision-making process through very complex calculations to predict the state of the financial markets.
Fourth, data science applications in retail
Retail is one of the most profitable fields in the whole world, and you can ask the richest man in the world Jeff Bezos about this, and data science has helped this field a lot in the last decade, until there is a lot of competition in it to have the best technologies for dealing with data.
Data science helps retailers discover their customers, analyze the services and products they want, discover how to make them buy more, and analyze their sentiments. In addition, many applications help manage the supply chain and determine the best pricing strategies.
Among the most important applications of data science in the field of retail:
a) Personalized Marketing
With the massive data that customers and buyers leave behind, big retailers like Amazon can tap into their customers and find out what drives them to buy, simply through their browsing history, past purchases, and product reviews.
All this helps these giants to carry out highly personalized marketing campaigns, and to provide marketing messages that are irresistible to their customers, providing more usable information in marketing, and so this cycle keeps repeating and repeating.
b) Personalized Recommendation
The algorithms of these companies’ websites and applications can predict user preferences and behavior towards products, thus they can provide them with products they are likely to buy when they search for what they want, or even predict what they are likely to buy and market to them through advertisements without them knowing. even looking for it.
Filtering algorithms based on deep learning have been one of the main reasons for the increase in sales of major retail sites, and they also make the relationship between the user and the site or company so strong that the user feels that the site from which they are buying truly understands them and their desires.
c) Customer Sentiment Analysis
This branch of data science has become very popular, as it allows users to analyze their experiences with different products through their product reviews or even through what they write on social media, which provides companies with a lot of valuable data for free.
This technology relies on Natural Language Processing (NLP), in order to be able to understand the textual comments written by customers, and then put them in their own context, whether they are positive or negative.
d) Supply Chain Management
Data science can contribute to a very complex area of retail, namely inventory and supply chain management, where algorithms can predict the type and number of products customers will purchase in a given period of time, helping to order and store products efficiently.
Fifthly, data science applications in the field of transportation
Data science is widely used in the field of transportation, as it contributes daily to saving thousands of lives and providing a safer driving experience by analyzing road, driver and accident data, and trying to discover the factors that cause or reduce road accidents.
That's why governments in developed countries are trying to collect as much data about driving, even fuel consumption, because tracking, detecting and understanding patterns effectively contributes to making roads safer and more realistic driving laws.
Also, with the help of data science, we have seen a development in recent years in the field of self-driving cars , which in the future will eliminate accidents completely, and will make driving just a routine thing that artificial intelligence does.
Private companies working in the field of transportation and transportation use data science heavily as well, for example, Uber, which uses data science to predict the price of a trip through several factors, such as: road congestion, weather factors, availability of drivers, … etc.
Sixth: Data science applications in government fields
Governments of developed countries rely mainly on technologies such as data science and artificial intelligence , through which they can make data-driven decisions, which have a much better impact than decisions based on theoretical evidence.
Governments are also expanding the use of data science in all walks of life, even in the detection of tax evasion as they compare the income provided by people with their financial statements and their social media accounts to ensure that this income matches their spending rates.
Data science is also used in response to crises, whether natural or national security, and is used in several ways in addressing cybercrime and hacking attempts.
Conclusion
Data is the oil of the twenty-first century, and it is the real wealth that everyone seeks to possess today, because through it, miracles can be achieved by achieving huge profits, or even controlling individuals and directing them according to the desire of those who possess their information.
Data science is the tool that deals with this wealth, and it is the basis for all modern fields, such as: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision and many others.
In this article, we discussed the applications of data science in six important areas, but the applications and uses of data science are limitless, and they are almost present in everything around us
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