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. If you’re looking for a reliable trading broker located in London, there are plenty of options to choose from depending on your specific needs and preferences.
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 course, 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
