6 Most Used Data Science Applications With Case Studies
In today’s world, data holds great importance. In order to handle and utilize this data, Data Science is helping the world with its various Data Science applications. Data Science makes use of different processes and algorithms for finding the insights in the data.
It has an impact on most of the activities in our daily lives. Data Science is an umbrella that provides various resources for solving any type of problem. As a result, many leading organizations are relying on data for various purposes. They are:
- To understand various trends and patterns in the data.
- To take some data-driven business decisions in order for their betterment.
Data Science plays an important role in many different fields such as E-commerce, Finance, Transport, Gaming, etc and has a wide range of applications.
In this article, we will explore some of the really important Data Science applications that have brought revolutionary changes. With Data Science applications you should also have knowledge of Data Science tools.
For that, check out TechVidvan’s Data Science Tools article.
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Data Science Applications
In today’s competitive world there are many industries that are using Data Science for effectively handling large data and achieve growth in their industries. Let’s discuss the applications of Data Science in various sectors.
Do you know? Data Science is of huge importance in the E-commerce industry. Data Science applications help enterprises for making predictions about profit, loss, and sales.
Companies also use Data Science for influencing customers. So that the customers buy their products by using the customers’ data for evaluating their needs and interests. Some of the ways in which Data Science is affecting the E-commerce industry are:
A. Whenever we search for any product over the internet, it starts giving us suggestions about some similar products. The companies are using Data Science for promoting their products according to the user’s interest through the recommendation system. These suggestions are mostly based on the users’ previous search history.
B. Customers’ sentiment analysis enables the company to predict the reviews of the users on their products. Various complex Machine Learning algorithms help to analyze the data present on different social media platforms to perform customer sentiment analysis.
It will help the companies to identify the customers’ views about the product. This feedback will help them to improve their products.
C. Data Science is also used to calculate the right price of the products by considering the different market trends, user response, etc.
Today, Data Science applications in the Healthcare Industry are achieving new heights and there exists a treatment for every disease except for some rare cases. This advancement of healthcare is somewhere the result of the use of various modern types of equipment.
These equipment generate a large amount of data related to health. This is where Data Science can help to take some important data-driven decisions. There are several ways in which Data Science is helping the healthcare Industry. Some of them are:
A. Predictive Analysis
What can be better than knowing the future? Yes, you heard it right. Data Science is used to analyze the patient’s data. Like previous medical history, current symptoms, drugs in the body, etc to predict the effect of the disease and the treatment on the patient.
It can be helpful in predicting the pattern of evolution, of the disease and reducing risks and undesired outcomes accordingly.
B. Genomics – Treatment Personalization
This is one of the best advancements in the healthcare industry. By gaining insights about the patient’s genetic structures and genomics, the Doctor can have a better understanding of what will be the best treatment for the patient.
C. Medical Image Analysis
Medical Image Analysis can make a considerable change in any patient’s treatment. Various tests like X-ray, Magnetic Resonance Imaging abbreviated as MRI, computed tomography, etc are improved by using image analysis.
This image analysis can help to better understand the problem. Various Machine Learning algorithms are used for this purpose.
D. Drug Discovery
The current technology and processes that we are using for drug discovery are very inefficient, time-consuming and costly. The journey of a drug from research to making it available in the market involves an average expenditure of 2.6 billion dollars and more than 10 years of time.
Using Data Science, we can overcome these drawbacks. Data Science helps us to get the early insights into the drugs easily.
There are more than 2 billion gamers all over the world and the industry has given a new medium of entertainment for people from different age groups. The applications of Data Science in the gaming industry have great scope. They are using Data Science to provide a better gaming experience to the users.
With a large number of users worldwide, the gaming data is also increasing. Some of the ways in which the gaming industries are using this data are:
A. Data Science plays a very important role in the entire development of a game. It helps in gaining insights from the data to design the games in such a way that it keeps the players engaged.
B. Game Monetization is another Data Science application. The development of most of the popular games involves a lot of time and money.
Thus, the companies are using Data Science applications to make this development process profitable for them. Also, to design such interesting games that convince people to pay for playing.
C. Visual effects and graphics play a very important role in gaming. Data Science is used to enhance the visual quality of the games. Attractive visuals and graphics in the game engage people more effectively.
D. Data Science can also be used for fraud detection. Also, to keep a check on the identification of hackers or any other suspicious activities.
4. Data Science in Energy Industry
Today, we are using different sources of energy like oil, gas, solar energy, hydro energy, etc. We have come across many smart ways of efficiently utilizing these energy resources. The Energy Industry is a continuously growing industry.
Along with other industries, the Energy Industry is also using Data Science applications on a wide range. The Energy industry uses Data Science to provide optimal cost and investments and also helps in minimizing the risks.
Data Science can help the Energy industry to discover new energy sources, increase efficiency and productivity, remove the gap between the supply and demand, better maintenance, provide effective safety measures for the public by proper observation, effective resource utilization and much more.
For example, if the Electricity provider gets the information about the high winds and stormy weather in advance then they can minimize the effects. By taking some preventive measures such as trimming the trees nearby the power line more frequently than the usual.
Data Science applications are used for extracting actionable insights from the data that might reduce energy expenses. It helps to predict the events or problems that might happen in the future and take appropriate actions.
The growth of the Travel Industry is on the rise these days because people love travelling and exploring the world. The reason behind this is that now a wide range of audiences can afford it.
With increasing customers, their needs are also increasing. Many booking websites for airlines, hotels, railway reservations, etc are using Data Science for providing better services to customers. Several areas in which the travel industry is using Data Science are:
A. The Industries are using Data Science for providing personalized services by dividing the people into different groups based on their interests and preferences. Data from various sources such as Social media is used for this purpose.
B. Customer Sentiment Analysis also plays a significant role in the Travel Industry as well. Various Machine Learning algorithms are applied to textual data from different social networking platforms.
This analysis helps the company to identify the real attitude of the customers towards their products.
C. Route Optimization is of great importance for the travel industry. Data Science is used to provide the optimal travel costs, time schedule, and route for the customers.
D. Travel bots is a technology which is enhancing the customer travel experience by providing suggestions for the different problems in their entire journey.
Finance Industries are applying Data Science applications to use the Customers’ data. So that they can take some important data-driven decisions for designing effective business strategies.
The success of the different financial companies from all across the world depends on the use of cutting edge technologies. Financial Industries are using Data Science applications for effective investments in the market full of competition. Some of the areas in which the Financial industries are using Data Science are:
A. To grow in this field, the traders, vendors, investors, etc need to make the right decision at the right time. Many Machine Learning algorithms can be used for real-time and predictive analysis.
The aim is to analyze a large amount of data available in the market to predict the behaviour of the market in the near future. These predictions give an idea about the stock market rates, prices, risks, etc.
B. Another area of Data Science application is Fraud Detection. Data Science and Artificial Intelligence play a very important role in this. The primary concern of these financial institutions is to provide security to the customers. Otherwise, there may be serious financial losses.
With the help of Data Science, the companies are coming up with services that keep a check on unusual or unauthorized financial activities.
C. The various Financial companies have realized, that to stand out in the market they need to maintain personalized relations with their customers. They are using Data Science for building models for evaluating the customers’ data and recognize the situations in which they need financial advice.
Data Science Case Studies
After exploring the different Data Science applications, let us see some of the case studies that will illustrate how Data Science plays a significant role in the betterment of these fields.
1. Transport for London – Data Science Case Study
In London, there is an organization named Transport For London (TFL) that looks after the connectivity of trains, cars, and other means of transport. The current population in London is more than 8.5 million.
Lauren Sager Weinstein, the Data Analytics Head at TFL realized the importance of Big Data to provide better services to all the travellers by efficient planning of different transport services. The data for this purpose is collected from different sources such as ticket systems, maps, etc.
TFL used this data to improve their services and provide 24×7 comfort to the passengers. It also provides online services to travellers. TFL analyzed the collected data in many different ways and came up with such meaningful insights that helped them to manage traffic effectively and discover alternate routes.
They also managed to convey the information suggesting alternate routes to the passengers via social media or custom messages.
2. American Cancer Society – Data Science Case Study
One of the research performed by the American Cancer Society in 2017 states that nearly 1.7 million people in the US were expected to be diagnosed with Cancer. Over 600 thousand people died because of Cancer, that is, 1650 per day. Importantly a huge number of these cases can be easily prevented if people would change their behaviour.
For example, by stopping smoking, eating better or by using sun-screen. This lets you know that there is a space for research in terms of things that can predict Cancer. It also provides a better understanding of how Cancer operates and how Data Scientists can help Cancer Physicians to develop better methods of treatment.
The early detection of cancer is critical. Some common forms of cancer such as Breast Cancer, Prostate Cancer, etc have a survival rate of around 100% if they are caught and treated early, that is, for the stage one but the survival rate is 30% if they are caught in stage four, that is, the most serious stage.
As a result, Data Scientists are working closely with medical researchers to find methods to identify Cancer as soon as possible with chances of improvements in the survival rate for everyone.
University of Toronto Research
For instance, research at the University of Toronto has worked with the development of a microchip that can detect the basics of many forms of cancer such as Brain Cancer, etc which are difficult to detect until they become too advanced. Developing this chip might find ways of sorting through the data within the chemical sample from the blood to give people a better clue on how they can recover sooner.
Also, researchers from the field of Computer Science and Artificial Intelligence are developing Machine Learning models to differentiate between various forms of cancer to ensure that patients receive the right treatment.
They are not only improving accuracy with their model but they are also making special efforts to ensure that their model is transparent and convenient by practicing it with physicians.
Data Science has also contributed to important advances in the Science of Cancer, that is, the research that gives a basic understanding of how the disease operates and how we can defeat it. It combines insights from the data with the Data mining of existing drugs to find effective drugs for cancer treatment.
Data Science also helps the doctors to schedule the treatment based on the patient’s data. Providing the right treatment at the right time is the most important and difficult thing. But, Data Science has helped by improving the existing treatments and making them more available for the patients.
Thus, Data Science has grown unbelievably and has significant effects on almost every field. Various industries like Finance, E-Commerce, Travel, Healthcare, etc are using Data Science to meet the requirements of the market as well as the users.
The industries are using Data science for improving their products and for producing the most profitable results by using a large amount of data available.
I hope you find this TechVidvan’s Data Science applications article helpful. If you have any doubts, ask in the comment section.