The relevance of Data Science application is undoubtedly one of the most important things that have happened in the technology space. Faster computing and cheaper storage brought in by cloud technology has enabled Data Science to its maximum potential. With Data Science application to predict outcomes in record-time within minutes, which would otherwise take hours or even days for humans to do.
Right now, Data Science has dominated almost all the industries around the world. There is hardly any industry in the world that doesn’t use data. And any industry which uses data is totally ready to scale up to the next level by using Data Science.
We will be touching upon numerous industries and would give a look at how data science applications have transformed these businesses.
- Data Science applications are accelerated due to the easier and cheaper availability of cloud computing power.
- Using Data Science, organizations have the potential to predict business activity outcomes in a few minutes.
- Data Science has introduced higher efficiencies related to issues like fraud detection, risk modeling, etc to provide better service to the customers.
- In the manufacturing domain, Data Science provides real-time optimal solutions for inventory management and having granular control on automation for production.
- Application of Data Science in the medical domain includes genetics, natural language processing, and drug discovery. All these fields used to be relatively stagnant before Data Science came into the fray.
What are the most popular data science applications?
The list below shows the industries which actively use data science applications to gain maximum utility.
In the Banking domain, the data science application has been one of the biggest of all times. Big Data and Data Science have enabled banks to do so much more and to stay competitive at the same time.
All thanks to Data Science, banks are able to manage their resources more efficiently. It also enables banks to act upon issues like fraud detection, risk modelling, management of customer data, real-time predictive analytics, etc through smarter decisions.
Using Data Science, banks can assess customer lifetime value reliably. This helps the banks to monitor the number of customers that they have and to generate various predictions which can be derived from them as businesses.
As for fraud detection, banks allow companies to detect frauds and fraud attempts involving a credit/debit card, insurance, and accounting. Data Science is also used to analyze investment patterns and to forecast cycles of customers. All of it can go into strategizing offers to customers which can attract businesses accordingly.
Risk modeling has become so much better with data science as it uses real-time and predictive analytics to further the model itself through overall performance. It allows banks to use real-time analytics to understand the intrinsic problems which obstruct their banking performance.
It also provides enough actionable insights through data science applications in terms of tailored, personalized marketing campaigns for potential customers.
In today’s time, it wouldn’t be wrong to consider data scientists to be the next generation of advanced factory workers. It equates data scientists to be holding a key position in manufacturing industries and companies.
Mostly, data science is used to an extensive degree in manufacturing industries for the optimization of production processes and to boost profits by reducing costs.
With the boom in the Internet of Things or IoT technology and devices using the same, data science has enabled organizations to forecast potential problems. All of it is made possible through IoT-enabled monitor systems and through continuous streaming data from these systems.
Similar systems can be deployed using data science to monitor their energy consumption costs and to optimize the production hours to their fullest.
On the customer end of things, data science enables us to do a thorough analysis of their reviews and to give proper recommendations to help make better business decisions and to improve the quality of their products.
Data science also reinforces automation and its efficiency and its functionalities. By utilizing historic and real-time data, industries can develop automations and autonomous systems which are helpful in boosting productivity of manufacturing lines.
Because of the same, it has powerful machines which use machine learning technologies like reinforcement learning to interpret and act efficiently enough.
The application of data science in healthcare industries is doing significant work and they offer immense functionalities. The specific industries and areas in healthcare that extensively uses data science is-
Genetics requires the application of various statistical techniques for genomic sequencing. Data Science provides the application of the same in a usable and efficient manner. This allows bioinformaticians and geneticists to get a better insight into various genetic structures. All of these are quite helpful in classifying diseases with genetic elements behind their cause.
ii. Drug Discovery
In drug discovery, everyday new medicines are formulated for various diseases or ailments. But it is a tedious and complex process to go through. Data Science simplifies this process through predictive analysis and to give us an estimation about the efficacy of success rates of newly discovered drugs. We can also analyse various combinations of chemicals and drugs and their effects on numerous gene structures to forecast its outcome too.
iii. Natural Language Processing
Probably the most well-known Data Science application, Natural Language Processing technology analyzes textual information. Using this, the creation of intelligent bots to answer user queries becomes a reality. Its application can be noteworthy in the healthcare sector in terms of the creation of bots that answer questions of patients and provide them with the right diagnostics guidelines.
E-commerce industries have been one of the topmost beneficiaries of data science applications. Some of these applications of the same which has essentially evolved the e-commerce industries are as follows-
- Identification of potential customer base
- Utilization of predictive analytics to forecast goods and services
- Identification of styles of popular products and prediction of their trends.
- Optimization of pricing structures for customers as per their needs.
In e-commerce websites, the advanced recommendation system heavily relies on collaborative filtering. This is mostly provided using data science, which enables the system to provide insights to the customers based on their historical browsing and purchases.
It also factors in other customers who have bought the same products and includes items purchased along with this main item too. These kinds of hybrid recommendation systems including both content-based and collaborative filtering helps industries to provide better quality of services and products to their customers.
There has been a huge impact in the transport domain by data science. Data science applications are moving towards making a safer driving environment and systems for the drivers. It also provides optimization in performance and to enable greater autonomy to the drivers.
Data science was the primary reason as to how some companies were able to come up with self-driving cars and its effectiveness actively increased manifold in the past few years.
By analyzing fuel consumption patterns, driver behaviour and active vehicle performance monitoring, data science has been able to create a strong footing in the transport industry. This is how it is all enabled to come up with self-driving cars by incorporating all the mentioned metrics using data science applications.
Games are now developed and designed using machine learning algorithms which evolve themselves as the player moves to a higher difficulty level. This provides a quality and seamless experience for gamers. In terms of motion gaming, the computer analyzes the gamer’s past moves, and using that, it can shape the current game. Famous game producers like EA Sports, Sony, Nintendo, Blizzard have adopted wholeheartedly to inculcate data science in developing their games.
To wrap things up, data science has been a major factor impacting all the mentioned domains in terms of applications. Various industries like banking, transport, gaming, e-commerce, healthcare, and many more are utilizing the potential of data science to better their products and services.
Data Science is a massively vast field that takes a lot of effort to learn and master the same. Its applications are also enormous, complex, and diverse. Industries need to have a lot of data before moving forward to take up data science as their potential solution as it would provide the much-needed inputs to make it all work.
This obviously makes data an essential aspect of all industries to pay attention to as to how to manage data and what all data is required for data science solution purposes.
If you are wondering how to become a data scientist, you can start off by learning more about technical tools like SQL, programming languages like R or Python, etc. Hope you liked reading our article.
Frequently Asked Questions
What is Data Science?
Data Science is a combination of various tools, algorithms and processes to discover hidden patterns out of a set of raw data.
What is correlation and covariance in Data Science?
Correlation- It is considered for measuring the quantitative relationship between two variables.
Covariance- It shows the extent to which two random variables change when one is changed while keeping the other constant.
How Data Science differs from Big Data?
Data Science uses various methods for data modelling and other data pertaining to tasks like data cleansing, pre-processing, data analysis, etc.
Big Data uses enormous amounts of data which can be structured, unstructured and semi-structured obtained from various data sources.
How do Data Scientists use Data Science?
Data Scientists use statistics to be able to use Data Science. They use a lot of techniques and algorithms to find structure in data and to get a deeper insight into the data.
What is Data Cleansing?
Data Cleansing is a process where all of the data within databases is either removed or updated if they are incomplete, incorrect, duplicated or obsolete.