Top 10 Data Science Applications

Top 10 Data Science Applications

7 mins read822 Views Comment
Rashmi
Rashmi Karan
Manager - Content
Updated on Nov 22, 2022 19:27 IST

We generate a lot of information every day and the evolution of technology has led us to improve the information extraction processes using increasingly complex processes and systems to obtain valuable information from these data. In this article we have mention 10 applications of data science in real-world.

2022_02_Data-Science-Applications.jpg

The data by itself is not valuable; the valuable thing is to process it in the form of valuable information, making data the gold of the 21st century. Data science is a constantly evolving discipline with data science techniques continuing to address complex business and industrial problems. Data science techniques are a core part of business operations and help to wrangle and analyze huge data sets to come to a defined solution. In this article, we explore some of the data science applications across different industries, and how data science contributes to solving business problems for those businesses.

Content

1. Cybersecurity

Data science helps in the identification of cyber threats. We can feed such user access data to the systems and network resources and it further searches patterns for any anomalies. It then gives an alert when it detects situations that do not respond to a predefined pattern.

2022_02_image-56.jpg

Popular approaches used in cybersecurity are –

  • Machine Learning Algorithms – Help to detect anomalies in an information security environment
  • Penetration Testing – A methodology to protect data and data structures
  • Behavioral Analytics – Contributes towards detecting and identifying cyber-attacks and other malware

Data science techniques lead to data-driven intelligent decision-making from security data for smart cybersecurity solutions. This data comes from activity logs, with overwhelming amounts of data collected in historical archives. From them, we can extract activity patterns for use as a reference.

A similar process of fraud detection is being used in finance where the role of data science is evident in transactional detecting fraud in credit card payments. Here, the systems can cross-reference data from different sources, such as the usual activity of a client, together with the “normal” ones of use.

2. Finance

2022_02_image-55.jpg

In this way, it is possible to identify fraudulent scenarios like duplicity, stolen cards, wrong or improper/duplicate charges), paralyzing, or warning about the irregular activity before the damage occurs. Some popular data science tools used in finance are –

  • Mathematical Models – Use large amounts of financial and non-financial data to identify patterns and provide enriched results to devise better strategies
  • Business Intelligence – Make financial decisions and investment strategies
  • Financial Analytics – Help in designing novel investment strategies using systematized quantitative approaches
  • Anomaly Detection Algorithms – Help in identifying and flagging suspicious transactions
  • Clustering Algorithms – Collating suspicious transactions

Also Read –  Top Industries Hiring Data Scientists in 2022

3. Insurance

One of the most talked-about data science applications is in the field of insurance.

2022_02_image-54.jpg

By analyzing driving habits using sensors, an insurance company can calculate a customer’s accident risks and offer a personalized quote for him. You can even introduce variable concepts that depend on the analysis of your routines at different times of the year. using different tools like –

Data Mining – Find helpful patterns from consumer data

Text analytics, Predictive Analytics, Behavioral Analytics, Pattern, Graph, and Link Analysis Techniques – Detect behavior patterns that allow knowledge of customers, facilitating customized services; Fraud detection; Facilitate more effective loss prevention

Statistical/Mathematical Models – Model frequency and size of losses and claims, using a user database

4. Healthcare

Data science has helped the healthcare industry by managing humongous volumes of data. Data science has now paved its way across different avenues of medical care, including tasks like –

  • Patient data management
  • Hospital data management
  • Medical image analysis
  • Vaccine development
  • Drug discovery
  • Sizing patient population
  • Genetics and genomics
Data Science in healthcare

Machine learning, artificial intelligence, data analytics, deep learning, etc can sort massive medical data in a digital environment. Here are some used cases of data science in medicine –

  • Use of predictive models to identify high-risk patients, for example, type 1 diabetes.
  • Classification of disease subtypes to select targeted and more precise clinical treatments. E.g. cancer
  • Provide better information for candidate screening in clinical tests of drugs and treatments
  • The same applies to discovering new drugs or offering personalized treatments.

Check Out Our Data Science Courses

5. Renewable Energy

Data science techniques have many applications in the renewable energy field; particularly deep learning models can predict environmental or climatological conditions that are important for power generation. In addition, these techniques have shown the potential to predict environmental conditions with the new difficulties of climate change.

data science in Renewable Energy

Additionally, over the years hydroelectric generation has been successfully modeled using stochastic models. However, the amount of rainfall and water levels in the reservoirs have changed a lot recently due to climate change, therefore traditional models have decreased their accuracy. Deep learning models have proven their efficiency in tackling climate change.

Must Read – Emerging Trends and Technologies in Data Science for 2022

6. Digital Marketing

Businesses are now extensively using data-driven methods and tools to create, access, manage, store, and analyze information obtained from consumer data. Data scientists use data mining tools to collect information about e-commerce transactions or clicks on web pages. They then add the collected data to analytical models that generate predictions about future behaviors. These can help companies make informed decisions, such as pricing products in the market based on anticipated demand and cost-benefit analysis. Through these marketing strategies, companies can target their customers more precisely because they have real-time access to customer buying patterns and consumer behavior.

2022_02_social_media.gif

Data science also contributes to analyzing the realm of human communications and interpreting patterns and behaviors. Some of the below techniques can help marketing managers observe the user behavior –

Data science has made it is possible to predict the demand for a product or service from offers segmented by social class, cultural preferences, purchasing power, gender, hobbies, etc.

7. Predictive Maintenance  

Predictive maintenance is a clear example of the application of Data Science in the industry. Manufacturing businesses often face the issue of ensuring maximum availability of critical assembly line systems, managing their working condition, reducing costs, and upkeeping them. Data science can help to identify these challenges in due time and avoid any equipment failures to happen.

The machines, logistics systems, and other elements of an industrial plant integrate thousands of sensors that collect data on temperatures, hours of operation, speeds, distances, noise levels, etc. With more and more equipment connected to a central server, detection of faults gets easier and more effective.

2022_02_image-53.jpg

Predictive maintenance facilitates tasks like failure prediction, diagnosis, and classification, and throws the right recommendations to avoid or overcome those challenges. As a result, businesses can ensure substantial savings in the amount spent on regular check-ups or buying spare parts. Not to mention preventing a production plant from stopping by surprise.

8. Image Recognition

Take Google Photos as an example. It automatically analyzes and classifies the photos we upload. This classification is based on elements that Google’s AI is capable of identifying, whether they are cars, planes, people, flowers, food, animals, landscapes, or unique places, among others. Once the user uploads the image or video, the AI tools read the image and extract information from it. Advanced image recognition is highly useful in industries like automotive, gaming, e-commerce, etc. It has applications in –

  • Face Recognition
  • Object Detection
  • Text Detection
  • Pattern Recognition
  • Logo Detection
  • Landmark Detection
GitHub - dnmanveet/Dog-Cat-Classification: This is simple image classifier  of dogs and cats. This model classifies images based on their particular  breeds.
Source

9. Aviation

The aviation industry uses data science and artificial intelligence in a variety of ways –

  • Analyze and predict consumer demands, thereby adding to the revenues
  • Offer demanded flight routes to customers basis data sources like search history and macroeconomic factors
  • Identify risks and propose solutions to ensure passenger safety
  • Use distribution channels and market analysis to improve and automate customer services
2022_02_20571152.jpg

10. Predictive Analytics

Autocomplete is a feature that allows an application to predict the rest of the word or sentence types by the user. Predictive text generation works on various concepts like data science, machine learning, NLP, and deep learning to achieve higher accuracy basis user behavior.

2022_02_image-58.jpg

Conclusion

Data science offers immense opportunities for the business to transform and evolve, and make better decisions, which thereby contributes towards better revenues. Successful data science applications have helped small-medium enterprises achieve solid growth in recent years. The forthcoming years will see more investments in data science and related technologies by businesses to gain critical insights and valuable information, and make the most out of the available data.

————————————————————————————————————–——–

Top Trending Articles:

Data Analyst Interview Questions | Data Science Interview Questions | Machine Learning Applications | Big Data vs Machine Learning | Data Scientist vs Data Analyst | How to Become a Data Analyst | Data Science vs. Big Data vs. Data Analytics | What is Data Science | What is a Data Scientist | What is Data Analyst

About the Author
author-image
Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio