Top 13 Data Science Applications in 2022

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Data science is a term that encompasses the usage of data and its analytics and computational techniques to draw insights from data. Rapid globalization has enabled us to deal with an even greater volume of data.

Data science technologies help draw insights from large, unstructured datasets. These insights help streamline business operations and achieve organizational goals.

Since data is everywhere, from healthcare to the business sector, data science technologies can apply to several fields.

This article outlines 13 applications of data science.

Banking and Finance

Banking and financial institutions are the forerunners in using data science technologies. Data science is rigorously used in the banking sector, from monitoring financial activities to pricing financial services and products. For example, technologies like risk modeling help banks detect risks like if a customer would default by overviewing their credit history. This practice is helpful when banks review loan applications. Investment banks also use risk modeling to detect risky assets to warn customers.

Considering the volume of customer data, banks have to employ big data strategies to collect, analyze and interpret customer data. Machine learning (ML) and natural language processing (NLP) technologies are used explicitly for customer data management. In addition, data science techniques like logistic regression, clustering, and decision trees help filter out irrelevant data and gain valuable insights from the relevant data. 

Retail stores

Retail stores have access to large amounts of consumer data, like their purchasing history or likes and preferences. With the help of data science technologies like SQL, retail corporations like Walmart can structure data into databases. But, you might be thinking, what is an SQL? SQL is a tool for processing and understanding large structured datasets. It can also be integrated with other data science tools like R and Python.

Retail organizations use data science tools for streamlining logistics and supply chains. Sentiment analysis of consumer data also aids in marketing in-demand products.

Fraud and risk detection

Large sets of unattended data are at an increased risk of malpractice. The banking and finance sector is a common target of fraudulent practices. Previously the finance sector relied on paperwork for collecting data. As a result, analytical strategies were slim to none. Now risk and predictive analysis help detect credit card fraud and illegal trading. Investment banking also benefits from data science for better portfolio management. Taxation authorities also use predictive analysis and clustering for assessing tax returns and detecting fraud.

Airlines

The airline industry uses data science tools for planning better routes and analyzing flight data. Feedback from consumers also helps in improving the quality of service. Some airline corporations have successfully scaled their revenue by utilizing data analytics.

Criminal investigations

Criminal investigations involve a lot of data but very little time for analyzing it. Data science tools like machine learning make it easier to identify criminal records and speed up investigations. In addition, artificial intelligence (AI) used in facial recognition systems makes biometric identification easier.

Image recognition

AI-powered image recognition is perhaps the most widely used data science tool. It has made biometric authentication easier. Through facial recognition, machines are programmed to detect objects and shapes. This has particularly helped with accessibility for people with disabilities.

Manufacturing

Data science applies to the manufacturing industry in several ways. First, data science tools help collect valuable data from industrial processes. Analysis of this information helps minimize costs, increase efficiency, and generate profits. Mainly data science is used for quality assurance, inventory management, creating energy-efficient solutions, and minimizing machine failure.

Game Development

Game development is more than a catchy storyline and vivid graphics. User data analysis is necessary to keep players happy and interested in the game. Insights from user data can help enhance game features like why users quit the game after a certain level or which colors seem appealing. In addition, these insights signal developers and marketers to redesign or rebrand the game for maximum downloads and better revenue.

Healthcare

The healthcare sector relies on abundant data in patient records, drug information, and data from medical devices. In addition, research and development for developing new drugs or devising new treatment plans require data analysis. Specific AI and ML tools have been used for discovering new drugs or enhancing clinical drug trials.

Genetics and Genomics Research

Research relies on data science technologies due to their common ground in statistical knowledge and computational techniques. Genome sequencing has benefitted from tools like MapReduce. Biotechnicians can better understand the impact of certain drugs on genes.

Transport

The evolution of cars into self-driving machines is considered the most significant achievement of data science. Other applications are finding fuel consumption trends, route planning, and vehicle monitoring. Through data analysis, authorities can identify network congestion and poor routes to prevent accidents and traffic. Services like Uber also use data science to filter out customer data like their profile, location, logistics, and other variables for pricing and finding better routes.

Education

Apart from being a multi-disciplinary field that offers abundant academic benefits, data science has plenty of non-academic benefits for the education sector. For example, through data science, we have created learning management systems (LMS) that help large educational institutions to manage student data and progress efficiently. Not only that, institutions can assess teacher-student interactions to enhance the learning process.

Marketing is the backbone of any business corporation. However, simply marketing a product with catchy headlines and striking imagery isn’t enough. Now consumer data can help understand what the target audience wants and, more importantly, who the target audience is.

Data science tools like AI and ML are widely used in analyzing data from users’ social media or past purchases. Social media platforms and search engines display ads based on the keywords you enter into their system to give you a personalized ad experience.

Content creation goes hand-in-hand with advertisement. AI-powered tools have enhanced content creation and streamlined productivity for marketers and creators.

Conclusion

Data science has a stronghold in almost every field and industry. Globalization has led to the interconnectedness of industries and corporations, making data insights crucial for decision-making. The volume, velocity, and complexity of this data call for new methods that only the field of data science offers. Rigorous analytical strategies and ML and AI models have allowed us to use information by learning from past trends to forecast a better future.


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