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8 Applications of Data Science

The amount of information that we create daily through different media is huge and together we call it the Big Data, characterized by the three V’s: volume, variety and velocity. Volume is the amount at which the different Variety of data is generated at an enormous Velocity. Identifying a pattern from those data and drawing meaningful insights have become an extremely important part of our today’s world. Using different methods, processes, algorithms and systems to extract the knowledge and insights from structured and unstructured data is defined as Data Science. It has become a “talk of the town” profession. 

Though it is widely popular, yet it has its myths and reality. Data Science is often considered to be as complex as coding using different tools but in reality, Data science is about understanding and solving problems. Another popular myth says, Data Science is a Science. The fact is, that Data Science is a mix of Art and Science. As long as the project requires one to write code, run traditional statistical analysis and build predictive machine learning models it is a science. The art aspect of data science is in the thought process of tackling a problem and devising a solution.

Irrespective of the prevailing myths, Data Science is the most cutting-edge domain that has changed almost every industry.

Here are 8 essential sectors that have been transformed by data science.


Medicine and healthcare are two of the most important part of our human lives. There are several fields in healthcare such as medical imaging, drug discovery, genetics, predictive diagnosis and several others that make use of data science. According to a study, the data generated by every human body is 2 terabytes per day. Computers learn to interpret, identify, and detect from the data and further expedite diagnosis and investigation. Recently Google AI published a study using Deep Learning to Inform Differential Diagnoses of Skin Diseases.


Data Science is used to quickly analyze finance and make a better decision to manage finance which leads to an increase in profit for financial institutions. Algorithmic trading, financial fraud detection, customer data management, risk analytics, Real-Time Analytics, Consumer analytics, personalized services are some of the core areas of application for Data science. Accuracy in detecting anomalies and fraud using Data Science has helped to reduce risks & scam, minimizing losses and saving the reputation of the financial institution. 


According to a recent study conducted by MIT on Data Analytics in Urban Transportation we utilize multiple automatic data sources, such as smart cards, GPS vehicle locations, cell phone Call Detailed Records, and mobility tracking apps, to estimate and predict travel demand, explore behavioral regularities, quantify service reliabilities and evaluate travel demand management program. In another study conducted by MIT on How UPS Uses AI to deliver holiday gifts, it talks about the latest platform which incorporates both machine-learning and AI to solve logistics problems.


The data collected from the field in real-time, is analyzed post-game using game theory and combinatorial game theory. The conclusion is then implemented during the practice sessions and predictions are made for various decisions affecting outcomes in the following games. Thus, helping teams to enhance their performance. Data Science is also being used to analyze the correlations between sleep, nutrition, and training of an athlete which serves as a guide towards maintaining exemplary physical conditioning. Fan analysis is yet another crucial part of sports analytics, sports management teams can discover how and when fans are likely to attend events or buy merchandise. Data helps them to easily identify fans that are likely to engage. 


Local governments, federal agencies, and departments use data-driven tools to prevent waste, fraud and abuse, to combat cyber-attacks and safeguard sensitive information, use business intelligence to make better financial decisions, improve defence systems and protect soldiers on the ground. The Analytical algorithms and AI-powered solutions are capable of detecting tax evasion. With real-time analytics, governments can watch, track and monitor the darkest and smallest streets, corners of cities and villages, borders, and the air as well as navy spaces. Security agencies are working on real-time detection of data patterns from unusual conversations, texts, interaction, contacts, purchases or any movements in potentially dangerous locations and linking these patterns of data for identifying potential terrorist behaviour. Undoubtedly, big data analytics supports national security and brings opportunities for different areas.


Data analysis is very important in the e-commerce and retail industry. Data Science uses complex machine learning and deep learning algorithms to predict the purchases, profits, losses and even nudge customers into buying additional products based on their behaviour. Recommendation engines are one such tool that is used by the retailers to analyze the customers’ profile and leverage these engines to push a customer towards buying the product. Customer sentiment analysis uses machine learning algorithms to identify words with a negative or positive attitude of the customer towards the brand. AI-powered predictive intelligence technology is utilized to predict and deliver what online shoppers need even before they look for a product. 

Social Good

Data Science helps local leaders to uncover new insights and trends from their data and build more impactful programs for the communities. Stanford University uses satellites and AI-assisted analytics tools to identify poverty zones in Africa. Data science allows social organizations to determine the most efficient and cost-effective ways to provide for those in need. Nonprofits have huge datasets that they can utilize to develop statistical models that can help them optimize fundraising. Data analytics and visualization can also play a huge role in real-time tracking during crises and optimizing relief efforts. 


Data Science extracts meaningful insights from various marketing aspects such as customer intent, experience, behaviour, etc to efficiently optimize marketing strategies and procure maximum revenue. Using a time series model marketers can determine which channels are giving an adequate lift. The customer lifetime value model can segment customers by their behaviour which marketers can leverage for devising retention strategies. A predictive lead scoring system calculates the probability of conversion and segmenting the lead list. Marketers use data science to do sentiment analysis and monitor how customers react to marketing campaigns.

The future of Data Science applications is even more than what we discussed. Augmented reality, Gaming, Speech recognition, Airline Industry, IoT, Self-Driving cars, Natural Language Processing, are some of the emerging fields where Data Science is constantly showing results. 


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