Cool Data Science Applications Every Business Should Know

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Data Science has been the buzzword across industry domains. Today Data Science is helping businesses to evaluate complex sets of structured and unstructured data. The leadership in an organization uses relevant insights derived using algorithms and other scientific methods efficiently. Businesses implementing data science are reaping profitable growth, cost efficiency, and profitability.

Data science is a multidisciplinary career domain where data scientists apply their knowledge and actionable data across a wide variety of application domains, be it the healthcare or the e-commerce industry. Businesses are getting rid of paperwork and are increasingly using data science analytics and machine learning algorithms to make data-driven decisions to increase the purchasing power of their customers.

So, let’s take a look at why you need an online data science certificate course to transform your business or boost your career path. You can now expose yourself to an array of career choices across industries that are predominantly dependent on data science.

Cool Data Science Applications Every Business Should Know

Data science is dominating all the industries in the world today and is increasingly becoming a must for every business. Organizations have become data-centric and use data science to ease their business processes. Data science is used in e-commerce, banking, finance, education, manufacturing, transport, and many other industries. Read below to know more about data science applications in different business areas:

  1. Data Science in the e-commerce industry

E-commerce and retail companies primarily use the internet to sell and purchase goods and services. With the help of data science and concepts like elastic searches, programmatic advertising, customers can choose from a wide range of products and filters as per their needs. Predictive analysis is being used to understand and gauge the likability and interest of a customer towards different products for future needs.

Customer feedback is key to driving customer loyalty; hence sentiment analysis is used to analyze customer feedback obtained through online surveys on the web and mobile apps platforms.  Thus, e-commerce companies, with the application of data science, are continuously improving their business landscape to address customer needs and emotions. They provide insights in the form of recommendations and better their service mindset.

  1. Data Science in Banking & Finance

Banking and Finance are amongst the most prominent applications of Data Science. Banks always want to keep themselves ahead of the competition in the market. Customer Lifetime Value Prediction is an important aspect in banks, where data science plays a vital role in gauging the duration of revenue generation by their customer. This industry is making better decisions through customer segmentation, securing & managing customer data. Banks are analyzing customer investment trends, performing risk modeling through data science. This helps the bank to evaluate its overall performance. This segment enhances cost efficiency through machine learning algorithms and methods like data mining & natural language processing, identifies, monitors, and prioritizes any potential risks and mitigates them.

  1. Application of Data Science in Healthcare Industry

Medical Image Analysis and Predictive Modelling are a boon to the healthcare industry because of data science.  Data Science and Artificial Intelligence are re-shaping this industry ever before. There is absolutely no shortage of data, and all of these are emerging from multiple clinical trials, genetic information, electronic media records, billing, care management database, internet research, etc.  Predictive modeling of a patient’s gene expression is helping doctors immensely to assess historical data. Natural Language Processing is used to create intelligent bots that answer queries when making correlations with molecular biology.

Data Science is being tremendously used in identifying health risks, treatment, disease diagnosis, drug discovery, prevention plans, hospital operations, post-care monitoring, etc. The Healthcare industry is fast emerging as one of the highest job creators in the field of data science.

  1. Prevalence of Data Science in Manufacturing Industry

The manufacturing industry continuously looks at transforming raw materials into useful customer products. These products can be used in automobiles, the transport industry, sports, or even in households. Data Science is being broadly used in manufacturing industries for optimizing production, reducing costs, and revenue generation. For example, an industrial automation company creates a machine-learning tool that obtains data, identifies times of highest efficiency, and provides recommendations for replicating that high-efficiency state. As the algorithm gathers more data, it provides:

  • Better suggestions for continuous improvement.
  • Delivering quality products.
  • Enhancing customer satisfaction prevalent in manufacturing lines.
  1. Application of Data Science for targeted advertisement

Internet companies have been immensely benefited from data science. With the help of machine learning techniques and applications, advertising companies can recognize their customers’ browsing patterns. They are receiving deep insights that help them gauge a customer’s interests and provide them with trends of customer browsing and buying habits. Sentiment analysis is helping internet companies to understand customer mood and determine if a customer is serious or has uncanny habits. The companies, thus, position themselves better by logically and sensibly targeting receptive customers. This allows internet companies to improve their goodwill, increase their customer reach and base, and have quality products available.

  1. Data Science in Transport Industry

Car manufacturing companies are manufacturing automatic, intelligent, and self-driving cars. Until a few years ago, these concepts were a distant dream.With the amalgamation of artificial intelligence and machine learning, data science is turning this into a reality.

The automobile industry has greatly benefitted by creating logistical roots to their benefit with the help of data science. Transportation companies use data science and predictive tools to make sense of different data types like customer profile, location, weather conditions, availability of transport, delivery, travel routes, driver behavior, vehicle monitoring system, etc.

  1. Data Science in Risk Analytics 

Risk management is prevalent in every business. There are data science analytical tools through which experts can easily identify potential risks and the factors that generate these risks, like resources, cost, time, etc. They will then understand the risks related to the project and develop risk mitigation plans. These will need to be continuously monitored for deviance and appropriate control measures. Organizations will be able to optimize their costs and will be ahead of their competitors.

  1. Data Science in Customer Data Management

There is enormous customer data available with every business. With the use of data science, companies are able to delve into deep customer insights and do better customer segmentation. For example, by applying predictive data analysis, customer traits, gender, education, lifestyle, ethnicity will help to segment the customer base. The sales and marketing team can use this data to their benefit to boost cross-selling and up-selling of products. Businesses can better identify and empathize with their appreciated, valued, and loyal customers.

The Bottom Line:

To conclude, the application and use of data science are growing immensely across all industry segments. Any work that generates data falls under the scope of data science. Programming skills, knowledge of statistics, critical & logical thinking are essential skills that one needs to know. MTech in data science is highly recommended if you want a sustainable career in Data Science. Job opportunities are on the rise, and so is the demand for skilled professionals in the world across all domains of data science.