Top 15 Data Science Applications for the Future

Read it in 10 Mins

Last updated on
17th Mar, 2022
Published
17th Mar, 2022
Views
5,143
Top 15 Data Science Applications for the Future

The potential of data science skyrocketed overnight. All thanks to fast computation, cheap storage, Big Data, and researchers who blended statistics and computer science for analyzing data. According to Analytics Insight forecast, by 2025, companies will have 137,630 data science job openings in India. A similar requirement is developing in other industry-driven countries as well. Data scientists are already in great demand in several leading companies. Every day, technology users produce 2.5 quintillion bytes of data (1 quintillion = 1000000000000000000) through emails, posts, mobile handling, user credentials, and other internet traffic. Nearly every kind of business is leveraging data science as the essential tool to launch their work in an innovative orbit. In this article, we will go through the top data science applications that will frame the future.

If you’re keen on learning data science, we strongly recommend  KnowledgeHut’s applied data science with python specialization course which helps you build your knowledge along with real world skills.

What is Data Science?

Data science is a popular stream blending statistics and computer science. This interdisciplinary branch expands by merging various domains letting enterprises remove and utilize granular data for making business decisions. Data science comprises analyzing and handling data using different tools and techniques. The field requires proficiency in statistics, mathematics, programming, and business workflow to draw meaningful insights from various datasets.

Data science practices data mining, analysis, machine learning, and other such methods on structured, semi-structured, and unstructured data. Data science has become a secret sauce for every organization to understand business requirements, customer needs, demands, behaviors, future trends, and diverse enterprise operations. Data analysts extract these data and use visualization tools like Tableau, Qlik, etc., to use data for storytelling so that business executives can make decisions from these tangible values. The vast and varied applications of Data Science continue to drive the demand for certified professionals in the field. A simple search for the best data science certification training programs can lead you to credible sources for pursuing such training.

Capabilities of Data Science

Companies of all genres are adopting data science for to utilize data-driven insights. Many product-based companies have implemented automated data-driven analytics into their applications and products to automatically understand the user needs, in some cases preempt them, and recommend solutions, or innovate to fulfil such needs. Researchers and enterprises are witnessing an unprecedented opportunity for data analysis and data science applications, working in tandem with machine learning models. Data science professionals and researchers are developing complex data science algorithms as packages or modules, which become easier to deploy in various projects. The capabilities of data science are endless. Almost all sectors, such as healthcare, finance, insurance, IT, pharmaceutical, manufacturing, energy, human resource, industries, marketing, etc., can leverage the benefits data science caters to every business. In the following section, we will discuss the top 13 data science applications that will drive the future.

Top 15 Data Science Applications for the Future

Top 15 Data Science Applications for the Future

There are a lot of data science application areas in real life that can impact the future of various sectors and industries. Let us now witness the top trending data science applications of the future.

1. Data science applications in Manufacturing:

Almost all medium and large manufacturing units across the globe are leveraging the data to understand the precise user requirements and necessities of various manufacturing products. Leveraging data science in manufacturing industries has blurred the boundaries between the physical & digital worlds. Through data science, manufacturing industries can analyze which part of the manufacturing requires more attention and workforce. Gathering different forms of manufacturing data & analyzing it helps the manufacturing team cut down operational redundancy, improves the precision to predict the amounts of goods required for manufacturing, optimize production rates, reduce forecasting errors in the supply chain, and various other manufacturing aspects. Machine learning and data mining techniques blended with data analytics also boost in achieving competitive advantage and reduce supply chain risks to bring all manufacturing products. Through previous data and records, the manufacturing industries can determine which companies have delivered the best spare parts and whose spare parts were defective. In the future, more data will help make better decisions in reducing losses, predicting demands, and user requirements in terms of product customization. Companies like Fitbit, Boat, JBL, Lenovo, etc., use data science to understand the market demand for the products they deliver before manufacturing.

2. Advanced personalization:

Millions of customers across the globe use smartphones, smartwatches, and other electronic gadgets. All of them generate a colossal quantity of data. Companies leverage this data to personalize various actions on the product or app that can drive more revenue for the business. The future of personalization based on user data and behavior is endless. For example, Netflix recommends web series based on users' choice, taste, genre. The app contains data analysis and machine learning models that automatically understand user traits and tastes towards web series and movies.

3. Medical image analysis:

There are several data science applications in healthcare and medical science. One of them is medical image analysis. Data science with machine learning can detect various health issues like artery stenosis, tumors, organ delineation, and various other diseases from images. The professionals use different frameworks & libraries for detecting issues from images (unstructured data) through texture classification. Such processes use machine learning methods like support vector machines (SVM), wavelet analysis, and content-based medical image indexing. Researchers and doctors use various free image datasets to understand and analyze the defect in a patient. With more datasets/images, soon doctors will gain insights and machine learning assistance for better diagnostics of patients.

Today, a massive quantity of search engines leverage data science to understand human behavior and searching patterns. All these search engines use different data science algorithms to deliver the best result in every search of every user. Google processes 20 petabytes of data every day. All this data helps search algorithms in understanding user patterns and traits. With time, search engines are getting smarter and better at delivering searched results in a fraction of seconds. Some common examples of search engines that leverage data science is Google, Yahoo, Bing, AOL, DuckDuckGo, etc.

5. Data Science for Genomics:

Genomics is a well-known discipline of medical science that studies the study and analyzes various sequencing genomes. Every organism including humans comprises genes. These genes include DNA and further existential traits that define each organism. After the Human Genome Project, scientists and researchers are trying harder to achieve improvement in genetic engineering. That is where they blended the ideas of big data, data science, and statistical analysis to extract valuable insight from data. Data science applications in healthcare by tech companies went higher as these companies are spending billions of dollars for analyzing the genetic sequences in humans and other animal species. These tests and examinations can help better understand the prospects of human behavior and how to cure people, without delaying that leads to casualties. Before the advent of data science, genetic projects were prohibitively expensive. But leveraging data science and machine learning models, researchers can interpret and acquire insights from the human gene in a short time span, at a cheaper expense, and with high accuracy. Various visualization tools and libraries help witness the genomic strands to examine the peculiarities and defects in myriad organisms. Companies like Google, Microsoft, Neuralink, etc. are doing genetic research leveraging data science.

6. Speech recognition:

Google's Voice, Apple's Siri, Microsoft's Cortana work on large datasets driven by Natural Language Processing (NLP) and data science algorithms. Speech recognition programs become more advanced and better understand human nature as they receive more data for analysis. Such companies need a lot of data from different languages to analyze them to make recognitions more precise. In the future, almost all disabled people will use the speech-recognition feature to ease their various day-to-day tasks. People who are not in a position to type a message can instantly use voice, and the application will convert them into text. Till today, speech recognition has not been performed accurately. But in the near future, with the advancement of data science and other technological advancements, companies will be able to make more precise speech recognition systems. Many data science professionals choose to train in applied data science with python specialization where they get hands-on experience with projects on speech recognition.

7. Data science in advertising:

Data has become the key element in rendering custom advertising to specific individuals. It is because of the complete digital marketing spectrum. All the Google ads you see and display banners appearing on various websites – almost all of them leverage data science and their algorithms. That's why digital marketing ads receive a lot higher CTR (Click-Through Rate) than legacy advertisement techniques.  Based on the analysis of user data, companies run custom ads for more sales and attract customers towards particular products or items. In the future, companies will leverage more data to make this requirement and recommendation more precise. Companies like Netflix, Amazon, Flipkart, Myntra, etc., have adapted such advertising within their platforms.

8. Data science in financial fraud detection:

There are various data science applications in finance. One of them that will continue to develop and become more robust in the future is fraud detection. Finance and data science go hand in hand where data analysts can capture the data patterns that lead to the various scenarios when fraud can take place. Different finance companies and apps have started leveraging this fraud detection system. Some of these systems use machine learning algorithms where the data is fed from the analysis drawn by the data science professionals. These systems help in analyzing risk probabilities by understanding the various factors like customer profiling, past expenditures, financial records, and other critical variables. For financial fraud detection systems, professionals utilize customer portfolios & analyze their trends, behavior, and other parameters using data through business intelligence tools. Various public and private-sector banks leverage such data science and machine learning algorithms for fraud detection.

9. Airline Route Planning:

Airline companies are another massive economic sector that often runs with losses. Thus, to convert this industry into a profitable business, airlines firms have started leveraging data science techniques to predict the market & their customers.  Through data science, airline firms started analyzing the customer head-count, and the cost incurred to make a flight take off. Data science can make precise analyses of all these variables & can bring these companies a significant profit. Also, through data science, companies can provide discounts on the flight fare based on various factors like the number of passengers, fuel price, monetary fluctuations, etc. So, leveraging data science helps them determine the strategic areas of advancement. Implementing data science, these airline firms can:

  • Forecasts delay in the flight
  • Decides whether to land at the destination from the source or make it a connecting or halt in between
  • Decide which airplane types to buy
  • Decide the efficient route
  • Drive customer loyalty programs and discounts with efficiency and keeping profit margin

10. Data science in defending against cyber-threats:

Data science algorithms and techniques have become the brain of cybersecurity and defense applications. It helps improve the techniques by analyzing the attack patterns for better combating cyber threats. Through data science, firewall, antivirus, and other defense applications can understand the patterns of the attacker and notify the network or system administrator of the possibilities of security breach. Nearly all digital intrusion detection systems come with data analysis and machine learning models that can identify threats and work upon them for better security of the organization's digital assets. Some popular companies that have already started using data science in their products are Crowd Strike, FireEye, Blue Hexagon, Cynet, Kaspersky, ForgeRock, Okta, etc. Considerably, data science is changing how traditional cybersecurity tools were being operated and is profoundly revolutionizing the security scenario through data-driven approaches.

11. Data science application in transportation:

Data science helps this industry prepare a comprehensive study of driver behavior, fuel usage, vehicle tracking, etc. Data science, together with machine learning, is making the transportation system a lot safer by improving car performance, offering drivers more security and leading to development of autonomous vehicles and improved documentation of routes, traffic, weather and other factors on the journey. Some of the well-known transportation and cab providing companies like Lyft, Uber, Ola, etc. leverage data science for improving the pricing, delivery routes, optimal resource allotment, by combining multiple elements such as geographic location, consumer profiles, economic indicators, and logistic capabilities.

12. Data science application in education:

There are a lot of data applications in education. Educationists and counselors can leverage data science to provide more precise counseling to every student, especially the weaker candidates through various analytical metrics. Another significant aspect of education is it provides techniques for measuring the instructors' performance, monitors students' benefits & needs. All modern universities and institutes are utilizing data science to modify their curriculum as per the industry standard.

13. Customer Behavioral Analytics:

Data analysis leverages user data to understand the behavior of users towards a particular product, aspirations, interests, and other related information that can benefit the organization. This data also helps in predictive analysis on how the consumer will use a particular product. Some typical datasets that help in analyzing customer behavior are an item in the cart, frequent visits of a product or item, purchasing patterns, email reading related to ads, social networking posts related to products, etc.

14. Data science application in gaming:

Game development companies can also use data science and data analytics algorithms to understand the gamer's mindset. Data scientists also leverage data science to develop and investigate the gamer's behavior towards the game. They help build mathematical models and automate game analytics to detect the points & usage of the game by each individual. Data science is helping the game companies to understand which user is finding the game interesting or challenging & which gamer gets bored after a specific time duration. These insights help game development companies provide custom game features to keep the gamer engaged. Ultimately data analytics helps in improving user engagement and user retention towards the game.

15. Data science application in Recommender systems:

Recommender systems are another popular application used for various recommendation-providing purposes. They deal with large and convoluted data margins and prioritize those items from the application/system that are potentially more interesting to the user. Data science plays a significant role in analyzing those user data and choices and based on that helps the recommendation algorithm to work. Today, almost all large e-commerce companies, search engine companies, and OTT platforms leverage this recommender system that uses data analytics. By employing more data, such systems will become better at understanding user preferences and choices.

Looking Forward

We hope this article has given you a crisp idea of the large spectrum of potential data science can bestow to all the different verticals and across industries. Every industry, whether it is banking, manufacturing, cybersecurity, healthcare, airlines, or advertising - data science can bring significant change in delivering the best result through filtered or refined data. Due to such diverse data science applications, data science aspirants need to stay updated and pursue data science certifications to carry forward such technological skills in the competitive job market.

Frequently Asked Questions(FAQs)

1. How Data Science is used in real life?

Data science plays a critical role in analyzing a specific set of individuals, actions, or operations based on past data. Then these data are accumulated to predict how it will turn things in the future. Internet searching, marketing, healthcare, entertainment, airlines planning, logistics, finance industries, gaming, almost all sectors & industries in real life.

2. What is Data Science and its applications in the real world?

Data science is a popular stream blending statistics and computer science. This interdisciplinary branch expands by merging various domains letting enterprises remove and utilize granular data for making business decisions. Data science comprises analyzing and handling data different tools and techniques. The data science field requires proficiency in statistics, mathematics, programming, business workflow to pull meaningful understandings from various datasets.

Data science plays a significant role in almost every sector like internet searching, online business, e-commerce, marketing, healthcare, entertainment, airlines planning, logistics, finance industries, gaming, almost all sectors & industries in real life.

Profile

Gaurav Kr. Roy

Author

Mr. Gaurav is a cybersecurity engineer, developer, researcher, and Book-Author who did his B.S.-Cybersecurity from EC-Council University & Masters from LPU. He is an India Book of Record holder, Guest speaker with 7+ years of experience in IT.