10 Top Data Science Applications You Should Aware in 2022

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10 Top Data Science Applications You Should Aware in 2022

Srijani Ghosh
Last updated on July 26, 2022

    We hear the buzzword 'Data Science' everywhere today. Do you want to know the most popular data science applications? If yes, this is the right place to get all the essential information about data science applications. Data science is said to be the future. Many online platforms also recommend learning data science today to secure a high-paying job in the future. For this purpose, there are many online and offline resources to learn data science and related technologies. How is data science so important and promising? Why is everybody recommending learning data science? What is data science? Through this article, we will explore the answers to all these questions and learn a lot about how data science is already changing the way big and small organizations work and the way we do day-to-day activities.

    What is Data Science?

    Of course, it has something to do with data. But what? Suppose you walk into a medical shop with a list of medicines. Also, these are regular medicines that you buy every month for your grandmother. After 2 to 3 visits, the pharmacist can recognize you and knows exactly what you want. If any medicines are not available, she will suggest alternatives or pre-order them for you. Since your data is structured and the pharmacist knows you in person, it is easy to know your preferences. In today's digital age, there are millions of customers logging in to Facebook, Instagram, Amazon, Flipkart, and so on. If the owners were to know the preferences of each user, then how would they:

    • Separate useful information and non-useful information?
    • Understand each user's preferences?
    • Provide future solutions based on current inputs?

    All this for a massive scale of unstructured data! This is where data science comes in. It is a technique that uses scientific methods, algorithms, and processes to extract helpful information from the massive chunks of data received from various users. Further, it processes the required data and provides business insights and potential solutions to business problems. We saw just one case of data science applications, i.e., online shopping! Can you think of more situations like these? Data science has found use in many fields (data science applications) today like healthcare, telecom, online shopping, gaming, image and speech recognition, AI, and IoT. Let us explore each of them to understand the top data science applications.

    Top Data Science Applications

    1. Recommender systems

    It is one of the best examples of data science applications. Used in most commercial applications, typically online services like Amazon, Netflix, and YouTube, recommender systems are slowly becoming an essential part of our life by giving us relevant choices based on our preferences. Whether you like it or not, an online recommender system will still suggest what it thinks you would like. An efficient system is believed to generate a lot of income. Of course, we all like people who understand our preferences. For example, our mothers know what we like and prepare meals accordingly. Similarly, suppose the YouTube recommender system thinks we like a particular genre of songs and plays similar ones. In that case, we get happy because we get to listen to many of our favorite songs without creating a playlist manually. These systems work on:

    1. Collaborative filtering – New recommendations are produced based on past user interactions. Such data is stored in the user-item interaction matrix and filtered for further use.
    2. Content-based method – These are based on past interactions plus a little more information about the user. For example, to show you the kind of movies or shows you might be interested in, Netflix will scan not only your past searches and views but also understand your age, gender, location, and other personal information to get the best results. For instance, if you are a man, you could be more interested in documentaries. If you are located in Maharashtra, you might also like shows from a famous local comedian. The system builds a model based on observed patterns and predicts user preferences.

    2. Healthcare

    Healthcare is another important market that benefits from data science. We often come across incorrect or incomplete diagnoses leading to delays in the proper treatment that can sometimes lead to a health disaster. Thankfully, data science has come to the rescue. Although data science has taken the online world by storm, healthcare is one area where data science has been used to its maximum potential and with a lot of success. Tasks like maintaining computerized medical records, discovering new medicines, extracting data from images obtained from different scans, research in new fields like genetics, and more accuracy in diagnostics have become more accessible and more efficient with the implementation of data science. Here are some of the critical areas of healthcare where data science has brought revolutionary changes:

    A. Medical Imaging

    For significant body problems, doctors recommend MRI, X-Ray, and sometimes CT scan. Doctors have to analyze the report manually, and sometimes it is almost impossible to find the smallest of deformities in the innermost and essential parts of the human body. This might lead to improper diagnosis and, thus, incorrect treatment. With the help of scanned images and image segmentation through deep learning techniques, filters like image enhancement, reconstruction, and corrections can be applied to the image. Using image processing techniques, it is now possible for doctors to easily see the data and provide appropriate treatment.

    B. New Drug Discovery

    Drug discovery typically requires a lot of effort as well as funding. It is a complex and risky process that needs thorough testing. However, with machine learning algorithms and data science, the insights and predictions about a medical product are far more accurate that too in way less time than the traditional methods. Various models are developed, trained, and tested based on patient information. Innovations can be predicted based on past data as well as genetics.

    C. Keeping Track of the Patient Health

    Have you heard of IoT (Internet of Things) ?. IoT is a concept wherein a device is connected to other devices via the internet to collect and process data and share the same with other devices. With such a concept in mind, let us say a patient has a wearable band (or device) that can track her heart rate, temperature, BP, and other vital signs and share this data with another device (e.g., a computer) that analyses all the data using various data science techniques. Doctors can virtually manage the patient's health from any location using the analyzed data and suggest relevant treatments. Real-time analytics help doctors monitor critical patients and advise a future course of action based on their current condition.

    D. Virtual Assistance Apps

    Suppose we have a platform where a patient can type their problems and symptoms, and the algorithms then determine the possible diseases based on the data entered by the patient. With such a platform, patients who, otherwise, don't like to discuss their problems can also come forward and be anonymous. Psychological issues are often hard to share, but now it has become easy for patients to get solutions to their problems through these platforms (in the form of apps). Some common examples are Ada, Babylon, and K Health .

    E. Genomics

    Genome is the genetic material of any organism. It consists of genes and different types of DNA. Genomic data is collected through the study of genomes (called Genomics) using data processing software for such purposes. Data Science is used extensively to analyze the structure of genomes and other vital genomic parameters. This helps researchers understand the functions of specific genes. Analysis and processing of such data can be revolutionary for the future of bioinformatics systems.

    F. Predictive Analysis for Improved Healthcare Management Systems

    Using a predictive data science model, machine learning algorithms can find patterns and produce accurate information about specific symptoms and diseases. This helps in early prevention or taking suitable measures for curing the same. It can also help in improving patient care. Predictive analysis also improves the logistics of medical supplies and other pharmaceutical items.

    3. Finance Industry

    Finance is another market that comes among the best data science applications. All financial institutions are data-driven. As such, a financial data scientist is an official position that comes with many responsibilities and, of course, money. The job is essential because it involves risk detection and monitoring, surveillance, fraud detection and prevention, claims, payments, improving overall customer experience, etc. Financial data science will exploit the knowledge of statistics, decision science, predictive analysis, and more to build a suitable model and produce meaningful and valuable results.

    A. Risk Analytics

    With risk analysis, a company can prioritize and make strategic decisions to become a key player in the market. Risk analysis also helps companies understand the credit scores of users based on transactions done by them. This is done by applying machine learning algorithms to analyze their data.

    B. Real-time Data Analysis

    Through data science, finance companies can now track user transactions, credit scores, and so on.

    C. Data Management and Personalized Customer Services

    Financial institutions need almost all the details of a user. They collect data in various forms that are unstructured and structured. Data science involves handling complex unstructured data and uses various techniques like data mining and text analytics to generate insights about the user. ML algorithms can also analyze market trends using this customer data. Also, based on the type of transactions and other user behaviors, these institutions strive to provide personalized services and offers to different users, thus creating more business opportunities and profits.

    D. Fraud Management

    Improvements in machine learning algorithms due to their self-learning nature helps data scientists analyze unusual patterns and transactions. This helps in preventing and detecting potential online frauds.

    E. Algorithmic Trading

    Algorithmic trading helps finance companies understand market trends and formulate new business strategies to raise their business to new levels in the future. It consists of analyzing vast data streams to build a model that makes predictions for future market trends.

    4. Logistics and Transport

    With big data, many issues with the supply chain mechanism have been eased out. Vast amounts of data are analyzed using machine learning algorithms to estimate delivery times and manage inventory by forecasting the demand and optimizing the warehouse. It also helps maintain the assets by finding usage patterns and reducing overall freight costs by optimizing delivery paths using IoT for real-time data analytics. This is a great area that needs more exploration and resources as currently, there are very few data scientists working for logistics systems. Cisco is one major brand that has successfully fulfilled more orders in considerably less time using advanced data science techniques and tools. Cisco refers to this revolutionary achievement as visualization of the 'circle of light'.

    5. Speech and Image Recognition

    Image and speech recognition are two of the top data science applications today. "Hey, Alexa! What's up?" Familiar with that? Yes, we are talking about the likes of Amazon Echo, Cortana, and Siri. All these are AI-powered intelligent systems that recognize speech and interact like humans. If you say anything like 'Alexa, fix me a hair appointment.' or 'Alexa, find nearby restaurants.', it will (almost) accurately give you the results. How? Let's know.

    Speech Recognition and Processing

    While speech recognition has been around for more than a decade, its popularity and scope have only increased after deep learning. Data science techniques have enabled more accuracy and faster results. Speech recognition requires a lot of data and processing the data into text. For example, the tone and pitch vary from person to person. One person might say Alexa very fast, while the other might say it slowly. These systems have to recognize different pitches, tones, speeds, and more. Hence, it requires a lot of data to build a model and train it for recognition purposes. So, what about image recognition and processing? Let's discuss that next.

    Image Recognition and Processing

    You want to see more information about apple on Google and thus type 'Apple'. However, based on popularity, Google shows you all the links about Apple Inc. So now, you have to modify your search and type 'apple fruit.' This can be boring and sometimes frustrating. Instead, how about if you could show the picture of an apple to Google and get the search results without typing? This kind of visual search is possible through image recognition and matching. When you upload photos on Facebook, it gives you suggestions on whom to tag by recognizing your friends' faces. Whoa! How does that happen? Facebook analyzes data stored by you on Facebook, and specific algorithms are applied that help systems recognize who is who. When you set up a face lock on your smartphone, you have to take 2 to 3 photos of yourself in different angles for the phone to identify you correctly. So, imagine the amount of data Facebook processes to accurately identify you and others in a group picture. The data is analyzed, a predictive model is built, and the outcome is a set of decisions. All this is possible with data science and big data. The computer recognizes images as a vector image or sequence of pixels (raster) the same way as a human eye remembers and recognizes pictures based on memory. Image recognition is widely used in security and surveillance, object and gesture recognition, and finding visual geolocation. It also finds use in image processing in the healthcare and medicine field, industrial automation, etc.

    6. Gaming

    Gaming is another field that is among the top data science applications. Can you think of the kind of data stored when you are up for a game? Take Farmville, for example. There is a tremendous amount of game data and a stored state. Everything is stored in your points, level, playtime, interaction time, peak activity time, rest time, diamonds achieved, friends, invitations, and so on. Well, all this data don't lie there! Everything is collected and analyzed. Your game data and user data are processed for many reasons. This includes understanding the problems you face as a game user and what features can be improved. It also helps to suggest similar games that you might like to play, identify your pattern, suggest more attractive offers to increase your game time, and so on. Machine learning algorithms process vast amounts of data and build a predictive model to identify trends and improve the overall gaming model. Big data analytics also help determine whether the game is giving the desired results and yielding profit or not. Gaming systems also use AI, advanced graphics, image recognition systems, and personalized marketing. Hence, this makes data science essential for the gaming world of the 21st century.

    7. Digital Marketing

    SEO or search engine optimization is another buzzword that we hear a lot today. With a lot of content being poured over the internet every day, Google has made it's ranking algorithms harder than ever. Have you ever scrolled past the first page of Google results? Most probably no! Some users might not even go beyond the third or fourth search result. So, how come some websites remain on top and are visited more often than others? The answer is data science. Yes, you can improve your website SEO by using data science . Let's take another example of using data science for digital marketing. A user subscribed for Grammarly, and ever since, she gets a weekly email report of her performance based on the documents checked on the Grammarly app. The report includes the total number of words checked, (writing) tones detected, and frequent writing issues. One week, the user did not use it. On the following Monday morning, she got an email saying, "It's been a while since you have used Grammarly." Inside there is an offer waiting to be grabbed! That is the power of data collection and analysis, i.e., data science. With data science tools and techniques, one can get valuable insights about the website performance, relevant keywords, most searched topics and words, unusual traffic, top conversion paths, redirects, and much more. You can also know for how long a user was on your website page, website load time, any errors, user clicks, etc. All this data can be visualized, compared, and analyzed to help you make better content and prepare better SEO campaigns resulting in more traffic for your website.

    8. Master Data Management

    Master data is the critical data maintained by every organization to carry out their regular affairs. It is a massive amount of data that should be accurate, consistent, and up to date. With companies maintaining multiple databases for storing and reading data and the amount of data increasing by the day, Master Data Management (MDM) can be thought of as a daunting task. However, it is not so. How can so much data be stored, and how is the relevant data picked out of huge chunks of data? This information can be integrated appropriately, channelized, and analyzed with data science, leading to a wholesome consumer experience. For example, when you are booking a flight, you are asked whether you want to book a hotel or rent a cab. Same way, if the data collection pattern finds that you are often traveling, the algorithm tries to give you offers and better deals. This allows for a more personalized and hassle-free experience. Yes, machine learning algorithms pick relevant information and analyze the same for different purposes. Having a master data storage space lets them gather data from a central place for different business purposes.

    9. Product Comparison Sites

    Earlier, we just had the option to go to Amazon, eBay, or some other online store to make a purchase. But now, there is a better way to grab the best online shopping deals. It is through price comparison websites. These sites compare similar products from various websites and then show the results nicely to make our purchase decision with ease. Thus, it saves us the hassle of visiting many online shopping websites to get the best deal. But how do these product comparison sites get data from so many websites and then filter the data by popularity, price, reviews, features, etc.? Most of the data comes from data feed files from various vendors. Price comparison websites collect, organize and structure this real-time information using data science techniques like data crawling to produce the best insights. Some famous price comparison websites are Shopzilla, Shopping.com, and PriceGrabber.

    10. Augmented Reality

    If you have been to the gaming arena of a busy mall, you will surely know what AR or Augmented Reality is. For example, some malls feature a simulation of a roller coaster ride. It provides you with the natural feel of riding a roller coaster while sitting in one place, and that too with perfect graphics and sound effects. It works on perception, i.e., what you see is what you believe. With unique visuals and haptic and audio sensory modalities, you are made to think that you are actually on a roller coaster ride. The motion, audio, and visuals are all in sync for the perfect simulation. The question now is, "How is data science helping AR?" AR applications survive and thrive on data. More data points can be turned into analytical insights to identify patterns through visualization through big data. For example, location-based or geospatial information helps to give users an immersive and interactive experience. On the other hand, with technology as advanced as AR, vast amounts of data can be represented and interpreted better using 3D graphics and charts. This data science application is yet to be fully explored as there are many challenges involved. However, the future sure seems bright for the synergy of AR and data science.

    Conclusion

    There are many more areas where data science continues to make its mark than the above-listed top data science applications. The gist is that as long as data is of prime importance, data science will continue to flourish, and data scientists will have the best jobs in the industry in terms of the kind of work and salary. Data science is the need of the hour, and in the years to come, it will assume the form of more advanced AI, self-driving cars, human-like robots, cryptography, and whatnot! It will also hold prime importance to revolutionize agriculture, education, retail, and more. Let's wait and watch! People are also reading:

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