Aug
10
Data Science – Applications ”; Previous Next Data Science involves different disciplines like mathematical and statistical modelling, extracting data from its source and applying data visualization techniques. It also involves handling big data technologies to gather both structured and unstructured data. Below, we will see some applications of data science − Gaming Industry By establishing a presence on social media, sports organizations deal with a number of issues. Zynga, a gaming corporation, has produced social media games like Zynga Poker, Farmville, Chess with Friends, Speed Guess Something, and Words with Friends. This has generated many user connections and large data volumes. Here comes the necessity for data science within the game business in order to use the data acquired from players across all social networks. Data analysis provides a captivating, innovative diversion for players to keep ahead of the competition! One of the most interesting applications of data science is inside the features and procedures of game creation. Health Care Data Science plays an important role in the field of healthcare. A Data Scientist”s responsibility is to integrate all Data Science methodologies into healthcare software. The Data Scientist helps in collecting useful insights from the data in order to create prediction models. The overall responsibilities of a Data Scientist in the field of healthcare are as follows − Collecting information from patients Analyzing hospitals” requirements Organizing and classifying the data for usage Implementing Data Analytics with diverse methods Using algorithms to extract insights from data. Developing predictive models with the development staff. Given below are some of the applications of data science − Medical Image Analysis Data Science helps to determine the abnormalities in a human body by performing image analysis on scanned images, hence assisting physicians in developing an appropriate treatment plan. These picture examinations include X-ray, sonography, MRI (Magnetic Resonance Imaging), and CT scan, among others. Doctors are able to give patients with better care by gaining vital information from the study of these test photos. Predictive Analysis The condition of a patient is predicted by the predictive analytics model developed using Data Science. In addition, it facilitates the development of strategies for the patient”s suitable treatment. Predictive analytics is a highly important tool of data science that plays a significant part in the healthcare business. Image Recognition Image recognition is a technique of image processing that identifies everything in an image, including individuals, patterns, logos, items, locations, colors, and forms. Data Science techniques have begun to recognize the human face and match it with all the images in their database. In addition, mobile phones with cameras are generating infinite number of digital images and videos. This vast amount of digital data is being utilized by businesses to provide customers with superior and more convenient services. Generally, the facial recognition system of AI analyses all facial characteristics and compares them to its database to find a match. For example, Facial detection in Face lock feature in iPhone. Recommendation systems As online shopping becomes more prevalent, the e-commerce platforms are able to capture users shopping preferences as well as the performance of various products in the market. This leads to creation of recommendation systems, which create models predicting the shoppers needs and show the products the shopper is most likely to buy. Companies like Amazon and Netflix use recommendation system so that they can help their user to find the correct movie or product they are looking for. Airline Routing Planning Data Science in the Airline Industry presents numerous opportunities. High-flying aircraft provide an enormous amount of data about engine systems, fuel efficiency, weather, passenger information, etc. More data will be created when more modern aircraft equipped with sensors and other data collection technologies are used by the industry. If appropriately used, this data may provide new possibilities for the sector. It also helps to decide whether to directly land at the destination or take a halt in between like a flight can have a direct route. Finance The importance and relevance of data science in the banking sector is comparable to that of data science in other areas of corporate decision-making. Professionals in data science for finance give support and assistance to relevant teams within the company, particularly the investment and financial team, by assisting them in the development of tools and dashboards to enhance the investment process. Improvement in Health Care services The health care industry deals with a variety of data which can be classified into technical data, financial data, patient information, drug information and legal rules. All this data need to be analyzed in a coordinated manner to produce insights that will save cost, both for the health care provider and care receiver, while remaining legally compliant. Computer Vision The advancement in recognizing an image by a computer involves processing large sets of image data from multiple objects of same category. For example, Face recognition. These data sets are modelled, and algorithms are created to apply the model to newer images (testing dataset) to get a satisfactory result. Processing of these huge data sets and creation of models need various tools used in Data Science. Efficient Management of Energy As the demand for energy consumption rises, the energy producing companies need to manage the various phases of the energy production and distribution more efficiently. This involves optimizing the production methods, the storage and distribution mechanisms as well as studying the customers’ consumption patterns. Linking the data from all these sources and deriving insight seems a daunting task. This is made easier by using the tools of data science. Internet Search Several search engines use data science to understand user behaviour and search patterns. These search engines use diverse data science approaches to give each user with the most relevant search results.