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Data Science Applications PowerPoint Presentation

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Slide 1 - Data Science Applications Data Science Applications didn't suddenly start serving a di erent purpose. Because of quicker computers and less expensive storage, we can now predict outcomes in minutes rather than the many human hours it used to take to process them. What is Data Science? The entire process of extracting useful information from unstructured data using ideas like statistical analysis, data analysis, machine learning techniques, data modeling, data preparation, etc. is known as data science. Data science is a field of study where data is processed using sophisticated statistical and mathematical theories and machine learning techniques to produce insights that may be used to solve real-world issues.
Slide 2 - Why Data Science? Currently, there is a huge demand for qualified data scientists across all businesses. They rank among the best-paid workers in the IT sector. A data scientist earns an average pay of $110,000 per year, making it the finest profession in America, according to Glassdoor. Few people have the ability to extract useful insights from unprocessed data. This data is gathered from all relevant resources, including Posts on social media networks that collect information from shoppers using sensors in malls Images and movies taken with smartphones are digital E-commerce transactions for purchases Big data refers to this information. Huge volumes of data are constantly flooding into organizations and businesses. Therefore, understanding what to do with and how to use this data is crucial. The idea of data science is depicted in the image above. It combines a variety of abilities, including statistics, math, and business domain knowledge, and aids organizations in: Reducing expenses develop brand-new markets Utilize di erent demographics measure the success of marketing initiatives Introduce fresh goods or services
Slide 3 - Applications of Data science Health care Data science applications are very helpful for the healthcare sector. The field of data science is rapidly advancing in the healthcare industry. Many areas of the health care industry use data science. Medical Image Analysis Drug Development Virtual Assistants and Health bots Medical Image Analysis Procedures like detecting malignancies, artery stenosis, and organ delineation use a variety of methods and frameworks like MapReduce to find the best parameters for tasks like lung texture categorization. It employs machine learning methods for solid texture classification, including wavelet analysis, content-based medical picture indexing, and support vector machines (SVM).
Slide 4 - Drug Development Data science applications and machine learning algorithms streamline and accelerate this process, giving a fresh perspective to each step, from the first screening of medicinal substances to the prediction of the success rate based on biological characteristics. Using complex mathematical modeling and simulations, these algorithms can forecast how the chemical will react in the body in place of "lab tests." The construction of computer model simulations in the shape of a biologically suitable network, which makes it simpler to predict future events with high accuracy, is the aim of computational drug development. Virtual Assistants and Health bots AI-powered smartphone apps, which are frequently chatbots, may be used to provide basic healthcare assistance. You only need to describe your symptoms or pose a question to learn important details about your health state from a wide network of symptoms and e ects. Apps can remind you to take your prescription on time and, if necessary, make an appointment with your doctor. Targeted Advertising If you believed that the most significant application of data science was in search, think again. Almost anything may be determined using data science algorithms, from display banners on various websites to digital billboards at airports. Because of this, digital advertisements have a much higher CTR (Call-Through Rate) than traditional advertising. They can be customized based on a user's prior behaviors. This explains why, in the same location, while you might see marketing for data science training programs, someone else might see advertisements for clothing.
Slide 5 - Website Suggestions This engine has been aggressively used by several businesses to market their goods depending on user interest and relevant information. Internet businesses like Amazon, Twitter, Google Play, Netflix, Linkedin, IMDb, and many others employ this technique to enhance customer experience. E-Commerce Natural language processing (NLP) and recommendation systems are two examples of machine learning and data science concepts that have significant benefits for the e-commerce industry. E-commerce platforms may employ these strategies to examine customer feedback and transactions in order to gather important data for the growth of their businesses. They analyze texts and online surveys using natural language processing (NLP). It is used in collaborative and content-based filtering to evaluate data and provide better services to its customers. Advanced Text and Image Recognition Data science algorithms control speech and image recognition. We may observe the fantastic work of these algorithms in our daily life. Have you ever had a need for a virtual speech assistant like Siri, Alexa, or Google Assistant? On the other hand, its speech recognition technology is at work in the background, making an e ort to understand and assess your words and providing helpful results from your use. Among other social networking sites, image recognition can be found on Facebook, Instagram, and Twitter. These applications o er to tag
Slide 6 - people when you publish a picture of yourself with them on your profile. Gaming More and more developers are using machine learning algorithms to make games that evolve and improve as the player advances through the stages. In motion gaming, your adversary (the computer) also analyses your prior moves and modifies the game accordingly. Data science has been employed by companies like EA Sports, Zynga, Sony, Nintendo, and Activision-Blizzard to advance gaming. Security Data science can be used to strengthen security at your business and safeguard important data. For instance, banks deploy complex machine-learning algorithms to identify fraud based on a user's typical financial behavior. These algorithms can identify fraud faster and more accurately than individuals because of the enormous volume of data generated every day. Such algorithms can be used to secure private information even if you don't work for a financial institution. Understanding data privacy may assist your business in avoiding the misuse of and sharing of sensitive consumer data including contact details, Social Security numbers, and credit card numbers. Customer Insights Information on your clients' activities, demographics, hobbies, aspirations, and other details may be found in their data. With so many potential consumer data sources, having a rudimentary understanding of data science may help make sense of it. When a consumer visits your website or physical store, adds something to their shopping cart, makes a purchase, reads an email, or engages with a social media post, for instance, you might collect information about them
Slide 7 - every time. Data wrangling is the process of integrating the data once you have double-checked that it is accurate from each source. Conclusion There are other areas where data science can be applied as well. In addition to these applications, data science is employed in marketing, finance, human resources, healthcare, public policy, and any other sector that produces data. Data science is used by marketing teams to identify the products that will sell the most. When analytical thinking and machine learning algorithms are combined, data can o er insights, support e ciency measures, and support projections.
 

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