{"id":1454,"date":"2023-01-19T21:52:40","date_gmt":"2023-01-19T21:52:40","guid":{"rendered":"https:\/\/uniquelines.co\/?p=1454"},"modified":"2023-03-16T14:33:06","modified_gmt":"2023-03-16T14:33:06","slug":"what-is-data-science-and-why-is-it-important-with","status":"publish","type":"post","link":"https:\/\/uniquelines.co\/?p=1454","title":{"rendered":"What Is Data Science and Why Is It Important? With Examples"},"content":{"rendered":"<div id=\"toc\" style=\"background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;padding: 1em;width: 350px;\">\n<p class=\"toctitle\" style=\"font-weight: 700;text-align: center;\">Content<\/p>\n<ul class=\"toc_list\">\n<li><a href=\"#toc-0\">What is the difference between data science and business analytics?<\/a><\/li>\n<li><a href=\"#toc-1\">Proyek Akhir Analitis Data Google: Selesaikan Sebuah Studi Kasus<\/a><\/li>\n<li><a href=\"#toc-2\">Modeling<\/a><\/li>\n<li><a href=\"#toc-3\">How does data science compare to other related data fields?<\/a><\/li>\n<li><a href=\"#toc-4\">What is Data Science?<\/a><\/li>\n<li><a href=\"#toc-5\">What kinds of problems do data scientists solve?<\/a><\/li>\n<li><a href=\"#toc-6\">Business Intelligence (BI) vs. Data Science<\/a><\/li>\n<li><a href=\"#toc-7\">Statistical Inference and Modeling for High-throughput Experiments<\/a><\/li>\n<\/ul>\n<\/div>\n<p>There are also self-paced options to study data engineering concepts or focus on the visualization components of data science. The outcome remains the same whichever course of study you chose \u00e2\u20ac\u201d you will learn to leverage technology to interpret and predict complex data. While there is an overlap between data science and business analytics, the key difference is the use of technology in each field.<\/p>\n<p>Business wants to make use of the unstructured data which can boost their revenue. Data scientists analyze this information to make sense of it and bring out business insights that will aid in the growth of the business. Oracle\u00e2\u20ac\u2122sdata science platformincludes a wide range of services that provide a comprehensive, end-to-end experience designed to accelerate model deployment and improve data science results. In fact,the platform market is expected to growat a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"308px\" alt=\"data science\"\/><\/p>\n<p>Analyze \u00e2\u20ac\u201d This stage is when multiple types of analyses are performed on the data. The analysis stage involves data reporting, data visualization, business intelligence and decision making. For example, finance companies can use a customer\u00e2\u20ac\u2122s banking and bill-paying history to assess creditworthiness and loan risk. You know what is data science, next up know the difference between business intelligence and data science, and know why you can&#8217;t use it interchangeably. Business intelligence is a combination of the strategies and technologies used for the analysis of business data\/information.<\/p>\n<p>Data scientists have a deep technical understanding of computer programming, data mining, AI, and predictive analytics, helping them to organize and analyze information. While technical ability is important to this profession, data scientists should also consider honing strong soft skills like effective communication. Data science platforms are built for collaboration by a range of users including expert data scientists,citizen data scientists,data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.<\/p>\n<p>Like data science, it can provide historical, current, and predictive views of business operations. After the data has been rendered into a usable form, it\u00e2\u20ac\u2122s fed into the analytic system\u00e2\u20ac\u201dML algorithm or a  statistical model. This is where the data scientists analyze and identify patterns and trends. Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction.<\/p>\n<h2 id=\"toc-0\">What is the difference between data science and business analytics?<\/h2>\n<p>Diagnostic analysis is a deep-dive or detailed data examination to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event. Data science has critical applications across most industries, and is one of the most in-demand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets.<\/p>\n<p>While the terms may be used interchangeably, data analytics is a subset of data science. Data science is an umbrella term for all aspects of data processing\u00e2\u20ac\u201dfrom the collection to modeling to insights. On the other hand, data analytics is mainly concerned with statistics, mathematics, and statistical analysis. It focuses on only data analysis, while data science is related to the bigger picture around organizational data.In most workplaces, data scientists and data analysts work together towards common business goals. A data analyst may spend more time on routine analysis, providing regular reports.<\/p>\n<h2 id=\"toc-1\">Proyek Akhir Analitis Data Google: Selesaikan Sebuah Studi Kasus<\/h2>\n<p>Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other  aspect of human life. We have text, audio, video, and image data available in vast quantities. An electronics firm is developingultra-powerful 3D-printed sensors to guide tomorrow\u00e2\u20ac\u2122s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width=\"303px\" alt=\"data science\"\/><\/p>\n<p>Put simply, data science refers to the practice of getting actionable insights from raw data. Our guide will walk you through the ins and outs of the data science field, including how it works and examples of how it\u00e2\u20ac\u2122s being used today. Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding. They primarily trace and supervise the working procedures of all data science team members.<\/p>\n<h2 id=\"toc-2\">Modeling<\/h2>\n<p>Most of the finance companies are looking for the data scientist to avoid risk and any type of losses with an increase in customer satisfaction. In the healthcare sector, data science is providing lots of benefits. Data science is being used for tumor detection, drug discovery, medical image analysis, virtual medical bots, etc. When you upload an image on Facebook and start getting the suggestion to tag to your friends. This automatic tagging suggestion uses image recognition algorithm, which is part of data science. Now if you have a problem which needs to deal with the organization of data, then it can be solved using clustering algorithms.<\/p>\n<ul>\n<li>This one-unit course showcases the power of data science to inform and impact all aspects of our lives and communities.<\/li>\n<li>Ou need to consider whether your existing tools will suffice for running the models or it will need a more robust environment .<\/li>\n<li>Make sure that the service you choose makes it easier to operationalize models, whether it\u00e2\u20ac\u2122s providing APIs or ensuring that users build models in a way that allows for easy integration.<\/li>\n<li>Data science and BI are not mutually exclusive\u00e2\u20ac\u201ddigitally savvy organizations use both to fully understand and extract value from their data.<\/li>\n<li>Despite the promise of data science and huge investments in data science teams, many companies are not realizing the full value of their data.<\/li>\n<li>The other type of problem occurs which ask for numerical values or figures such as what is the time today, what will be the temperature today, can be solved using regression algorithms.<\/li>\n<\/ul>\n<p>Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it\u00e2\u20ac\u2122s not always required. That said, a related bachelor\u00e2\u20ac\u2122s degree can certainly help\u00e2\u20ac\u201dtry studying data science, statistics, or computer science to get a leg up in the field. Data scientists determine the questions their team should be asking and figure out how to answer those questions using data.<\/p>\n<h2 id=\"toc-3\">How does data science compare to other related data fields?<\/h2>\n<p>We can refer to this type of problem which has only two fixed solutions such as Yes or No, 1 or 0, may or may not. And this type of problems can be solved using classification algorithms. In the decision tree, we start from the root of the tree and compare the values of the root attribute with record attribute.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width=\"302px\" alt=\"data science\"\/><\/p>\n<p>Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service. Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information.<\/p>\n<h2 id=\"toc-4\">What is Data Science?<\/h2>\n<p>On the basis of this comparison, we follow the branch as per the value and then move to the next node. We continue comparing these values until we reach the leaf node with predicated class value. Matplotlib \u00e2\u20ac\u201d It provides an object-oriented API for embedding plots into applications. It creates a figure or plotting area in a figure, plots some lines in a plotting area. It allows developers to perform fast array processing with minor coding changes. Make sure the platform includes support for the latest open source tools, common version-control providers, such as GitHub, GitLab, and Bitbucket, and tight integration with other resources.<\/p>\n<p>On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier. FocusBusiness intelligence focuses on both Past and present dataData science focuses on past data, present data, and also future predictions. A data scientist\u00e2\u20ac\u2122s role and day-to-day work vary depending on the size and requirements of the organization.<\/p>\n<h2 id=\"toc-5\">What kinds of problems do data scientists solve?<\/h2>\n<p>Get a crash course in the basics withIBM\u00e2\u20ac\u2122s <a href=\"https:\/\/globalcloudteam.com\/data-science-what-is-it-and-how-to-become-a-data-scientist\/\">data science<\/a> Professional Certificate. If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. A data scientist earns an average salary of $122,499 in the United States as of April 2022, according to Glassdoor .<\/p>\n<p>Data science workflows are not always integrated into business decision-making processes and systems, making it difficult for business managers to collaborate knowledgeably with data scientists. Without better integration, business managers find it difficult to understand why it takes so long to go from prototype to production\u00e2\u20ac\u201dand they are less likely to back the investment in projects they perceive as too slow. Despite the promise of data science and huge investments in data science teams, many companies are not realizing the full value of their data.<\/p>\n<h2 id=\"toc-7\">Statistical Inference and Modeling for High-throughput Experiments<\/h2>\n<p>You will apply Exploratory Data Analytics using various statistical formulas and visualization tools. These relationships will set the base for the algorithms which you will implement in the next phase. Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc .<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Content What is the difference between data science and business analytics? Proyek Akhir Analitis Data Google: Selesaikan Sebuah Studi Kasus Modeling How does data science compare to other related data fields? What is Data Science? What kinds of problems do data scientists solve? Business Intelligence (BI) vs. Data Science Statistical Inference and Modeling for High-throughput [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[52],"tags":[],"class_list":["post-1454","post","type-post","status-publish","format-standard","hentry","category-software-development"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/uniquelines.co\/index.php?rest_route=\/wp\/v2\/posts\/1454","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uniquelines.co\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uniquelines.co\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uniquelines.co\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/uniquelines.co\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1454"}],"version-history":[{"count":1,"href":"https:\/\/uniquelines.co\/index.php?rest_route=\/wp\/v2\/posts\/1454\/revisions"}],"predecessor-version":[{"id":1455,"href":"https:\/\/uniquelines.co\/index.php?rest_route=\/wp\/v2\/posts\/1454\/revisions\/1455"}],"wp:attachment":[{"href":"https:\/\/uniquelines.co\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1454"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uniquelines.co\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1454"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uniquelines.co\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1454"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}