{"id":8221,"date":"2018-12-31T15:48:45","date_gmt":"2018-12-31T10:18:45","guid":{"rendered":"http:\/\/ivyproschool.com\/blog\/?p=8221"},"modified":"2021-06-22T18:10:36","modified_gmt":"2021-06-22T12:40:36","slug":"top-5-data-science-and-ai-trends-to-look-out-for-in-2019","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/top-5-data-science-and-ai-trends-to-look-out-for-in-2019\/","title":{"rendered":"Top 5 Data Science and AI Trends to look out for in 2019"},"content":{"rendered":"


\n\"\"<\/a><\/p>\n

 <\/p>\n

Data is omnipresent and AI is permeating into our everyday lives. There are numerous new technologies expanding into new horizons that are only beginning to be understood and appreciated. Now, as we are ready to enter 2019, let\u2019s take a look at 5 major trends that have already set their steady foot and it\u2019s just about time before they revolutionise the world.<\/p>\n

    \n
  1. Edge computing<\/strong><\/li>\n<\/ol>\n

    Edge computing is the practice of processing data near the edge of your network, instead of in a centralised data-processing warehouse. In simple words, Edge computing is done at or near the data source thus providing a real-time local data analysis. It allows smart applications and devices to respond to data almost instantaneously, as its being created, eliminating lag time. This is critical for technologies such as self-driving cars and in the realisation of physical computing, smart cities and the IoT. The global data sphere will predictably grow from 33 zettabytes in 2018 to 175 by 2025. Nearly 30% of the world\u2019s data will need real-time processing.<\/p>\n

      \n
    1. AI Chatbots<\/strong><\/li>\n<\/ol>\n

      A Chatbot is software that can simulate human-like conversations (text or audio) with customers. The chatbot is only expected to grow bigger and better, and consumers are welcoming the convenience of intelligent voice search with open arms. It is estimated that by 2020, half of all search queries will be voice-based and chatbots will power 85% of all customer service interactions. Bots will be able to quickly digest, process and report business data when it is needed. Customers, on the other hand, can receive summarised answers to questions involving product and service lines, or technical support issues.<\/p>\n

        \n
      1. Augmented Reality (AR)<\/strong><\/li>\n<\/ol>\n

        The much raved about the launch of 2016, Pok\u00e9mon Go, had put the AR into the spotlight and many other similar apps have been created ever since. There has been a plethora of AR announcements over the last year – Mozilla\u2019s Firefox Reality, Snapchat\u2019s Lens Studio, Apple\u2019s ARkit, Facebook\u2019s Spark AR Studio and Google\u2019s ARCore. Such heavy investment in technology indicates real potential beyond entertainment, and there are some people looking deeper into AR and how to combine it with other emerging technologies.<\/p>\n

          \n
        1. Blockchain<\/strong><\/li>\n<\/ol>\n

          For the longest time, Blockchain has been inextricably tied to cryptocurrencies, but that\u2019s simply one application of the technology. A variety of industries \u2013 from manufacturing to retail \u2013 will start to explore the improvements Blockchain can bring to supply chain transparency, ownership tracking, and others. According to a PWC report, 77% of financial institutions are expected to adopt Blockchain technology as part of an in-production system or process by 2020. By 2022, Gartner predicts that at least five countries will issue a national cryptocurrency and by 2022, more than a billion people will have some data about them stored on a Blockchain, but they may not be aware of it.<\/p>\n

            \n
          1. Predictive Analytics<\/strong><\/li>\n<\/ol>\n

            Predictive analytics<\/a> is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. The idea behind machine learning<\/a> is to reduce errors in an automated way. In business, predictive analytics<\/a> models explore\u00a0patterns\u00a0found in transaction data\u00a0to identify risk and opportunity for the future.\u00a0According to a report, the global predictive analytics market\u00a0was valued at approximately USD 3.49 billion in 2016 and is expected to reach approximately USD 10.95 billion by 2022, growing at a compound annual growth rate of around 21% between 2016 and 2022.<\/p>\n

            This article has been contributed by our student Shreya Jaiswal. Shreya is pursuing her Actuaries<\/a> and Data Science at Ivy Pro School. She has graduated in Mathematics and has a keen eye for the latest in Technology, Machine Learning and AI. Her LinkedIn Connect: https:\/\/www.linkedin.com\/in\/shreya-jaiswal-7a9295119<\/p>\n

            Ivy Pro School<\/a>\u00a0is one of the first few Big Data Analytics <\/a>Training institutions in the country since 2008. Ranked among the Top 5 in the country and No 1 in East India, Ivy is known for its quality of content, hands-on learning approach. Ivy\u2019s expert faculty come from esteemed institutes like IIM, IIT, ISI and US universities. Ivy is the official learning partner for many Large Analytics companies like Honeywell, Genpact, Capgemini, HSBC Analytics, eBay\/PayPal among many others. Ivy conducts courses on Data Science and\u00a0Machine Learning in Bangalore<\/em><\/strong>, Kolkata, New Delhi and Pune.<\/p>\n","protected":false},"excerpt":{"rendered":"

              Data is omnipresent and AI is permeating into our everyday lives. There are numerous new technologies expanding into new horizons that are only beginning to be understood and appreciated. Now, as we are ready to enter 2019, let\u2019s take a look at 5 major trends that have already set their steady foot and it\u2019s just about time before they revolutionise the world. Edge computing Edge computing is the practice of processing data near the edge of your network, instead of in a centralised data-processing warehouse. In simple words, Edge computing is done at or near the data source thus providing a real-time local data analysis. It allows smart applications and devices to respond to data almost instantaneously, as its being created, eliminating lag time. This is critical for technologies such as self-driving cars and in the realisation of physical computing, smart cities and the IoT. The global data sphere will predictably grow from 33 zettabytes in 2018 to 175 by 2025. Nearly 30% of the world\u2019s data will need real-time processing. AI Chatbots A Chatbot is software that can simulate human-like conversations (text or audio) with customers. The chatbot is only expected to grow bigger and better, and consumers […]<\/p>\n","protected":false},"author":7,"featured_media":8232,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[851,5,881],"tags":[22,714,56,76,230,467,728],"class_list":["post-8221","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-analytics","category-data-analytics","category-data-science","tag-analytics","tag-artificial-intelligence","tag-big-data-analytics","tag-business-analytics","tag-data-analytics","tag-data-science","tag-data-science-career"],"yoast_head":"\nTop 5 Data Science and AI Trends in 2019 | Official Blog | Ivy Pro School<\/title>\n<meta name=\"description\" content=\"This blog discusses the trends in Data Science and AI to look out for in 2019. 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