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Top 10 Data Science Tools of 2020

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Data Science has proven to be a boon to both the IT and the business. The innovation incorporates acquiring value from information, understanding the data and its patterns, and afterward anticipating or producing results from it. Data scientists play a fundamental job in this since they are responsible for organizing, evaluating, and studying data and its patterns. Not just having suitable qualifications and education, a successful data scientist must be skilled at a specific set of tools. He should be conversant in at least one of the tools from the lifecycle of a data science journey, in particular: data acquisition or capture, data cleaning, data warehousing, data exploration or analyzing, and finally, data visualization.


"How AI will power the next-gen applications in Connected Industries (CI)"

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Hiroshige Seko, the minister of Economy Trade and Industry (METI) of Japan introduced a new concept for their roadmap to realize'Society 5.0' the future urbanism as the next big thing in industries. He mentioned that we require another industrial revolution using advanced technological innovations including, AI, IoT, and Big Data; this would be'Connected Industries.' This was the inception of'Connected Industries' as introduced by Hiroshige with the impact on future lives. Artificial Intelligence or AI will be on a next-level role in this development, with a more significant impact on each ecosystem entity. Before moving ahead to understand the role of AI in the'Connected Industries', let's first understand AI and its applications.


The insideBIGDATA IMPACT 50 List for Q4 2020 - insideBIGDATA

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The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. Our in-box is filled each day with new announcements, commentaries, and insights about what's driving the success of our industry so we're in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they're impacting the enterprise through leading edge products and services. We're happy to publish this evolving list of the industry's most impactful companies! The selected companies come from our massive data set of vendors and industry metrics.


Microsoft Teams will use artificial intelligence to better reduce background noises in video conferences

USATODAY - Tech Top Stories

Always worried about the potential for embarrassing background noises at home during video meetings? Microsoft is working on an update that could save you from future videoconferencing faux pas. The company's Microsoft 365 roadmap lists as in development "AI-based real-time noise suppression," which is scheduled for release in November 2020. The feature, spotted by news site Windows Latest, "will automatically remove unwelcome background noise during your meetings." Artificial intelligence technology is used to analyze a user's audio and "specially trained deep neural networks" will filter out noises and keep the person's voice, the software giant's planning document says.


The Emerging Architectures for Modern Data Infrastructure

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As an industry, we've gotten exceptionally good at building large, complex software systems. We're now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. In fact, many of today's fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond.


Ten Research Challenge Areas in Data Science · Harvard Data Science Review

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To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving. Although data science builds on knowledge from computer science, engineering, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: fundamental scientific questions and pressing problems of societal importance.


Global Big Data Conference

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Bad actors use machine learning to break passwords more quickly and build malware that knows how to hide, experts warn. Three cybersecurity experts explained how artificial intelligence and machine learning can be used to evade cybersecurity defenses and make breaches faster and more efficient during a NCSA and Nasdaq cybersecurity summit. Kevin Coleman, the executive director of the National Cyber Security Alliance, hosted the conversation as part of Usable Security: Effecting and Measuring Change in Human Behavior on Tuesday, Oct. 6. Elham Tabassi, chief of staff information technology laboratory, National Institute of Standards and Technology, was one of the panelists in the "Artificial Intelligence and Machine Learning for Cybersecurity: The Good, the Bad, and the Ugly" session.text Attackers can use AI to evade detections, to hide where they can't be found, and automatically adapt to counter measures," Tabassi said.


How to Drive Insight and Action in Today's Fast and Furious Data World

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Why data? Good outcomes depend on good decisions, which depend on good data. The decision value chain is giving up its secrets. Let's have fun figuring out how.


What Is The Future Of Data Science In 2020

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Programming Skills like R, Python, and SAS are the most commonly used tools by the data scientists. Explore R vs Python vs SAS for Data Science and choose the most suitable tool to start your Data Science learning. Get your hands dirty with Data This field uses scientific methods and algorithms. And apply this approach in processing, cleaning and verifying the data. Good hands-on Machine Learning Skills As we have discussed above it is the driving force behind data science.


Why Use Machine Learning For Anomaly Detection: Interview With Siddharth Bhatia

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"Anomaly detection has great significance in detecting fake profiles in Social Networks like Twitter, Facebook, Amazon reviews, and even financial frauds." For this week's ML practitioner's series, Analytics India Magazine got in touch with Siddharth Bhatia, who is into machine learning research at National University of Singapore (NUS). His work focuses majorly on Streaming Anomaly Detection. At NUS, he is supported by a President's Graduate Fellowship. He has also been previously recognised as a young researcher in the ACM Heidelberg Laureate Forum.