data science

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Published By: Intel     Published Date: Jun 07, 2017
Using the Integrated Analytics Hub, data analytics projects have already accounted for an estimated quarterly savings on marketing digital-media expenditures of approximately USD 170,000. Download this white paper to find out more.
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intel, analytics, data, data analytics, data science
    
Intel
Published By: Intel     Published Date: Jun 07, 2017
Intel's Bob Rogers, chief data scientist for big data solutions, sat down with Dan Magestro, research director at the international Institute of Analytics (IIA), to discuss the power of asking questions when assessing an organisation's analytics maturity. Read on to find out more.
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intel, analytics, data, data analytics, data science
    
Intel
Published By: Sage People     Published Date: Dec 04, 2017
The more you know about your people, the more you can enable them to do their best work. And in turn, the greater the chance of business success. Yet, a rapidly changing world of work makes it difficult for companies to achieve this. There is a growing global skills crisis, and it’s getting worse. A shortage of skilled people makes it tough to find and attract the people you need — and it’s even tougher to get them through the door once you find them. To win the war for talent, you need to understand and engage with your candidates better than ever before.
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data, reporting, analytics, insights, science, sage, solutions
    
Sage People
Published By: Pure Storage     Published Date: Apr 18, 2018
Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data. Amazon CEO Jeff Bezos spoke about the potential of artificial intelligence and machine learning at the 2017 Internet Association‘s annual gala in Washington, D.C., “It is a renaissance, it is a golden age,” Bezos said. “We are solving problems with machine learning and artificial intelligence that were in the realm of science fiction for the last several decades. Natural language understanding, machine vision problems, it really is an amazing renaissance.” Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy
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Pure Storage
Published By: Dassault Systèmes     Published Date: Nov 12, 2019
The Life Sciences industry has changed significantly over the past 10 years. With a view to developing new, more effective treatments, industry leaders are exploring new therapeutic areas and approaches like biologics and precision medicine. To address this shift, pharmaceutical and medical devices manufacturers look to connect systems, people and data - characterized by more predictive and adoptive facilities that leverage machine learning, 3D modeling, Industrial Internet of Things (IIOT), digital twin, and augmented reality. This eBook explains the main challenges of manufacturing and explores 5 manufacturing experiences that disrupt the future of manufacturing in the Life Sciences industry.
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iiot, medical device, life sciences, machine learning, 3d modeling, digital twin
    
Dassault Systèmes
Published By: Teradata     Published Date: Oct 15, 2012
Does your organization struggle to get new business insights from all data types with rapid exploration?
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data scientists, analyst, statistician, quants, quantitative analyst, scientist, data science, business technology
    
Teradata
Published By: Akamai Technologies     Published Date: Sep 10, 2019
Contemporary internet threats are sophisticated and adaptable, they continuously change their complexion to evade security defenses. Traditional rigid, deterministic, rule-based security research are becoming less effective. Security research approaches employing data science methods to implement anomalies-based analysis across very large volumes of anonymized data are now essential. This paper will: • Briefly cover security research challenges in today’s threat landscape • Explain why DNS resolution data is a rich resource for security research • Describe how Akamai teams use DNS data and data science to create better threat intelligence • Discuss improvements in threat coverage, accuracy, and responsiveness to today’s agile threats
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Akamai Technologies
Published By: Pure Storage     Published Date: Nov 02, 2017
In the new age of big data, applications are leveraging large farms of powerful servers and extremely fast networks to access petabytes of data served for everything from data analytics to scientific discovery to movie rendering. These new applications demand fast and efficient storage, which legacy solutions are no longer capable of providing.
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big data analytics, genomics, medicine, digital science, engineering, design, software, development, next-generation
    
Pure Storage
Published By: Exabeam     Published Date: Sep 25, 2017
In evaluating UEBA solutions’ ability to detect, prioritize, and respond, it is important to understand the full potential of data sciencedriven analytics. Organizations should ask their vendors if they can support the following Top 12 UEBA use cases, and most importantly, demand that the vendor demonstrate this support within the POC or pilot.
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Exabeam
Published By: TIBCO Software     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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TIBCO Software
Published By: Trifacta     Published Date: Feb 12, 2019
Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. Businesses now have the ability to work with data at a scale and speed that many of us would have never thought was possible. Yet, why are so many organizations still struggling to drive meaningful ROI from their data investments? The answer starts with people. In this latest Data Science Central webinar, guest speakers Forrester Principal Analyst Michele Goetz and Trifacta Director of Product Marketing Will Davis focus on the roles and responsibilities required for today’s modern dataops teams to be successful. They touch on how new data platforms and applications have fundamentally changed the traditional makeup of data/analytics organizations and how companies need to update the structure of their teams to keep up with the accelerate pace of modern business. Watch this recorded webcast to learn: What are the foundational roles within a modern dataops team a
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Trifacta
Published By: Aberdeen Group     Published Date: Nov 13, 2015
Aberdeen’s Content Marketing survey revealed that while 95% of marketers are using or considering using a content marketing strategy, there are some distinct differences between those using content well and those just using content. The Best-in-Class are not only creating content at volume, they are taking a much more data-driven approach to their content marketing strategy — and it’s paying off. Find out how.
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customer acquisition, marketing leads, marketing challenges, marketing messages, contact management, data science, demand generation, email marketing, inbound marketing, lead generation, lead intelligence, market research, integrated marketing, lead nurturing, marketing analytics
    
Aberdeen Group
Published By: Aberdeen Group     Published Date: Nov 13, 2015
Aberdeen’s research shows that 90% of Best-in-Class marketers report fueling lead generation efforts with content marketing. What do you need to know to follow this best practice of the Best-in-Class? That’s exactly what this Knowledge Brief is intended to uncover.
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customer acquisition, marketing leads, marketing challenges, marketing messages, contact management, data science, demand generation, email marketing, inbound marketing, lead generation, lead intelligence, market research, integrated marketing, lead nurturing, marketing analytics
    
Aberdeen Group
Published By: Aberdeen Group     Published Date: Nov 23, 2015
This report examines the pressing need to break down data silos due to the damage they cause to analytical initiatives and user engagement. Read this report to find out more.
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customer acquisition, marketing leads, marketing challenges, marketing messages, contact management, data science, demand generation, email marketing, inbound marketing, lead generation, lead intelligence, market research, integrated marketing, lead nurturing, marketing analytics
    
Aberdeen Group
Published By: Waterline Data & Research Partners     Published Date: May 18, 2015
Waterline Data automates the cataloging of data assets and provides an Amazon.com-like guided shopping approach to data discovery that is intended to take the guesswork out of targeting the right data.
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waterline, big data, automation, cataloging, processing, analysis, assets, data science, data management
    
Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: May 18, 2015
In this report, Forrester Research recommends that application development and delivery (AD&D) professionals working on BI and big data initiatives get the best out of both by designing and integrating them in a flexible data platform.
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waterline, big data, automation, cataloging, processing, analysis, assets, data science, data management
    
Waterline Data & Research Partners
Published By: Oracle HCM Cloud     Published Date: Jun 07, 2016
Leveraging analytics to drive business growth is top-of-mind for corporate (C-Suite) leaders, prompting HR executives to take a more strategic, data-driven approach to workforce management. Learn the art and science of combining workforce data, business data and IT expertise together to allow HR departments to make more effective and efficient decisions about people.
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Oracle HCM Cloud
Published By: IBM APAC     Published Date: May 14, 2019
Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future. You will find topics like: - What is machine learning? - Explaining the business imperative - The key machine learning algorithms - Skills for your data science team - How businesses are using machine learning - The future of machine learning
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IBM APAC
Published By: IBM     Published Date: Jul 14, 2016
This video describes how data scientists, analysts and business users can save precious time by using a combination of SPSS and Spark to uncover and act on insights in big data.
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ibm, data, analytics, predictive business, ibm spss, apache spark, coding, data science, software development, enterprise applications, data management, business technology, data center
    
IBM
Published By: Oracle     Published Date: Mar 01, 2019
Join Oracle’s CX and Marketing Strategy Director, Wendy Hogan, and Senior Vice President Oracle Marketing, Shashi Seth, as they tell how AI, machine learning and data science can engage customers, automate tasks and build ROI. Reaching the right customers on the right channel at the right time, brings rewards for CMOs who embrace these innovations, including engaged customers and increased ROI. Be inspired by the new-generation AI, machine learning and data science and take your marketing to the next level. Watch the webinar.
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Oracle
Published By: IBM APAC     Published Date: Aug 25, 2017
Machine learning automates the development of analytic models that can learn and make predictions on data. It has been one of the fastest growing disciplines within the world of statistics and data science, but the barrier to entry has been high, not only in cost, but also in the need for specialized talent.
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machine learning, apache spark, additional resources, big data, ibm
    
IBM APAC
Published By: IBM     Published Date: Oct 21, 2016
The greatest challenge of the big data revolution is making sense of all the information generated by today's vast digital economy. It's well enough for an organization to collect every slice of data it can reach, but how does it extract value from this massive volume of information?
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ibm, analytics, data science, data, big data, aps data, aps, enterprise applications, data management, data center
    
IBM
Published By: IBM     Published Date: Oct 21, 2016
Between the Internet of Things, customer experience and loyalty programs, social network monitoring, connected enterprise systems and other information sources, today's organizations have access to more data than they ever had before-and frankly, more than they may know what to do with. The challenge is to not just understand that data, but actualize it and use it to recognize real business value. This ebook will walk you through a sample scenario with Albert, a data scientist who wants to put text analytics to work by using the Word2vec algorithm and other data science tools.
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ibm, analytics, aps, aps data, open data science, data science, word2vec, enterprise applications, business technology, data center
    
IBM
Published By: IBM     Published Date: Jan 18, 2017
It's all well enough for an organization to collect every slice of data it can reach, but having more data doesn't mean you'll automatically get better insights. First, you have to figure out what you want from your data you have to find its value.
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ibm, aps data, data science, open data science, analytics, enterprise applications
    
IBM
Published By: IBM     Published Date: Jan 18, 2017
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. But data scientists can only go so far without support.
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ibm, analytics, aps data, open data science, data science, data engineers, enterprise applications
    
IBM
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