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By: Datastax     Published Date: May 14, 2018
"What’s In The Report? The Forrester Graph Database Vendor Landscape discusses the expanding graph uses cases, new and emerging graph solutions, the two approaches to graph, how graph databases are able to provide penetrating insights using deep data relationships, and the top 10 graph vendors in the market today Download The Report If You: -Want to know how graph databases work to provide quick, deep, actionable insights that help with everything from fraud to personalization to go-to-market acceleration, without having to write code or spend operating budget on data scientists. -Learn the new graph uses cases, including 360-degree views, fraud detection, recommendation engines, and social networking. -Learn about the top 10 graph databases and why DSE Graph continues to gain momentum with customers who like its ability to scale out in multi-data-center, multi-cloud, and hybrid environments, as well as visual operations, search, and advanced security."
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     Datastax
By: Datarobot     Published Date: May 14, 2018
The DataRobot automated machine learning platform captures the knowledge, experience, and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods
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     Datarobot
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.
Tags : intel, analytics, data, data analytics, data science
     Intel
By: Teradata     Published Date: Oct 15, 2012
Does your organization struggle to get new business insights from all data types with rapid exploration?
Tags : data scientists, analyst, statistician, quants, quantitative analyst, scientist, data science
     Teradata
By: Virgin Pulse     Published Date: Jun 02, 2017
This report includes analysis from behavior and data scientists to help you understand why your employees may not be working at optimal levels and discover ways to address this growing problem.
Tags : presenteeism, employee presenteeism, workforce presenteeism, job presenteeism, presenteeism in the workplace, worker productivity, workforce productivity
     Virgin Pulse
By: SAS     Published Date: Apr 20, 2015
To create real business value with data scientists, top management must learn how to manage them effectively.
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     SAS
By: EMC Corporation     Published Date: Jul 07, 2013
3TIER helps organizations understand and manage the risks associated with renewable energy projects. A pioneer in wind and solar generation risks analysis, 3TIER uses science and technology to frame the risk of weather-driven variability, anywhere on Earth. 3TIER's unique expertise is in combining the latest weather data with historical weather patterns, and using the expertise of 3TIER's meteorologists, engineers and data scientists to create a detailed independent assessment of the future renewable energy potential of any location.
Tags : renewable energy, customer profile, emc, risk management, best practices, storage, technology, security
     EMC Corporation
By: Veritas     Published Date: Oct 03, 2016
This benchmark report, the Data Genomics Index, encompasses a community of like-minded data scientists, industry experts, and thought leaders together with the purpose of better understanding the true nature of the unstructured data that we are creating, storing, and managing on a daily basis - a report on real storage environments’ composition.
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     Veritas
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.
Tags : ibm, data, analytics, predictive business, ibm spss, apache spark, coding, data science
     IBM
By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. Download this white paper to find out how you can improve your data preparation for business analytics.
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     Waterline Data & Research Partners
By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.
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     Waterline Data & Research Partners
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.
Tags : ibm, analytics, aps, aps data, open data science, data science, word2vec
     IBM
By: Veritas     Published Date: May 12, 2016
The Data Genomics Index is a first-of-its-kind benchmark analysis of data stored within a typical enterprise environment. This report reveals insights into data growth, data age, and data type thereby providing organizations with the comparison standard for beginning to take action on their data. In addition to the Index, Veritas has founded the Data Genomics Project. This community of likeminded data scientists, industry experts and thought leaders will come together to surface the true nature of enterprise environments, build the data-genome that matters for information management, and share the discussion with a world struggling to solve tremendous data growth challenges.
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     Veritas
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.
Tags : ibm, aps data, data science, open data science, analytics
     IBM
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.
Tags : ibm, analytics, aps data, open data science, data science, data engineers
     IBM
By: Alteryx, Inc.     Published Date: Apr 21, 2017
The traditional multiple-step, multi-tool legacy approach is a slow, time-consuming, and in most cases, a costly process that prevents organizations from making faster decisions with confidence. Data analysts today need an agile solution that empowers them to take charge of the entire analytics process. Download The Definitive Guide to Self-Service Data Analytics to: Understand why traditional analytic tools designed for data scientists are not ideal for data analysts like you Learn how self-service data analytics delivers the ease of use, speed, flexibility, and scalability you require See how Alteryx stacks up against traditional data prep and analytics tools
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     Alteryx, Inc.
By: Alteryx, Inc.     Published Date: Apr 21, 2017
Data Analytics has become critical for many business decision makers. However, many of these managers and data analysts still rely on spreadsheets and other legacy-era tools that fall far short of current needs. As a result, they also rely heavily on a virtual army of data specialists and scientists, working under the auspices of a centralized analytics group, to prepare, blend, analyze, and even report on the critical data they need for decision making. Download this new paper to get the details behind self-service data analytics, and how it lets business analysts: Take charge of the entire analytical process, instead of relying on other departments Overcome limitations of legacy tools to save time and prevent errors Make more comprehensive and insightful business decisions at speed
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     Alteryx, Inc.
By: Amazon Web Services     Published Date: Feb 01, 2018
At Amazon, we’ve been investing deeply in AI for more than 20 years. Machine learning (ML) algorithms drive many of our internal systems, and have formed the core of our customers' experience —from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, and our new retail experience, Amazon Go. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every executive, developer, and data scientist.
Tags : machine learning, algorithms, interal systems, amazon
     Amazon Web Services
By: SAS     Published Date: May 24, 2018
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. You’ll then be ready to experiment with these methods in SAS Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
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     SAS
By: Oracle     Published Date: Jan 28, 2015
Traditional brick-and-mortar multi-channel retailers have online competitors ruled by data scientists who define retail as a data mining and optimization problem. John Bible, Senior Director of Retail Data Science and Insight at Oracle Retail discusses the science of pricing, and predictions for the role of science in retail over the next five years.
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     Oracle
By: Alteryx, Inc.     Published Date: Sep 06, 2017
According to Forrester, the level of analytic satisfaction within organizations is on the decline.* The traditional multiple-step, multi-tool legacy approach is a slow, time-consuming, and in most cases, a costly process that prevents organizations from making faster decisions with confidence. Data analysts today need an agile solution that empowers them to take charge of the entire analytics process. Download The Definitive Guide to Self-Service Data Analytics to: Understand why traditional analytic tools designed for data scientists are not ideal for data analysts like you Learn how self-service data analytics delivers the ease of use, speed, flexibility, and scalability you require See how Alteryx stacks up against traditional data prep and analytics tools Find out how self-service data analytics bridges the gap across skills, speed, and depth of analysis to empower you to achieve ever-greater insights without coding or depending on other departments.
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     Alteryx, Inc.
By: IBM     Published Date: Oct 21, 2015
IBM SPSS Solutions offer a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end.
Tags : ibm, data, analytics, data scientist, statistician
     IBM
By: SnowFlake     Published Date: Jul 08, 2016
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access to only a few highly-skilled data scientists. However, these efforts are quickly frustrated by the limitations of current data warehouse technologies. These systems simply were not built to handle the diversity of today’s data and analytics. They are based on decades-old architectures designed for a different world, a world where data was limited, users of data were few, and all processing was done in on-premises data centers.
Tags : snowflake, data, technology, enterprise, application, best practices, social media, storage
     SnowFlake
By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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     SAS
By: ARKE University     Published Date: Dec 02, 2015
In this module, you’ll learn how marketers harness the power of data analytics to deliver measurable, value-added results.
Tags : arke, arke university, data analytics, digital marketing, big data, data scientist, web analytics, customer experience/engagement
     ARKE University
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