- published: 12 Oct 2016
- views: 3328
Data (/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.
Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.
The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").
A right is a legal or moral entitlement or permission.
Right or Rights may also refer to:
Trousers (pants in North America) are an item of clothing worn from the waist to the ankles, covering both legs separately (rather than with cloth extending across both legs as in robes, skirts, and dresses).
In the UK the word "pants" generally means underwear and not trousers.Shorts are similar to trousers, but with legs that come down only to around the area of the knee, higher or lower depending on the style of the garment. To distinguish them from shorts, trousers may be called "long trousers" in certain contexts such as school uniform, where tailored shorts may be called "short trousers", especially in the UK.
In most of the Western world, trousers have been worn since ancient times and throughout the Medieval period, becoming the most common form of lower-body clothing for adult males in the modern world, although shorts are also widely worn, and kilts and other garments may be worn in various regions and cultures. Breeches were worn instead of trousers in early modern Europe by some men in higher classes of society. Since the mid-20th century, trousers have increasingly been worn by women as well. Jeans, made of denim, are a form of trousers for casual wear, now widely worn all over the world by both sexes. Shorts are often preferred in hot weather or for some sports and also often by children and teenagers. Trousers are worn on the hips or waist and may be held up by their own fastenings, a belt or suspenders (braces). Leggings are form-fitting trousers, of a clingy material, often knitted cotton and spandex (elastane).
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.
Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on." Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,connectomics, complex physics simulations, biology and environmental research.
My Favorite Star Trek Vignettes The Next Generation, Episode 6x18 - "Starship Mine", Stardate: 46682.4 [Turbolift] DATA: Captain. PICARD: Bridge. DATA: It has been quite a day, has it not? PICARD: Yes, it has. DATA: However, a change of routine is often invigorating and can be a welcome diversion after a long assignment. PICARD: Exactly. DATA: I understand that Arkaria has some very interesting weather patterns. PICARD: Mister Data, are you all right? DATA: Yes, sir. I am attempting to fill a silent moment with non-relevant conversation. PICARD: Small talk. DATA: Yes, sir. I have found that humans often use small talk during awkward moments. Therefore, I have written a new subroutine for that purpose. How did I do? PICARD: Perhaps it was a little too non-relevant. But if you...
" Data" wurde gegründet im Jahre 1978 von Georg Kajanus, bis dahin Kopf von "Sailor". Er wollte Electropop neu erfinden. Aber schon im Jahr 1985 scheiterte "Data" nach drei Alben und Kajanus ging zurück zu Sailor. Data" was founded in 1978 by Georg Kajanus, previously head of "Sailor". He wanted to reinvent Electropop. But already in 1985 "data" failed after three albums and Kajanus went back to Sailor.
The UNESCO Institute for Statistics is producing globally-comparable education data for the monitoring and implementation of SDG 4 and the Education 2030 agenda.
Wie gelangen Unternehmen an die wirklich relevanten Daten aus den Material- und Informationsflüssen ihrer Wertströme? Das hängt maßgeblich von einer optimalen Stammdatenqualität ab. Wie man diese mittels TSV (Total Spending Visibility) erreichen kann, schildert der Videobeitrag.
Patrick Dermak of Adbaker and Philipp Schoeffmann talk about success stories, Facebook hacks and strategies, Facebook creatives, and more at Affiliate World Asia in Bangkok, Thailand. ----- Website: https://affiliateworldconferences.com Facebook: https://www.facebook.com/affiliateworldconferences Twitter: https://twitter.com/AWConferences Instagram: https://www.instagram.com/AWConferences #AWasia
Roger Magoulas, Research Director and Strata Conference Co-chair at O'Reilly Media, talks about how social science matters as part of the picture when looking at data management and analytics. He describes how talking to people and using the techniques of ethnography and anthropology to gather information is critical to making sense of the data.
Hugo Campos is a passionate advocate for the rights of patients to access their data and become empowered participants in their own health care. His $30,000 implantable cardiac defibrillator continually collects information about his body, yet he has no access to this valuable data. He is on a mission to change that. Learn more about TEDxCambridge at http://www.tedxcambridge.com.
Wondering what data plan is right for you? Solenn Heussaff & Nico Bolzico are here to help us find the answer with the GoSURF Drill! 🏋 #ThePLAN http://globe.com.ph/DataDrills
When building out complex data visualizations, where do you even start? We’ll show you in our recent webinar: How to Choose the Right Chart for Your Data. Infragistics Senior VP of Developer Tools, Jason Beres, will walk you through many of the various chart types found in the iOS, Windows Forms and jQuery controls of Infragistics Ultimate – he’ll even go over some data viz do’s and don’ts.
Sometimes payroll can export the right data but put it into the wrong column. In this situation the uploaded file will be rejected as "wrong" by the pension provider. pensionsync users don't suffer this problem because all our data interfaces with pension providers are subjected to rigorous testing by both our own teams and the pension providers teams as part of a collaborative IT project. https://www.pensionsync.com
More than ever before, business leaders expect HR professionals to share insights based on data. Research shows us that 15 per cent of leaders have changed a business decision based on HR analytics in the past year, but only 18 per cent of these leaders trust talent data and insights from their HR function.
The Direct Bank of the Royal Bank of Scotland changes how their employees work to improve customer success using Adobe Analytics, Adobe Experience Manager, and Adobe Target within Adobe Marketing Cloud. To learn more about Adobe Marketing Cloud Solutions, see: http://adobe.ly/1CorT4A
The ninth installment of the Thomson Reuters Knowledge Worker Innovation Series featured Karen Rubin, Product Director, Quantopian, a crowd-sourced quantitative hedge fund that offers a free algorithmic trading platform. Karen set out to understand something she was passionate about: investing in female CEOs of Fortune 1000 companies.
Competitive Enterprise Institute Vice President Jim Harper weighs in on the NSA’s overreach and an upcoming Supreme Court case that would determine if a person has a right to privacy for the data they transmit.
Best practices for growth through the right strategies at the right time.
Machine learning has its challenges, and understanding the algorithms is not always easy. In this session, you'll discover methods to make these challenges less daunting.
Recorded at DataEngConf SF17 in April, 2017. With more than a decade of Big Data experience now behind us, we’ll talk with a few veterans from the front lines about what skills mattered — and what didn't — on the first data engineering teams at Silicon Valley’s data-intensive start-ups. This will be a must-watch panel for those looking to build out a data-engineering function or get into the field themselves. We’ll explore the build vs. buy debate, looking at which classes of software teams hand-rolled, borrowed (and extended) from open-source projects, or bought - and discuss how that mix is changing. We’ll learn about the hardest parts of data pipelines and the data stack to build. And we’ll highlight the necessary skills that make data engineers different than data scientists.
When building out complex data visualizations, where do you even start? We’ll show you in our recent webinar: How to Choose the Right Chart for Your Data. Infragistics Senior VP of Developer Tools, Jason Beres, will walk you through many of the various chart types found in the iOS, Windows Forms and jQuery controls of Infragistics Ultimate – he’ll even go over some data viz do’s and don’ts.
The ninth installment of the Thomson Reuters Knowledge Worker Innovation Series featured Karen Rubin, Product Director, Quantopian, a crowd-sourced quantitative hedge fund that offers a free algorithmic trading platform. Karen set out to understand something she was passionate about: investing in female CEOs of Fortune 1000 companies.
Are you an entrepreneur looking to share data with your investors, stakeholders, or consumers - but you don't know where to start? Visual presentation of information is a necessary skill in modern communication, even if you don't know the first thing about Photoshop. Join William Beutler and Jenny Karn, of digital agency Beutler Ink, to learn how you can use data to share powerful visual stories with any audience. You will learn: • The do's and don'ts of data visualization • The importance of analysis alongside the numbers • Choosing the right presentation for your data • Tools that can help with - or without - a budget Have questions about Data Visualization for Non-Programmers? Contact Harvard Innovation Lab Learn more about the Harvard Innovation Lab at http://i-...
Machine learning has its challenges, and understanding the algorithms is not always easy. In this session, you'll discover methods to make these challenges less daunting.
Recorded at DataEngConf SF17 in April, 2017. With more than a decade of Big Data experience now behind us, we’ll talk with a few veterans from the front lines about what skills mattered — and what didn't — on the first data engineering teams at Silicon Valley’s data-intensive start-ups. This will be a must-watch panel for those looking to build out a data-engineering function or get into the field themselves. We’ll explore the build vs. buy debate, looking at which classes of software teams hand-rolled, borrowed (and extended) from open-source projects, or bought - and discuss how that mix is changing. We’ll learn about the hardest parts of data pipelines and the data stack to build. And we’ll highlight the necessary skills that make data engineers different than data scientists.
Best practices for growth through the right strategies at the right time.
The presentation touches on the past, present and future of Data Science, particularly focusing on the current goals and challenges. We will look at the patterns around successful (and less successful) journeys from innovation to production and the technology frameworks available. The presentation showcases an Analytical Design Pattern that takes IoT data and applies Machine Learning in a real time Stream. Finally we take a look at the emerging trends for data science around cognitive AI and Deep Learning. Ian Sharp is the Lead Data Scientist for Oracle UK with over 15 years’ experience with Oracle Core Technology, specialising in Machine Learning, Statistics and Business Analytics. Ian has helped customers deliver analytical successes across Finance, Media, Entertainment, Retail and Comm...
Using Data to Tell the Right Story to the Right Donors (The Right Way) Retaining donors is one of the biggest challenges nonprofits face today. With so many worthwhile organizations out there it’s hard to stand out in the crowd and keep the donors you have coming back for more. Mile High United Way in Denver, CO, was facing this ever-pressing “retention problem” and looked to Salesforce for a solution. Mile High United Way partners with local nonprofits, government agencies, policy-makers and businesses to collectively solve Denver’s community-wide problems. Hear from Nicole Adair, United Way’s Senior Director of Development Operations and Donor Communications, about how they’re using Salesforce’s Marketing Cloud to drive their email marketing and the promising results they’re seeing on...
With AWS, you can choose the right storage service for the right use case. Given the myriad of choices, from object storage to block storage, this session profiles details and examples of some of the choices available to you. Learn about real-world deployments from customers who are using Amazon Simple Storage Service (Amazon S3), Amazon Elastic Block Store (Amazon EBS), Amazon Glacier, and AWS Storage Gateway.
Telling stories with data has become one of the most important ways for companies to communicate effectively and develop positive customer relationships. Peter Arvai, chief executive of Prezi, Andrew Crow, experience design director of GE, and Pete Flint, chief executive of Trulia, discuss how to make the most of your data—and where data visualization is going next, at The Economist's Information Forum on June 4th, 2013 in San Francisco. The session was moderated by The Economist's Martin Giles. For more about Economist Events, visit: http://econ.st/ZaP1jf
Dr Lior Horesh, IBM, talks on ‘From Big Data to Right Data: A Hybrid First-Principles Machine-Learning Meta-Level Optimization Approach’. This was presented at The Alan Turing Institute’s workshop on Optimal Experimental Design and Inverse Problems, funded by the Turing – Lloyd’s Register Foundation Programme on Data-Centric Engineering.
Join us for a discussion about how to collect and share critical anomalies, change and threat indicators across your enterprise. Break down your data silos by delivering the right information to the right stakeholders across policies, business units and specific audit requirements. • What are the current best practices and methodologies for collecting and sharing data? • What is the best way to go beyond file system monitoring and expand the breadth of data collection? • How can IT consolidate change insight in mixed environments, including the cloud? • Is it possible to easily provide insight and business context across different polices and departments?
With Dr. Dean Wampler, Office of the CTO and Fast Data Architect at Lightbend, Inc. Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? Batch-mode processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention. In this webinar, Lightbend’s Big Data Architect, Dr. Dean Wampler, examines the rise of streaming systems for handling time-sensitive problems. We’ll explore the characteristics of fast data architectures, and the open source tools for implementing them. We’ll also take a brief look at Lightben...
Recording of webinar from March 2, 2017. Learn everything you need to know about the program directly from the UBC Master of Data Science faculty and staff, so you can make the best decision for your future. Presented by Paul Gustafson and Giuseppe Carenini, MDS Co-Directors, and Milad Maymay, Director, Program Operations and Student Management, this live webinar.
In Wrike, you can easily pick and choose what you'd like to share, from a single task to your entire workspace. Watch to learn all the tips and tricks for using Wrike's sharing feature. Not using Wrike yet? Start your free trial: https://www.wrike.com/tour/#getstarted/?utm_source=socials&utm;_medium=youtube&utm;_campaign=videos Also, check out our short guide about the "Share" feature in Wrike: http://bit.ly/1hfJGqt Wrike website: https://www.wrike.com/?utm_source=socials&utm;_medium=youtube&utm;_campaign=videos Find great tips on our blog: https://www.wrike.com/blog/?utm_source=socials&utm;_medium=youtube&utm;_campaign=videos
www.IoT6Exchange.com Keynote Sponsor Presentation - IIoT and Predictive Analytics - The Right Data, The Right Time, The Right Predictive Response Moderator: Nadine Manjaro, Lead IoT Consultant & CEO, Beyond Machine to Machine Communications Speakers: Drew Conway, CEO, Alluvium | Stuart Finn, Global Operations Executive, DataRPM Description: The session dives into key trends, insights, and industry solutions around predictive analytics, the considerations for real-time data access and analytics, and leveraging process, manufacturing, and industrial machine information to increase production, reduce downtime, and improve efficiencies. www.nGagevents.com
The first choice many young data scientists face is staying in academia or moving to industry. For some, like Shubha Nabar, director of data science at Salesforce.com, “industry affords you the pleasure of seeing your products deployed in the real world and affecting the lives of millions.” But when Bin Yu, a professor at the University of California, Berkeley, worked at Bell Labs, she missed the feeling of youth that’s part of academia, she said during the Women in Data Science (WiDS) Conference at Stanford on Nov. 2, 2015. Aleksandra Korolova, on the computer science faculty at the University of Southern California, was faced with the choice of taking a high-paying job at Facebook or staying in academia to finish her PhD. “I knew I wanted to be a scientist and understand problems in a d...
Are you looking – or struggling – to hire the right data scientist for your team? We’ve partnered with DataScience.com to discuss the complexities of hiring these “unicorns”, and how you can revamp your hiring process! Data scientists, the unicorns of the analytical world over the past few years, help to provide insights that can make a big impact on your bottom line, but just hiring any data scientist isn’t a shoe-in for your business, because you need to hire the right data scientist who is compatible for your team. In this webinar, DataScience.com Co-founder & COO, Jonathan Beckhardt, DataScience.com Lead Data Scientist, Andrea Trevino, and Linda Burtch, Founder & Managing Director at Burtch Works, cover the data science recruiting process end-to-end, including how to: - Assess your...
It; was only today
I noticed the way
I'm denying the signs
You've been giving
Took somebody to say
They noticed the change
You're what my life
Has been missing
And I won't let go
'Cos tonight I will give everything
Give me the right
To make love to you tonight
And I'll let tomorrow morning
Take it's time
'Cos love ain't gonna
Let us wait tonight, no no
Let's not talk anymore
No words can explain
The way that we
Both know we're feeling
So what more can I say
I want you to stay
Your love is all
I believe in
And we won't say no
To a night that will bring everything
Give me the right
To make love to you tonight
And I'll let tomorrow morning
Take it's time
'Cos love ain't gonna
Let us wait tonight, no no
Oh now we're alone
And love's found a home
We both need tonight
So there's no more to say
At the end of the day
Just come to me baby
You're what my heart
Has been missing
Give me the right
To make love to you tonight
And I'll let tomorrow morning
Take it's time
'Cos love ain't gonna
Let us wait tonight, no no